Forem: Fady Nabil

AWS DevOps Agent - Your New AI Teammate for DevOps

Fady Nabil — Sat, 11 Apr 2026 23:20:30 +0000

Meet Your New AI Teammate: A Deep Dive into the AWS DevOps Agent
The world of cloud operations is shifting from "automated" to "autonomous." At the end of 2025, AWS officially signaled this shift by introducing the AWS DevOps Agent. Described as a "frontier agent" for operational excellence, this tool isn't just another monitoring dashboard—it is an AI-driven collaborator designed to investigate, diagnose, and resolve infrastructure issues alongside human engineers.
Here is everything you need to know about the AWS DevOps Agent, from its core architecture to how it changes the daily life of a DevOps engineer.

AWS DevOps Agent
Drive operational excellence with a frontier agent that resolves and proactively prevents incidents.

Why AWS DevOps Agent?
AWS DevOps Agent is a frontier agent that resolves and proactively prevents incidents, continuously improving reliability and performance. DevOps Agent investigates incidents & identifies operational improvements as an experienced DevOps engineer would: by learning your resources & their relationships, working with your observability tools, runbooks, code repositories, and CI/CD pipelines, & correlating telemetry, code, and deployment data across all of them to understand the relationships between your application resources, including applications in multicloud and hybrid environments. AWS DevOps Agent uses this deep understanding of your operations and workloads to reduce MTTR (mean time to resolution) & drive operational excellence.

Benefits
Resolve issues quickly when they arise
(AWS DevOps Agent) is your always-on, autonomous on-call engineer. It begins investigating the moment an alert comes in, whether at 2 AM or during peak hours, to quickly restore your application to optimal performance. DevOps Agent autonomously triages incidents 24/7, providing root cause analysis & actions for resolution. It uses its understanding of your application resources & relationships to quickly understand dependencies & interactions. DevOps Agent streamlines incident response by automatically routing observations, findings, and mitigation steps through your preferred communication channels such as Slack, ServiceNow, & PagerDuty.
Proactively prevent future incidents
(AWS DevOps Agent) analyzes patterns across historical incidents to provide actionable recommendations that strengthen four key areas: observability, infrastructure optimization, deployment pipeline enhancement, & application resilience. For example, in the area of infrastructure optimization, if you experience unexpected traffic spikes, DevOps Agent may recommend the Kubernetes Horizontal Pod Autoscaler (HPA) for EKS clusters to better distribute traffic.
Access untapped insights in your operations and workloads
(AWS DevOps Agent) enables you to access the untapped insights in your operational data by securely integrating with your workflows and observability tools, runbooks, code repositories, and CI/CD pipelines. AWS DevOps Agent offers built-in integrations with observability tools such as Amazon CloudWatch, Dynatrace, Datadog, New Relic, and Splunk, and code repositories and CI/CD pipelines like GitHub and GitLab. You can extend AWS DevOps Agent beyond its built-in integrations by connecting to your own MCP server, enabling integrations with additional tools such as your organization’s custom tools, specialized platforms, or proprietary ticketing systems.

What is the AWS DevOps Agent?
The AWS DevOps Agent is a generative AI-powered service that automates the "undifferentiated heavy lifting" of cloud operations. While traditional tools tell you that something is broken, the DevOps Agent focuses on why it broke and how to fix it.
Built on top of Amazon Bedrock, the agent uses advanced foundation models to reason through complex system behaviors. It integrates directly with your AWS environment to monitor health, perform root cause analysis (RCA), and even execute remediation steps.

Key Capabilities: Beyond Simple Alerts
According to the official documentation and early previews, the agent excels in three primary areas:
1. Autonomous Root Cause Analysis (RCA)
When a CloudWatch alarm triggers or an EKS (Amazon Elastic Kubernetes Service) pod starts crashing, the agent doesn’t wait for a human to start grepping logs. It immediately begins an investigation, pulling relevant logs, metrics, and traces to build a timeline of the failure.
2. Intelligent Remediation
Once the agent identifies the root cause (e.g., a misconfigured IAM policy or a memory leak in a container), it doesn't just provide a generic suggestion. It generates specific, context-aware code snippets or CLI commands. In many cases, it can provide a "one-click" fix to resolve the issue.
3. Proactive Operational Health
The agent constantly "observes" the environment. By analyzing patterns across services, it can identify potential bottlenecks or configuration drifts before they lead to a full-scale outage, fulfilling the promise of "Operational Excellence."

Always-on, autonomous incident response
AWS DevOps Agent autonomously investigates issues the moment they occur:
Automated incident investigation: Begins investigating immediately when an alert or support ticket comes in
Interactive investigation chat: Initiate and guide investigations using natural language in the Dev Op Agent Space web app
Detailed mitigation plans: Provides specific actions to resolve incidents, validate success, and revert changes if needed
Automated incident coordination: Routes observations, findings, and mitigation steps through your preferred communication channels like Slack & ServiceNow
AWS Support integration: Create AWS Support cases directly from an investigation with immediate context provided to AWS Support experts

Prevent future incidents
DevOps Agent analyzes patterns across historical incidents to help you move from reactive firefighting to proactive operational improvement:

Targeted recommendations: Delivers specific, actionable improvements that strengthen four key areas: observability (monitoring, alerting, logging), infrastructure optimization (autoscaling, capacity tuning), and deployment pipeline enhancement (testing, validation).
Continuous learning: Refines recommendations based on your team's feedback

How It Works: The Architecture
The AWS DevOps Agent operates as a bridge between your observability data and your infrastructure management tools.

Data Ingestion: The agent taps into Amazon CloudWatch (logs and metrics), AWS X-Ray (traces), and AWS CloudTrail (user activity).
The Reasoning Engine: Using Amazon Bedrock, the agent processes this data. It understands the relationships between AWS resources (e.g., how a Lambda function interacts with a DynamoDB table).
Action Framework: The agent uses AWS Systems Manager (SSM) and IAM roles to safely execute commands within your environment. The Human-in-the-Loop Model Security is a primary concern with autonomous agents. AWS has built the DevOps Agent with a "Human-in-the-loop" philosophy. While the agent can operate autonomously in "read-only" mode to provide insights, any destructive or configuration-changing actions typically require manual approval from an administrator.

Why This Matters for DevOps Teams
The primary metrics in DevOps are MTTD (Mean Time to Detect) and MTTR (Mean Time to Resolve).
Current industry standards often involve "alert fatigue," where engineers are buried under a mountain of notifications. The AWS DevOps Agent filters the noise. Instead of an engineer spending two hours digging through EKS logs to find a failing node, the agent presents the findings in seconds: "I found that Pod X is failing because it exceeded its memory limit; here is the PR to update the resource constraints."
Key Benefits:
Reduced Burnout: Eliminates the "toil" of manual log analysis.
Faster Recovery: Drastically slashes MTTR by providing instant context.
Knowledge Transfer: Provides clear explanations of issues, helping junior engineers learn from the agent's reasoning.

Getting Started (Preview Phase)
As the service is currently in Preview, users can enable it through the AWS Management Console under the DevOps Agent service page.
Onboarding: You grant the agent permissions to access specific CloudWatch log groups and resources.
Configuration: You define the "scope" of the agent—telling it which applications or VPCs it should prioritize.
Interaction: You can interact with the agent via the AWS Console or through integrated chat environments, asking questions like, "Why did my production deployment fail ten minutes ago?"

The Future of Autonomous Operations
The AWS DevOps Agent represents the "Frontier" of cloud management. As it moves from preview to general availability, we can expect deeper integrations with Infrastructure as Code (Terraform/CDK) and even more sophisticated predictive capabilities.
For organizations looking to scale without linearly increasing their headcount, the AWS DevOps Agent isn't just a luxury; it’s becoming a necessity for maintaining operational excellence in an increasingly complex cloud landscape.

Architecture

How to maximize Agent's Effectiveness
While the topology provides important context during investigations, AWS DevOps Agent is not limited to investigating only the resources shown in the topology.
The agent may use additional data sources, such as AWS service APIs or connected observability tools, to investigate resources that are not in the application topology.
And that is why AWS has given option to add capabilities to maximize Agent's effectiveness by :

Connect multiple AWS accounts
Connect CI/CD pipelines through repo like Github/GitLab
MCP servers
Telemetry sources like Datadog, New Relic
Ticketing and chat like serviceNow and slack
Even EKS (demo over here)

Note: We can also provide runbooks as pre-loaded guidance/hints to enhance investigation performance to provide investigation hints and guidance.
You can configure runbooks to guide the AWS DevOps Agent as it performs incident response investigations and incident prevention evaluations. Click on the settings icon in the top right of your DevOps Agent web app and enter one or more runbooks.

Demo

AgentSpaces
Topology Sources
Capablities and Multi AWS Account
DevOps Center
Incident Respons
Prevention
Investigation timline
Using Chat
Mitigation plan

Hands-On Examples & Resources
· Terraform example: The aws-samples/sample-aws-devops-agent-terraform repo shows how to provision Agent Spaces and IAM roles via Infrastructure as Code. [github.com]
· EKS Workshop: The sample-devops-agent-eks-workshop repository includes demos (e.g., CloudWatch alerts, EKS failures) that illustrate real-world investigation flows. [github.com]

For more technical deep dives and setup guides, refer to the AWS DevOps Agent User Guide.

References:

AI SalesGrid: Transforming Enterprise Sales with AWS Multi-Agent Intelligence

Fady Nabil — Sat, 14 Mar 2026 23:51:25 +0000

The Future of Solution Design: Building an AI-Powered Digital Sales Organization - Close Faster, Stress Less - Solved by AI SalesGrid Your New Sales Superpower

Designing AI Solution
Now you're ready to create your own AI agents. But before diving into development, it's essential to establish a clear vision for your agent.
Ask yourself these key questions:
- What problem will it solve? Will it automate tasks, provide information, offer entertainment, or facilitate creative exploration?
- What solution with AI will it be Valid?
- What are its primary functions? Will it execute tasks or delegate tasks?
- What are its limitations? Will it be able to do everything autonomously?
- What personality or persona should it have? Will it be formal, informal, humorous, helpful, or informative?
- What are the success metrics? How will you measure the agent's effectiveness?

To speed up the process, here are the answers to those questions for the travel agent you will be creating today (In my USE Case and Our Solution):

What problem will it solve? Sales teams operate in one of the most complex environments in the organization. They must understand customer requirements, match them with available solutions, comply with company policies, prepare accurate proposals, and ensure profitability - all while moving quickly to win deals.
What are its primary functions? The agent should be able to multi-agent system where: Sales talks naturally AI validates offers instantly AI builds proposals AI checks rules automatically AI generates executive reports Everything is real-time Add Leads to CRM
What are its limitations? The agent might not be able to answer complicated queries by default
What personality or persona should it have? This agent should be knowledgeable, helpful, and enthusiastic about sales for every industry. It should be able to communicate information clearly and concisely.
What are the success metrics? Success for this agent could be measured by how satisfied users are with its recommendations (exploring, pricing, dealing)

AI SalesGrid - Your AI Superpower in Sales for Enterprise

In modern enterprises, sales teams operate in one of the most complex environments in the organization. They must understand customer requirements, match them with available solutions, comply with company policies, prepare accurate proposals, and ensure profitability - all while moving quickly to win deals.
However, this process is rarely smooth.
Sales teams often rely on fragmented tools, manual approvals, and disconnected knowledge sources. This results in pricing mistakes, compliance risks, inaccurate proposals, and delayed responses to customers.
AI SalesGrid addresses this challenge by introducing a new paradigm: a real-time AI-powered multi-agent sales system that acts as an intelligent digital sales organization.
Powered by AWS, Amazon Nova, and AWS Bedrock (Agent - Knowledgebase - Flow), AWS API Gateway, Lambda, S3, RDS, Dynamodb, OpenSearch, Redshift AI SalesGrid enables enterprises to transform their sales workflow into an intelligent, automated, and policy-aware system that operates in real time.
Instead of manually coordinating across departments, sales teams interact naturally with an AI system that orchestrates specialized agents responsible for solution design, pricing validation, proposal generation, risk analysis, reporting, and summarization.
The result is a faster, smarter, and more compliant sales process.

The Problem in Enterprise Sales
Large organizations face several critical challenges during the sales lifecycle:
1. Knowledge Fragmentation
Sales representatives often struggle to access the most up-to-date company rules, product documentation, pricing policies, and compliance guidelines.
2. Pricing and Discount Violations
Discounts and pricing structures are often approved manually, which can lead to:
Unauthorized discounts
Reduced margins
Profit leakage
3. Proposal Inconsistency
Proposals generated manually may include incorrect information, unrealistic timelines, or unsupported services.
4. Lack of Real-Time Decision Support
Sales teams frequently need input from solution architects, finance teams, and legal departments before making commitments to clients.
5. Limited Executive Visibility
Executives often lack immediate insight into deal risk, profitability, and pipeline health.
These challenges slow down deal cycles and introduce significant operational risk.
AI SalesGrid solves these issues by acting as an AI-powered digital sales organization.

The AI SalesGrid
AI SalesGrid introduces a multi-agent AI architecture where specialized AI agents collaborate under a central orchestrator.
Each agent performs a specific task, similar to roles within a real enterprise sales organization.
At the center of this architecture is the Account Manager Agent, which functions as the orchestrator of the entire system.
Sales representatives interact with the system through natural voice conversations, powered by the AWS Bedrock with Amazon Nova LLM, enabling real-time dialogue and interruption handling.
Behind the scenes, the orchestrator coordinates specialized agents to analyze requirements, validate pricing, generate proposals, assess risk, produce executive reports, and summarize results.

Use AI SalesGrid on Amazon Nova: https://nova.amazon.com/chat?model=AI+SalesGrid

Benefits of AI SalesGrid
AI SalesGrid delivers several benefits to enterprise organizations:
Faster Sales Cycles
Real-time AI assistance reduces delays caused by manual approvals and research.
Improved Compliance
All offers and proposals follow company policies automatically.
Higher Profitability
Pricing validation prevents margin erosion.
Better Decision-Making
Executives gain immediate insights into deal health and risk.
Consistent Customer Experience
Proposals and communications follow standardized formats and policies.

Multi-Agent Architecture
AI SalesGrid is built using a hierarchical multi-agent model.

Head Agent: Account Manager AI
The Account Manager Agent acts as the central coordinator of the system.

Solution Agent
The Solution Architect Agent analyzes client needs and identifies appropriate services offered by the company.
Using a grounded knowledge base stored in AWS Bedrock Knowledge base with AWS OpenSearch Verctor store, this agent ensures that proposed solutions align with existing offerings.

Pricing & Offer Validation Agent
Pricing mistakes can significantly impact company profitability.
The Pricing Agent ensures that all offers comply with company rules stored in the knowledge base.

Proposal Generation Agent
Once pricing and solutions are validated, the Proposal Agent generates a structured proposal for the client.
This agent ensures that proposals follow the company's official templates and policies.

Risk Assessment Agent
The Risk Agent evaluates the deal from multiple perspectives.
A risk score is generated to help decision-makers evaluate the opportunity before final approval.
Executive Reporting Agent
Enterprise leaders require high-level insights into deal health.
These insights are stored in analytics systems and can power executive dashboards.

Summarization Agent
To simplify communication and record keeping, the Summarization Agent produces concise summaries of:
Sales conversations
Deal structure
Proposal details
These summaries can be used for CRM updates, emails, or meeting notes.

Real-Time Interaction with AWS API Gateway
A key differentiator of AI SalesGrid is its real-time conversational interface.
Using the AWS API Gateway with Amazon Nova LLM on AWS Bedrock, sales representatives can speak naturally with the system.

Knowledge Grounding with AWS Bedrock Knowledge base
A critical aspect of the system is ensuring that AI responses are accurate and aligned with company policies.
AI SalesGrid uses AWS Bedrock Knowledge base to create a knowledge base.
These documents are stored in formats such as PDF and DOCX.
When agents generate responses, they use grounding, meaning responses are based on verified company knowledge rather than model assumptions.
This greatly reduces hallucination risks and ensures compliance.

Deep Technical Architecture
AI SalesGrid is built entirely on AWS, leveraging several managed services.

Repo for solution: https://github.com/fadynabil10/AI-SalesGrid-on-AWS

AWS Bedrock
AWS Bedrock is responsible for orchestrating the multi-agent architecture.
It enables:
Agent creation
Agent orchestration
Tool integration
Knowledge grounding
Workflow management

The Account Manager Agent acts as the orchestrator, delegating tasks to specialized agents.

AWS API Gateway
The AWS API Gateway enables low-latency streaming interaction.
Features include:
WebSocket-based communication
Real-time processing
Bidirectional streaming
Interruptible conversations

This allows the system to function as a natural voice assistant for enterprise sales teams.

AWS Bedrock Knowledge Base
This component provides knowledge retrieval and grounding.
Documents such as policies and catalogs are indexed and made searchable by agents.
Agents retrieve relevant sections to ensure responses are based on authoritative information.

ECS & Lambda
Backend services are deployed using ECS & Lambda.
ECS & Lambda manages:
API endpoints
Agent integration services
Real-time streaming gateway

Its serverless architecture ensures scalability while minimizing operational overhead.

Dynamodb & RDS
Dynamodb & RDS are used to store transactional data including:
Deal records
Client requirements
Proposal drafts
Approval states

This provides a structured database for ongoing sales activities.

Redshift
Redshift powers analytics and executive reporting.
Deal data stored in Dynamodb & RDS can be synchronized with Redshift to generate dashboards for leadership teams.

These dashboards provide insights into:
Sales performance
Profitability
Deal risk
Pipeline trends

Solution Used Tech stack:-

- Amazon Nova Act

Amazon Nova Act is an AWS service that helps you to build and manage AI agents tasked with automating UI workflows at massive scale for repetitive tasks. Build fleets of reliable agents to automate production UI workflows at scale.

- Streamlit (Python)

A Python-based web application that serves as the user interface (UI). It captures user input (prompts) and displays responses.
Streamlit is often used as the frontend interface in an architecture where the backend interacts with the Amazon Bedrock Agent via AWS API Gateway and AWS Lambda. The "feature" for Streamlit in this context is the ability to easily build an interactive, Python-based user interface to send user queries to, and display responses from, the Bedrock agent's API endpoint.
Building a web application that uses a Streamlit frontend with an AWS Bedrock agent and an AWS API Gateway involves a common architectural pattern for deploying generative AI solutions. This approach is used in many AWS GenAI projects and allows for separating the user interface from the backend logic and security.

- AWS Bedrock Flow
AWS Bedrock Flow is a AWS platform designed to build and orchestrate multi-agent systems for enterprise use. It integrates with existing processes and technology stacks, regardless of where you are in your AI adoption journey.
The platform reduces infrastructure complexity while allowing flexibility in agent development.
AWS Bedrock Flow equips teams with tools to create agents that perceive their environment, reason about tasks, and operate autonomously. Its capabilities include:

Multi-agent orchestration: Apply deterministic guardrails and workflow controls.
Context retention: Maintain short-term and long-term memory.
Model Context Protocol (MCP): Connect to diverse enterprise data sources.

Agent Design & Multi-agent Orchestration
AWS Bedrock enables multi-agent systems. It offers deterministic guardrails and orchestration controls for precise behavior.
You can orchestrate workflows combining specialized agents for tasks such as document processing, approval routing, and data validation while maintaining compliance.

Data Grounding & Knowledge Integration
AWS Bedrock Knowledge Base supports retrieval-augmented generation (RAG) for intelligent data access. AWS Bedrock Knowledge Base offers ready-to-use RAG. Vector Search supports hybrid searches for precision.
Custom RAG engines connect to sources like:

S3, RDS, Cloud Storage, Google Drive
Slack, Jira, other enterprise systems

Model Context Protocol (MCP) extends data access. Over 100 pre-built connectors cover ERP, HR, procurement systems, and more. AWS API Gateway integration enables secure API reuse.

Security, Compliance & Guardrails
AWS Bedrock uses AWS security. Agents run in IAM-controlled environments. VPC Service Controls limit network access. Audit logs track all interactions.

Content filters and deterministic guardrails allow precise behavior control. Bedrock meets SOC 2, ISO 27001, and HIPAA-eligible standards.

AI SalesGrid: Transforming Enterprise Sales with Google Cloud Multi-Agent Intelligence

Fady Nabil — Sun, 08 Mar 2026 16:34:52 +0000

The Future of Solution Design: Building an AI-Powered Digital Sales Organization - Close Faster, Stress Less - Solved by AI SalesGrid Your New Sales Superpower

To speed up the process, here are the answers to those questions for the travel agent you will be creating today (In my USE Case and Our Solution):

What problem will it solve? Sales teams operate in one of the most complex environments in the organization. They must understand customer requirements, match them with available solutions, comply with company policies, prepare accurate proposals, and ensure profitability - all while moving quickly to win deals.
What are its primary functions? The agent should be able to multi-agent system where: Sales talks naturally AI validates offers instantly AI builds proposals AI checks rules automatically AI generates executive reports Everything is real-time Add Leads to CRM
What are its limitations? The agent might not be able to answer complicated queries by default
What personality or persona should it have? This agent should be knowledgeable, helpful, and enthusiastic about sales for every industry. It should be able to communicate information clearly and concisely.
What are the success metrics? Success for this agent could be measured by how satisfied users are with its recommendations (exploring, pricing, dealing)

AI SalesGrid - Your AI Superpower in Sales for Enterprise

In modern enterprises, sales teams operate in one of the most complex environments in the organization. They must understand customer requirements, match them with available solutions, comply with company policies, prepare accurate proposals, and ensure profitability - all while moving quickly to win deals.
However, this process is rarely smooth.
Sales teams often rely on fragmented tools, manual approvals, and disconnected knowledge sources. This results in pricing mistakes, compliance risks, inaccurate proposals, and delayed responses to customers.
AI SalesGrid addresses this challenge by introducing a new paradigm: a real-time AI-powered multi-agent sales system that acts as an intelligent digital sales organization.
Powered by Google Cloud, Gemini Live API, and Vertex AI Agent Builder, Cloud Run, Bigquery, Cloud SQL AI SalesGrid enables enterprises to transform their sales workflow into an intelligent, automated, and policy-aware system that operates in real time.
Instead of manually coordinating across departments, sales teams interact naturally with an AI system that orchestrates specialized agents responsible for solution design, pricing validation, proposal generation, risk analysis, reporting, and summarization.
The result is a faster, smarter, and more compliant sales process.

The AI SalesGrid
AI SalesGrid introduces a multi-agent AI architecture where specialized AI agents collaborate under a central orchestrator.
Each agent performs a specific task, similar to roles within a real enterprise sales organization.
At the center of this architecture is the Account Manager Agent, which functions as the orchestrator of the entire system.
Sales representatives interact with the system through natural voice conversations, powered by the Gemini Live API, enabling real-time dialogue and interruption handling.
Behind the scenes, the orchestrator coordinates specialized agents to analyze requirements, validate pricing, generate proposals, assess risk, produce executive reports, and summarize results.

Multi-Agent Architecture
AI SalesGrid is built using a hierarchical multi-agent model.

Head Agent: Account Manager AI
The Account Manager Agent acts as the central coordinator of the system.
Solution Agent
The Solution Architect Agent analyzes client needs and identifies appropriate services offered by the company.
Using a grounded knowledge base stored in Vertex AI Search, this agent ensures that proposed solutions align with existing offerings.
Pricing & Offer Validation Agent
Pricing mistakes can significantly impact company profitability.
The Pricing Agent ensures that all offers comply with company rules stored in the knowledge base.
Proposal Generation Agent
Once pricing and solutions are validated, the Proposal Agent generates a structured proposal for the client.
This agent ensures that proposals follow the company's official templates and policies.
Risk Assessment Agent
The Risk Agent evaluates the deal from multiple perspectives.
A risk score is generated to help decision-makers evaluate the opportunity before final approval.
Executive Reporting Agent
Enterprise leaders require high-level insights into deal health.
These insights are stored in analytics systems and can power executive dashboards.
Summarization Agent
To simplify communication and record keeping, the Summarization Agent produces concise summaries of:
Sales conversations
Deal structure
Proposal details
These summaries can be used for CRM updates, emails, or meeting notes.
Real-Time Interaction with Gemini Live API
A key differentiator of AI SalesGrid is its real-time conversational interface.
Using the Gemini Live API, sales representatives can speak naturally with the system.
Knowledge Grounding with Vertex AI Search
A critical aspect of the system is ensuring that AI responses are accurate and aligned with company policies.
AI SalesGrid uses Vertex AI Search and Conversation to create a knowledge base.
These documents are stored in formats such as PDF and DOCX.
When agents generate responses, they use grounding, meaning responses are based on verified company knowledge rather than model assumptions.
This greatly reduces hallucination risks and ensures compliance.

Deep Technical Architecture
AI SalesGrid is built entirely on Google Cloud Platform, leveraging several managed services.

Repo for solution: https://github.com/fadynabil10/AI-SalesGrid

Vertex AI Agent Builder
Vertex AI Agent Builder is responsible for orchestrating the multi-agent architecture.
It enables:
Agent creation
Agent orchestration
Tool integration
Knowledge grounding
Workflow management

The Account Manager Agent acts as the orchestrator, delegating tasks to specialized agents.

Gemini Live API
The Gemini Live API enables low-latency streaming interaction.
Features include:
WebSocket-based communication
Real-time audio processing
Bidirectional streaming
Interruptible conversations

This allows the system to function as a natural voice assistant for enterprise sales teams.

Vertex AI Search & Conversation
This component provides knowledge retrieval and grounding.
Documents such as policies and catalogs are indexed and made searchable by agents.
Agents retrieve relevant sections to ensure responses are based on authoritative information.

Cloud Run
Backend services are deployed using Cloud Run.
Cloud Run manages:
API endpoints
Agent integration services
Real-time audio streaming gateway

Its serverless architecture ensures scalability while minimizing operational overhead.

Firestore
Firestore is used to store transactional data including:
Deal records
Client requirements
Proposal drafts
Approval states

This provides a structured database for ongoing sales activities.

BigQuery
BigQuery powers analytics and executive reporting.
Deal data stored in Firestore can be synchronized with BigQuery to generate dashboards for leadership teams.

These dashboards provide insights into:
Sales performance
Profitability
Deal risk
Pipeline trends

Solution Used Tech stack:-

- Agent Development Kit
Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.
Reference:

- Vertex AI Agent Builder

Vertex AI Agent Builder is a Google Cloud platform designed to build and orchestrate multi-agent systems for enterprise use. It integrates with existing processes and technology stacks, regardless of where you are in your AI adoption journey.
The platform reduces infrastructure complexity while allowing flexibility in agent development. You can build agents using Google's Agent Development Kit (ADK), leverage open-source frameworks such as LangChain or LangGraph, or connect agents created with other tools.
Vertex AI Agent Builder equips teams with tools to create agents that perceive their environment, reason about tasks, and operate autonomously. Its capabilities include:

Agent Development Kit (ADK): Build agents in under 100 lines of Python code.
Multi-agent orchestration: Apply deterministic guardrails and workflow controls.
Agent2Agent (A2A) protocol: Connect agents across frameworks and vendors.
Enterprise integration: Access systems and data with 100+ pre-built connectors.
Agent Engine: Deploy and scale agents in a managed runtime.
Audio/video streaming: Enable human-like conversations.
Context retention: Maintain short-term and long-term memory.
Model Context Protocol (MCP): Connect to diverse enterprise data sources.

Vertex AI Agent Builder is designed for you if you need scalable, auditable AI systems integrated into your workflows.

Workflow Diagram: Vertex AI Agent Builder

How It Differs From Other Agent Builders?
Vertex AI Agent Builder offers flexible development and strong enterprise integration. Unlike platforms locked to a single framework, Vertex supports multiple approaches.
With the Agent Development Kit (ADK), you can build production-ready agents with minimal code, control reasoning and interaction, and use bidirectional audio/video streaming for natural conversations.
You can also develop agents with LangChain, LangGraph, AG2, or Crew.ai and deploy them on Vertex AI without rewriting code, leveraging existing expertise and Google's managed infrastructure.
The Agent2Agent (A2A) protocol enables agents across different frameworks and vendors to communicate, supported by 50+ partners including Box, Deloitte, Elastic, Salesforce, ServiceNow, and UiPath.

Core Features & Architecture
Vertex AI Agent Builder separates agent development, communication, data access, and operations. This lets you build both single-agent applications and complex multi-agent systems without mixing concerns.

Agent Design & Multi-agent Orchestration
The Agent Development Kit (ADK) enables multi-agent systems with under 100 lines of Python. It offers deterministic guardrails and orchestration controls for precise behavior. Agent Garden provides reusable patterns and components to speed development.
You can orchestrate workflows combining specialized agents for tasks such as document processing, approval routing, and data validation while maintaining compliance. ADK manages short-term and long-term memory so agents retain context over interactions.

Communication Between Agents
The Agent2Agent (A2A) protocol enables communication across frameworks. It lets agents:

Publish capabilities for discovery.
Negotiate formats such as text or bidirectional audio/video.
Maintain context across systems.
Work securely under enterprise governance.

A2A removes integration barriers and fosters collaboration across teams without rebuilding systems. Over 50 partners contribute to the growing A2A ecosystem, avoiding vendor lock-in.

Data Grounding & Knowledge Integration
Vertex AI supports retrieval-augmented generation (RAG) for intelligent data access. Vertex AI Search offers ready-to-use RAG. Vector Search supports hybrid searches for precision.
Custom RAG engines connect to sources like:

Local files, Cloud Storage, Google Drive
Slack, Jira, other enterprise systems

Model Context Protocol (MCP) extends data access. Over 100 pre-built connectors cover ERP, HR, procurement systems, and more. Apigee integration enables secure API reuse.

Security, Compliance & Guardrails
Vertex AI Agent Builder uses Google Cloud's security framework. Agents run in IAM-controlled environments. VPC Service Controls limit network access. Audit logs track all interactions.

Content filters and deterministic guardrails allow precise behavior control. Vertex meets SOC 2, ISO 27001, and HIPAA-eligible standards.

Unlocking the Potential of Amazon Nova: Capabilities, Performance, Use Cases, FM, Model Insights and Deployment

Fady Nabil — Sun, 08 Jun 2025 10:38:19 +0000

Amazon Web Services ((AWS)) has launched its most powerful foundation model to date — Amazon Nova.
This cutting-edge multimodal model promises to revolutionize how developers & businesses leverage AI for complex tasks & agentic workflows.

Amazon Nova models
Amazon Nova is a new generation of foundation model ((FM)) offering frontier intelligence & industry-leading price-performance. They offer fast inference, support agentic workflows with Amazon Bedrock Knowledge Bases & RAG, and allow fine-tuning for text and multi-modal data. Optimized for cost-effective performance, they are trained on data in over 200 languages.

Amazon Nova’s Model Family
Before diving into Nova Premier’s capabilities, it’s important to understand how it fits within AWS’s broader Nova model family:

Amazon Nova Micro: A text-only model delivering the lowest latency responses at very low cost
Amazon Nova Lite: A low-cost multimodal model optimized for quickly processing image, video, and text inputs
Amazon Nova Pro: A balanced multimodal model offering the best combination of accuracy, speed, and cost for general use cases
Amazon Nova Premier: The most capable model designed specifically for complex tasks & teacher model distillation

Check out here to explore and know more:
https://nova.amazon.com/

The Power of Model Distillation
Perhaps one of the most exciting aspects of Nova Premier is its role as a teacher model for distillation. This process allows organizations to:

Leverage Nova Premier’s broad intelligence to create specialized models
Use Nova Premier invocation logs as training data for smaller models like Nova Micro
Create student models that match Nova Premier’s accuracy for specific use cases while maintaining lower costs & latency

Result? Complex tasks that might take Nova Premier almost a minute can be completed twice as fast after distillation, making sophisticated AI capabilities accessible to everyday users at scale.

Technical Capabilities and Benchmarks
Amazon Nova understanding models, including Premier, offer impressive technical capabilities:

Support for over 200 languages
Text & vision fine-tuning
State-of-the-art performance on benchmarks like Berkeley Function Calling Leaderboard (BFCL), VisualWebBench, and Mind2Web
Excellent in-context learning (ICL) and retrieval augmented generation (RAG) performance
Seamless integration with Amazon Bedrock features like Knowledge Bases and Agents

Generated using Amazon Nova Canvas “shapes flowing in and out of a funnel”.

Amazon Nova understanding models
Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro, and Amazon Nova Premier are understanding models that accept text, image, and video inputs and generate text output. They provide a broad selection of capability, accuracy, speed, and cost operation points.

Fast & cost-effective inference across intelligence classes
State-of-the-art text, image, and video understanding
Fine-tuning on text, image, and video input
Leading agentic & multimodal Retrieval Augmented Generation (RAG) capabilities
Excels in coding & software development use cases

Generated using Amazon Nova Canvas “a hummingbird in a garden”.

Amazon Nova creative models
Amazon Nova Canvas & Amazon Nova Reel are creative content generation models that accept text and image inputs and produce image or video outputs. They are designed to deliver customizable high-quality images & videos for visual content generation.

Cost-effective image & video generation
Control over your visual content generation
Multiple approaches to customize & edit visual content
Support for safe & responsible use of AI with watermarking & content moderation

Generated using Amazon Nova Canvas “white background with a dark purple neural network in the center, voice signal as input on left & output on right”.

Amazon Nova speech-to-speech model
Amazon Nova Sonic is a speech-to-speech model that accepts speech as input & generates speech & text as output.

Model is designed to deliver real-time, human-like voice conversations with contextual richness.

State-of-the-art speech understanding and generation
Available through a bidirectional streaming API, enabling real-time, interactive communication
Supports function calling & knowledge grounding with enterprise data using RAG
Robust handling of user’s pauses, hesitations, and audio interruptions
Built-in controls for safe & responsible use of AI

Model versions

Amazon Nova Micro

A text-only model that delivers the lowest latency responses at very low cost. It is highly performant at language understanding, translation, reasoning, code completion, brainstorming, and mathematical problem-solving. With its generation speed of over 200 tokens per second, Amazon Nova Micro is ideal for applications that require fast responses.
Max tokens: 128k
Languages: 200+ languages
Fine-tuning supported: Yes, with text input

Amazon Nova Lite

Very low-cost multimodal model that is lightning fast for processing image, video, and text inputs. Accuracy of Amazon Nova Lite across a breadth of tasks, coupled with its lightning-fast speed, makes it suitable for a wide range of interactive & high-volume applications where cost is a key consideration.
Max tokens: 300k
Languages: 200+ languages
Fine-tuning supported: Yes, with text, image, and video input

Amazon Nova Pro

A highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Capabilities of Amazon Nova Pro, coupled with its industry-leading speed & cost efficiency, makes it a compelling model for almost any task, including video summarization, Q&A, mathematical reasoning, software development, and AI agents that can execute multistep workflows.
Max tokens: 300k
Languages: 200+ languages
Fine-tuning supported: Yes, with text, image, and video input

Amazon Nova Premier

Most capable model for complex tasks & teacher for model distillation. Customers can use Amazon Nova Premier with Amazon Bedrock Model Distillation to create highly-capable, cost-effective, and low-latency versions of Amazon Nova Pro, Lite, and Micro, for specific needs.
Max tokens: 1M
Languages: 200+ languages
Fine-tuning supported: No. Amazon Nova Premier can be a teacher for model distillation.

Amazon Nova Canvas

A cost-effective image generation model that creates professional-grade images from text or images provided in prompts. Amazon Nova Canvas also provides features that make it easy to edit images using text inputs, controls for adjusting color scheme and layout, and built-in controls to support safe and responsible use of AI.
Max input characters: 1,024
Languages: English
Fine-tuning supported: Yes

Amazon Nova Reel

A cost-effective video generation model that allows customers to easily create high quality video from text & images. Amazon Nova Reel supports use of natural language prompts to control visual style and pacing, including camera motion control, and built-in controls to support safe and responsible use of AI.
Max input characters: 512
Languages: English
Fine-tuning supported: Coming soon

Amazon Nova Sonic

A state-of-the-art speech understanding & generation model that delivers real-time, human-like voice-conversations with industry-leading price-performance. The model supports fluid dialogue and turn-taking, low latency multi-turn conversations, function calling, and knowledge grounding with enterprise data using RAG. Amazon Nova Sonic supports expressive voices, including both masculine-sounding & feminine-sounding voices.
Max tokens: 300k
Languages: English (including American & British accents). Additional languages coming soon.

Migrate from OpenAI to Amazon Nova … Why?
OpenAI’s models remain powerful, but their operational costs can be prohibitive when scaled. You can check analysis from Artificial Analysis:

For high-volume applications — like customer support or large document analysis — these cost differences are disruptive.
Not only does Nova Pro offer over three times cost-efficiency, its longer context window also enables it to handle more extensive & complex inputs.

Amazon Nova Use Cases and Real Testing:

You can also get started with this Nova Workshop codebase:
Nova Sample code

Output for Nova Pro vs Nova Micro in Amazon Bedrock Playground

Amazon Bedrock Playground to experience with Nova Reel foundation model:

Upload your image. This image will used by the model to generate video.

Amazon Nova Reel playground provides real-time progress updates as it generates requested video.

Once video clip is successfully generated, you’ll see an option to download it.

This video clip is also automatically stored in your S3 bucket. You can delete it from there so that you don’t incur ongoing cloud cost for this s3 bucket.

Fine-tune an Amazon Nova model:
In this we will make fine-tuning & hosting customized Amazon Nova models using Amazon Bedrock.
The following diagram illustrates solution architecture.

Create a fine-tuning job
Complete the following steps to create a fine-tuning job:

Open Amazon Bedrock console.
Choose us-east-1 as AWS Region.
Under Foundation models in navigation pane, choose Custom models.
Choose Create Fine-tuning job under Customization methods.

At the time of writing, Amazon Nova model fine-tuning is exclusively available in us-east-1 Region.

For Source model, choose Select model.
Choose Amazon as the provider & Amazon Nova model of your choice Lite or Micro.
Choose Apply.

For Fine-tuned model name, enter a unique name for the fine-tuned model.
For Job name, enter a name for fine-tuning job.
Under Input data, enter location of the source S3 bucket (training data) & target S3 bucket (model outputs & training metrics), and optionally the location of your validation dataset.

In the Hyperparameters section, you can customize the following hyperparameters:

For Epochs¸ enter a value between 1–5.
For Batch size, value is fixed at 1.
For Learning rate multiplier, enter a value between 0.000001–0.0001
For Learning rate warmup steps, enter a value between 0–100.

Recommend starting with the default parameter values and then changing settings iteratively. It’s a good practice to change only one or a couple of parameters at a time, in order to isolate the parameter effects. Remember, hyperparameter tuning is model & use case specific.

In Output data section, enter the target S3 bucket for model outputs & training metrics.
Choose Create fine-tuning job.

Run fine-tuning job
After you start fine-tuning job, you will be able to see your job under Jobs & status as Training.
When it finishes, the status changes to Complete.

You can now go to training job & optionally access the training-related artifacts that are saved in output folder.

You can find both training & validation (highly recommend using a validation set) artifacts here.

You can use training & validation artifacts to assess your fine-tuning job through loss curves

as shown in the following figure, which track training loss ((orange)) & validation loss ((blue)) over time.

Host fine-tuned model & run inference
Now that you have completed the fine-tuning, you can host the model & use it for inference. Follow these steps:

On Amazon Bedrock console.
Under Foundation models in navigation pane, choose Custom models.
On the Models tab, choose model you fine-tuned.

Choose Purchase provisioned throughput.

Specify a commitment term & review associated cost for hosting the fine-tuned models.

After customized model is hosted through provisioned throughput, a model ID will be assigned, which will be used for inference.

For inference with models hosted with provisioned throughput, we have to use Invoke API in the same way we described previously in this post — simply replace model ID with customized model ID.

Results
The results of base Amazon Nova models to their fine-tuned pro is best than lite & lite best than micro in accuracy & performance.
Multi-agent collaboration use case:
This use case on AWS Blogs. Nova Premier works a multi-agent collaboration architecture for investment research.

We can build application using multi-agent collaboration in Amazon Bedrock, with Nova Premier powering supervisor agent that orchestrates the entire workflow.

The supervisor agent analyzes initial query

(Example: “What are emerging trends in renewable energy investments?”), breaks it down into logical steps, determines which specialized subagents to engage, and synthesizes the final response.

Components:

A supervisor agent powered by Nova Premier
Multiple specialized subagents powered by Nova Pro, each focusing on different financial data sources
Tools that connect to financial databases, market analysis tools, other relevant information sources

Architect for Components:

The supervisor agent powered by Nova Premier does the following:

Analyzes & determine ( underlying topics & sources )
Selects appropriate subagents specific to those topics & sources
Each subagent retrieves their relevant data
Supervisor agent synthesizes this information into a comprehensive report.

Nova Premier in a multi-agent architecture such as this streamlines the financial professional’s work.

Some of Resources here from AWS Documentations and Blog.

All the latest feature releases, updates and announcements of AWS re:Invent 2024

Fady Nabil — Sat, 08 Feb 2025 15:02:49 +0000

SAP on AWS

Deploy GROW with SAP on AWS from AWS Marketplace GROW with SAP in AWS Marketplace to Help Customers Rapidly Adopt Cloud ERP and Innovate with Advanced Generative AI Solutions. https://news.sap.com/2024/12/grow-with-sap-on-aws-simplify-cloud-erp-deployment/

CDN

CloudFront VPC Origins Perhaps long overdue, VPC Origins is a new feature for CloudFront that allows you to keep your resources within private subnets by dropping an ENI in your subnet that it will use to access origins at no extra cost.

Compute

New EC2 P5en instances with NVIDIA H200 Tensor Core GPUs & EFAv3 networking Amazon EC2 P5en instances deliver up to 3,200 Gbps network bandwidth with EFAv3 for accelerating deep learning & gen AI.
EC2 Trn2 Instances & Trn2 UltraServers for AI/ML training & inference are available now With 4x faster speed, 4x more memory bandwidth, 3x higher memory capacity than predecessors, and 30% higher floating-point operations, compute power for ML training & gen AI.
EC2 I8g instances storage optimized powered by AWS Graviton4 processors & 3rd gen AWS Nitro SSDs
Storage performance with AWS’s newest I8g instances, which deliver unparalleled speed & efficiency for I/O-intensive.
Storage optimized Amazon EC2 I7ie instances
New AWS I7ie instances deliver unbeatable storage performance: up to 120TB NVMe, 40% better compute & up to 65% better real time storage performance.

Storage

New Amazon S3 Tables: Storage optimized for analytics workloads Amazon S3 Tables optimize tabular data storage in Apache Iceberg, enabling high-performance, low cost queries using Athena, EMR, & Spark.
New physical AWS Data Transfer Terminals let you upload to the cloud faster Rapidly upload large datasets to AWS with the new AWS Data Transfer Terminal, secure physical locations & offering high throughput connection.
Connect users to data through your apps with Storage Browser for Amazon S3 Storage Browser for Amazon S3 is an open source interface component that you can add to your web applications to provide your authorized end users, such as customers, partners, and employees, with access to easily browse, upload, download, copy, and delete data in S3.
Queryable object metadata for Amazon S3 buckets Unlock S3 data insights effortlessly with AWS’ rich metadata capture; query objects by key, size, and more using Athena, Redshift, & Spark when scale.

Database

Amazon MemoryDB Multi-Region is now generally available Build highly available, globally distributed apps with microsecond latencies across Regions, automatic conflict resolution, & up to 99.999% availability.
Amazon RDS Blue/Green Deployments support storage volume shrink RDS now supports live volume shrink using blue/green deployments. This allows for a volume reduction after space is cleared without incurring downtime.
Amazon Aurora Serverless v2 supports scaling to zero capacity Aurora Serverless v2 now supports scale to zero, with resume time typically being ~15 seconds. This provides a nice reduction in cost for low-usage databases.
Amazon DynamoDB global tables preview multi-Region strong consistency Strongly consistent multi-region DynamoDB is now an option within Global Tables.
Announcing Amazon Aurora DSQL Aurora DSQL is a new, highly scalable, and multi-region SQL database. I liken it to CosmosDB or Spanner.

Security

Securely share AWS resources across VPC and account boundaries Resource Gateways expose a single resource (IP, DNS, or ARN) to VPC Lattice and Step Functions, including TCP resources like databases, with gateways being free to create. This provides a new, cost-competitive alternative to VPCEs and TGWs.

Migration & Transfer Services

AWS Database Migration Service now automates time-intensive schema conversion tasks using generative AI AWS DMS Schema Conversion converts up to 90% of the schema to accelerate database migrations & reduce manual effort with power of GenAI.
AWS Transfer Family web apps for fully managed Amazon S3 file transfers AWS Transfer Family web apps are a new resource that you can use to create a simple interface for authorized line-of-business users to access data in S3 through a web browser.
Introducing default data integrity protections for new objects in Amazon S3 S3 the default behavior of object upload requests with new data integrity protections that build upon S3’s existing durability posture.

Containers

Use your on-premises infrastructure in Amazon EKS clusters with Amazon EKS Hybrid Nodes Kubernetes management across your cloud & on-premises environments with EKS Hybrid Nodes _ to use existing hardware while offloading control plane responsibilities to EKS for consistent operations.
Streamline Kubernetes cluster management with new Amazon EKS Auto Mode With EKS Auto Mode, AWS simplifies Kubernetes cluster management, automating (compute, storage, networking), enabling higher agility and reducing operational.

Analytics

Simplify analytics & AI/ML with new Amazon SageMaker Lakehouse. SageMaker Lakehouse seamlessly integrates S3 data lakes & Redshift warehouses, enabling unified analytics and AI/ML on a single data copy through open Apache Iceberg APIs & fine-grained access controls.
Solve complex problems with new scenario analysis capability in Amazon Q in QuickSight Find solutions to most critical business challenges with ease. Q in QuickSight enables business to analysis up to 10x faster than spreadsheets.
New Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse Analyze operational data in Amazon SageMaker Lakehouse, freeing developers from building custom pipelines & enabling seamless insights .

Generative AI / Machine Learning

Amazon Bedrock IDE enables teams to rapidly build & customize Accelerate your generative AI app development.
Amazon Nova foundation models https://aws.amazon.com/blogs/aws/introducing-amazon-nova-frontier-intelligence-and-industry-leading-price-performance/
Amazon Bedrock Marketplace New capability that gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. Discover, test, and use over 100 emerging, & specialized foundation models with the tooling, security, & governance.
Amazon Bedrock Prompt caching and intelligent prompt routing Two capabilities that help reduce costs & latency for generative AI applications. Route requests & cache frequently used context in prompts to reduce latency & balance performance with cost efficiency.
Multi-agent collaboration capability With multi-agent collaboration, you can build, deploy, and manage multiple AI agents working together on complex multi-step tasks.
Amazon Bedrock Guardrails now supports multimodal toxicity detection with image support Build responsible AI applications _ Safeguard them against harmful text & image content.
New Amazon Bedrock capabilities enhance data processing and retrieval Bedrock enhances generative AI data analysis with multimodal processing, graph modeling, & structured querying, accelerating AI app development.
RAG evaluation and LLM-as-a-judge capabilities in Amazon Bedrock Evaluate AI models & applications efficiently in Amazon Bedrock’s new LLM-as-a-judge capability for model evaluation & RAG evaluation for Knowledge Bases, offering a variety of quality & responsible AI metrics at scale.
Build faster, more cost-efficient, highly accurate models with Amazon Bedrock Model Distillation Automates the process of creating a distilled model for your specific use case by generating responses from a large foundation model & fine-tunes a smaller FM with the generated responses.
Automated reasoning checks https://lnkd.in/g8NmGk4B
Amazon Kendra GenAI Index to connect more than 40 enterprise data sources with Amazon Bedrock Knowledge Bases https://lnkd.in/gRfsAtqv
Amazon Bedrock Data Automation to extract information from documents, audio, images & videos and transform it into structured formats https://lnkd.in/gaKRiFaa
Amazon Q Developer to build ML models in SageMaker Canvas Q Developer empowers non-ML experts to build ML models using natural language. faster with reduced time to be in market.
Next generation of Amazon SageMaker: The center for all your data, analytics, & AI Unify data engineering, analytics, & generative AI in a streamlined studio with enhanced capabilities of SageMaker.
Maximize accelerator utilization for model development with new Amazon SageMaker HyperPod task governance Enable priority-based resource allocation, fair-share utilization, & automated task preemption for optimal compute utilization across teams.
Accelerate foundation model training & fine-tuning with new Amazon SageMaker HyperPod recipes Training & fine-tuning popular publicly available foundation models, in just minutes with state-of-the-art performance.
Discover, govern, collaborate on data & AI securely with Amazon SageMaker Data & AI Governance Manage data & AI assets through a unified catalog, granular access controls, & consistent policy enforcement.
Amazon Q Business is adding new workflow automation capability & 50+ action integrations Q Business extends productivity with GenAI powered workflow automation capability and 50+ actions for enterprise efficiency, enabling seamless task execution across tools like ServiceNow, Jira and Asana.
New capabilities from Amazon Q Business enable ISVs to enhance generative AI experiences Amazon Q Business capabilities help ISVs integrate with the Amazon Q index to retrieve data from multiple sources through a single API & customize the design of Q embedded assistant.
Introducing Amazon Nova foundation models: Frontier intelligence & industry leading price performance Amazon Nova foundation models deliver frontier intelligence & industry leading price-performance, with support for text & multimodal intelligence, multimodal fine-tuning, and HQ images and videos.
Prevent factual errors from LLM hallucinations with mathematically sound Automated Reasoning checks Enhance conversational AI accuracy with Automated Reasoning checks — first & only gen AI safeguard that helps reduce hallucinations.
Enhance your productivity with new extensions & integrations in Amazon Q Business Seamlessly access AI assistance within work applications with Q Business’s new browser extensions & integrations.
New APIs in Amazon Bedrock to enhance RAG applications With custom connectors & reranking models, you can enhance RAG applications by enabling direct ingestion to knowledge bases without requiring a full sync.
New PartyRock capabilities & free daily usage Unleash your creativity with PartyRock’s new AI capabilities: generate images, analyze visuals, search hundreds of thousands of apps, & process multiple docs simultaneously — no code.
Amazon Q Business adds support to extract insights from visual elements within documents Users can now query information embedded in various types of visuals, including diagrams, infographics, charts, & other image-based content.

Some of Resources here from AWS Documentations and Blog.

Amazon Bedrock Flows: Now Generally Available with Enhanced Safety and Traceability

Fady Nabil — Sat, 30 Nov 2024 10:13:50 +0000

AWS excited to announce the general availability of Amazon Bedrock Flows (previously known as Prompt Flows), a robust feature designed to streamline the creation, deployment, and scaling of generative AI applications on AWS. With an emphasis on enhanced safety and traceability, this solution caters to businesses seeking efficient and secure ways to leverage generative AI capabilities in production.

Bedrock Flows empowers developers and businesses to leverage generative AI effectively, facilitating the development of advanced and efficient AI-powered solutions for customers.

https://aws.amazon.com/bedrock/flows/

Simplifying Generative AI Application Development
Amazon Bedrock Flows is a significant step forward in simplifying how developers and organizations implement generative AI models. By abstracting the complexity of integrating foundational models, Bedrock Flows provides pre-configured workflows that reduce the need for deep technical expertise in AI, empowering teams to focus on innovation.

Enhanced Safety and Compliance
A critical component of generative AI is ensuring ethical and secure usage. Bedrock Flows incorporates advanced safety mechanisms to mitigate risks such as data leakage and unauthorized access. Traceability features enable organizations to track and audit the application of AI models, ensuring adherence to compliance requirements and ethical guidelines.

Core Features of Amazon Bedrock Flows

Pre-Built Workflows: Accelerate time-to-market with ready-to-use configurations tailored for various generative AI use cases.
Model Customization: Seamlessly integrate proprietary or third-party foundational models, allowing customization to meet specific business needs.
Scalability: Utilize the scalable architecture of AWS to handle fluctuating workloads with ease, ensuring performance stability.
Integrated Safety Controls: Leverage in-built tools for monitoring and mitigating risks, such as overfitting or misuse of AI capabilities.
Comprehensive Traceability: Enable detailed auditing of model behavior and interactions for improved transparency and governance.
Quickly build generative AI workflows visually.
Test and deploy faster with serverless infrastructure.

Key advantages include:

Streamlined creation of generative AI workflows using a user-friendly visual interface.
Easy incorporation of advanced foundation models (FMs), prompts, agents, knowledge bases, guardrails, and other AWS tools.
Customizable workflows tailored to align with specific business logic.
Minimization of time and effort required for testing and deploying AI workflows, thanks to SDK APIs and serverless infrastructure.

New Features in Amazon Bedrock Flows

Generative AI adoption requires robust safety measures and clear workflow visibility. Amazon Bedrock Flows introduces two new features to address these needs, empowering organizations to create secure and traceable AI applications:

1. Enhanced Safety

The addition of Amazon Bedrock Guardrails enables organizations to filter harmful content and exclude unwanted topics. Guardrails are available in:

Prompt Nodes: Enforce strict controls over interactions with foundation models.
Knowledge Base Nodes: Regulate responses derived from your knowledge base.

2. Enhanced Traceability

Organizations can now validate and debug workflows with comprehensive input/output tracking and inline validation, ensuring complete visibility into execution. Key enhancements include:

Detailed tracing for input and output nodes.
Execution path insights, including input, output, errors, and execution time for each node.
Inline validation status directly within the visual builder.

Practical Application: E-commerce company Challenges

Consider An e-commerce company implementing a customer service chatbot with Amazon Bedrock Flows. Their challenges include:

Chatbot responses occasionally reveal sensitive customer data.
Inconsistent tone and quality in customer interactions.
Troubleshooting workflow issues is time-consuming.
Difficulty ensuring compliance with company policies and regulations.
Limited visibility into performance bottlenecks that degrade customer experience.

With Amazon Bedrock Flows, E-commerce company can overcome these hurdles, creating a more secure, efficient, and policy-compliant chatbot.

Real-World Applications

Organizations across industries are leveraging Amazon Bedrock Flows to transform operations and unlock value through generative AI. From automating content creation to enhancing customer support with intelligent chatbots, the use cases are diverse and impactful. For instance, marketing teams can generate tailored ad copy, while R&D departments accelerate product innovation with automated ideation tools.

Getting Started with Amazon Bedrock Flows

Adopting Amazon Bedrock Flows is straightforward, with guided documentation and support available to assist at every stage. AWS customers can now harness this technology to build robust generative AI applications, ensuring both efficiency and ethical deployment.

Create a flow with a single prompt

The following image shows a flow consisting of a single prompt, defined inline in the node, that builds a playlist of songs, given a genre and the number of songs to include in the playlist.

To build and test this flow in the console

1- To create a flow in the Console tab at Create a flow in Amazon Bedrock. Enter the Flow builder.

2- Set up the prompt node:

a. From the Flow builder left pane, select the Nodes tab.
b. Drag a Prompt node into your flow in the center pane.
c. Select the Configure tab in the Flow builder pane.
d. Enter MakePlaylist as the Node name.
e. Choose Define in node.
f. Set up the following configurations for the prompt:
i. Under Select model, select a model to run inference on the prompt.
ii. In the Message text box, enter Make me a {{genre}} playlist consisting of the following number of songs: {{number}}.. This creates two variables that will appear as inputs into the node.

e. Expand the Inputs section. The names for the inputs are prefilled by the variables in the prompt message. Configure the inputs:

This configuration means that the prompt node expects a JSON object containing a field called genre that will be mapped to the genre input & a field called number that will be mapped to the number input.
h. You can’t modify the Output. Response will from the model, returned as a string.

3- Choose the Flow input node & select the Configure tab.
Select Object as the Type. Flow invocation will expect to receive a JSON object.

4- Connect your nodes to complete the flow:

a. Drag a connection from the output node of the Flow input node to the genre input in the MakePlaylist prompt node.
b. Repeat the same steps for Flow input node to the number input in the MakePlaylist prompt node.
c. Repeat the same steps for modelCompletion output in the MakePlaylist prompt node to the document input in the Flow output node.

5- Choose Save to save flow.

6- Test flow by entering the following JSON object is the Test flow pane on the right.
Click Run and the flow should return a model response.

{ "genre": "pop", "number": 3 }

Create a flow with a condition node

Shows a flow with one condition node returns one of three possible values based on the condition that is fulfilled:

Prerequisites for Implementing New Capabilities in Amazon Bedrock Flows

Before utilizing the enhanced safety and traceability features in Amazon Bedrock Flows, ensure the following prerequisites are met:

1- An AWS account
2- In Amazon Bedrock:
Create and test your base prompts for customer service interactions in Prompt Management.
Set up your knowledge base with relevant customer service documentation, FAQs, and product information.
Configure any auxiliary AWS services needed for your customer service workflow (for example, Amazon DynamoDB for order history).

3- In Amazon Bedrock Guardrails:

Create a guardrail configuration for customer service interactions (for example, CustomerServiceGuardrail) with:
Content filters for inappropriate language and harmful content
Personally identifiable information (PII) detection and masking rules for customer data
Custom word filters for company-specific terms
Contextual grounding checks to ensure accurate information
Test and validate your guardrail configuration.
Publish a working version of your guardrail.

4- Required IAM permissions:
Access to Amazon Bedrock Flows
Permissions to use configured guardrails
Appropriate access to any integrated AWS services

Next Steps

Once these prerequisites are in place, you can seamlessly implement the new capabilities to enhance your customer service workflows with Amazon Bedrock Flows.

Enabling enhanced safety in Flows

For Customer service chatbot, implementing guardrails helps ensure safe, compliant, and consistent customer interactions.

Enable guardrails in both Prompt node and Knowledge base node:

1- In AWS Console for Amazon Bedrock, open the Prompt node or Knowledge base node in your customer service flow where you want to add guardrails. Create a new flow if required.

2- In the node configuration panel, locate the Guardrail section.

3- Select an existing guardrail from the dropdown menu.

4- In this instance, CustomerServiceGuardrail-chatbot is configured to:

-1- Mask customer PII data (name & email)

-2- Block inappropriate language & harmful content

-3- Have responses align with company policy

-4- Maintain professional tone in responses

5- Choose the appropriate version of your guardrail.

6- Enter your prompt message for customer service.

7- Connect your Prompt node to the flow’s input & output nodes.

8- Test your Flows with the implemented guardrails.

9- In the Test flow shown on the right pane of the interface, you can see how the model response handles sensitive information.

Original response: “ Dear Mr. John Smith… ”
Guardrail response: “ Dear Mr. {NAME}… ”

Enhanced traceability with Flows Trace View

The new Flows Tracing capability now provides detailed visibility into the execution of the flows, enhancing debugging capability with Trace view and inline validations.

This monitoring solution helps developers monitor, debug, and optimize their GenAI workflows more effectively.

Key benefits of enhanced traceability with Flows Trace View:

Complete execution path with visibility through Trace view
Detailed input/output tracing for each node
Errors, warnings, and execution timing for every node
Quick identification of bottlenecks and issues
Faster root-cause analysis for errors
Monitor response times for user interactions
Identify patterns in user queries that cause delays
Debug issues in the conversation flow
Optimize the user experience

To use the Trace view:

1- In Amazon Bedrock console, open your flow and test it.

2- After running your flow, choose Show trace to analyze.

3- Review the Flow Trace window showing:

1- Response times for each step

2- Guardrails are applied

3- How customers inputs are processed

4- Performance bottlenecks

4- Analyze execution details, including:

1- Processing steps

2- Response generation & validation

3- Time taken by each step

4- Error details

Inline validation status

Flows visual builder and SDK now include intuitive node validation capabilities:

Visual Builder:

Green indicates → a valid node configuration.

Red indicates → an invalid node configuration.

Yellow indicates → a warnings node configuration.

These validation capabilities help developers & AI engineers quickly identify & resolve potential issues by giving real-time validation feedback.

Workshops:

No-code generative AI application development with Amazon Bedrock Flows
https://catalog.us-east-1.prod.workshops.aws/workshops/ca98ae19-48a5-4f95-90e3-56bbfeaae0fc/en-US

Simplifying the Prompts Lifecycle with Prompt Management and Prompt Flows for Amazon Bedrock
https://catalog.us-east-1.prod.workshops.aws/workshops/c81935bc-0b43-4bd6-bd01-db45f847d6bd/en-US

Some of Resources here from AWS Documentations and Blog.

AWS user guide for Guardrails integration and Traceability.

Discovering the Latest Features of AWS CloudFront: Enhancing Performance and Security

Fady Nabil — Sat, 23 Nov 2024 19:04:56 +0000

🚀 Amazon CloudFront now supports VPC Origins, allowing private network resources in your AWS account to connect directly to CloudFront.
https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-cloudfront-vpc-origins/

🚀 CloudFront Virtual Private Cloud (VPC) Origins: Shield your web applications from public internet
https://aws.amazon.com/blogs/networking-and-content-delivery/introducing-cloudfront-virtual-private-cloud-vpc-origins-shield-your-web-applications-from-public-internet/

Amazon CloudFront introduced CloudFront Virtual Private Cloud (VPC) Origins, a new feature that allows users to use CloudFront to deliver content from applications hosted in a VPC private subnet.

This long-awaited feature eliminates the need to make load balancers public to leverage CloudFront's performance and caching capabilities. 🌐

🎯 The benefits?

Enhanced Security Posture: Keep your backend resources private while still benefiting from CloudFront's global distribution.
Simplified Architecture: No more workarounds to protect sensitive resources.
Improved Compliance: Aligns with many security and regulatory requirements.
No additional costs compared to the 'old' setup

Steps for VPC origins in CloudFront:-

Create a VPC Origin for your existing public subnet application: A. Open the CloudFront console and select VPC Origins from the left navigation, as shown in the following figure.
Use CloudFront's continuous deployment to create a staging distribution: A. Create a VPC Origin by selecting the ALB that we created previously. B. Now that we have a VPC origin created and deployed, then we can use the VPC origin to create an origin within a CloudFront distribution. C. Do that through a staging distribution to safely promote the switch to the newly created VPC origin. D. Create a staging distribution, and add a new origin by choosing the VPC Origin created. E. Update the behaviors pointing to the existing origin to use the new VPC Origin.
Test the VPC Origin in the staging distribution: A. Make sure that the VPC Origin functions as expected. B. This helps make sure that your VPC configuration is accurate.
Promote the staging distribution's configuration to the primary distribution: A. After confirming that the VPC Origin works correctly in the staging environment, you can promote the configuration to your production distribution. B. Remove public access to your application. Your application now inaccessible from the public internet, but CloudFront still has private access to it through the VPC Origin.

🚀 Amazon CloudFront VPC origins: Enhanced security and streamlined operations for your applications.
For resources such as ALBs/NLBs/EC2s, CloudFront now allows the most secure origin cloaking mechanism. You can keep these resources on a private subnet, and only CloudFront will be able to communicate with them. No additional charge.
https://aws.amazon.com/blogs/aws/introducing-amazon-cloudfront-vpc-origins-enhanced-security-and-streamlined-operations-for-your-applications/

🚀 Amazon CloudFront now supports additional log formats and destinations for access logs.
CloudFront's standard logging got some love. You can now select the field you want to log, select additional destinations such as CloudWatch and Amazon Data Firehose, and choose new formats such as JSON and Parquet.
Here's what's new:
✅ Direct log delivery to CloudWatch Logs and DataFirehose
✅ Expanded log output formats, including Parquet and JSON
✅ Customizable S3 prefixes and partitioning for S3 log delivery
✅ Selectable log fields for tailored insights
✅ Log delivery to S3 buckets in opt-in AWS regions
https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-cloudfront-log-formats-destinations-access/

🚀 Using CloudFront Functions, you can now route requests to different origins. Couple of KeyValueStore, it's a powerful capability to implement global traffic routing with minimal cost.
https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-cloudfront-origin-modifications-cloudfront-functions/

🚀 CloudFront supports another API communication protocol, gRPC, i addition to WebSockets and HTTP (REST, graphQL, etc..). gRPC is a byte efficient protocol based on Google's protobuf. No additional charge.
https://aws.amazon.com/blogs/aws/amazon-cloudfront-now-accepts-your-applications-grpc-calls/

🚀 CloudFront allows anycast routing with static IP addresses, opening up new use cases such as Zero rating agreements or allow listing IP addresses on firewalls. It has additional charges and currently it requires ticket to support, to progressively ramp up customers to this feature.
https://aws.amazon.com/blogs/networking-and-content-delivery/zero-rating-and-ip-address-management-made-easy-cloudfronts-new-anycast-static-ips-explained/

🚀 When running live video streaming workload, you can now implement better resiliency using Media Quality-Aware Resiliency. It catches grey failures (e.g. black screen or bad video quality) on a MediaPackage endpoint to failover to another MediaPackage endpoint with better quality score.
https://aws.amazon.com/about-aws/whats-new/2024/11/aws-announces-media-quality-aware-resiliency-live-streaming/

aws #awscommunitybuilder #awscommunitybuilders #cloudfront #aws_cloudfront #cdn #security #waf #aws_waf

New features in Amazon Bedrock Prompt Management

Fady Nabil — Fri, 08 Nov 2024 22:22:36 +0000

AWS just launched the general availability (GA) of Prompt Management, a new feature that makes it easier for developers and prompt engineers to build GenAI applications in Amazon Bedrock.

Amazon Bedrock Prompt Management simplifies the creation, evaluation, versioning, and sharing of prompts to help developers and prompt engineers get better responses from foundation models (FMs) for their use cases.

AWS launch includes two new capabilities:

Structured prompts - Define system instructions, tools, and additional messages when building your prompts.
Converse API integration - Invoke your cataloged prompts directly from the Amazon Bedrock Converse and InvokeModel API calls.

These functionalities allow customers to easily run prompts stored in their AWS accounts, store prompts for Bedrock Agents, and easily compare different versions of prompts to spot differences.

This will accelerate workflows for devs and their teams.

https://aws.amazon.com/blogs/machine-learning/amazon-bedrock-prompt-management-is-now-available-in-ga/

New Feature: Amazon CloudFront no longer charges (No Billing) for requests blocked by AWS WAF

Fady Nabil — Fri, 08 Nov 2024 15:11:29 +0000

AWS has introduced an invaluable feature for users of CloudFront protected by AWS WAF: CloudFront will no longer bill requests that are blocked by AWS WAF. This new feature provides enhanced financial protection, especially helpful against DDoS attacks, that generate a significant volume of requests on CloudFront.

https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-cloudfront-charges-requests-blocked-aws-waf/

Some additional insights:

Billing Exemptions on Blocked Requests: CloudFront does not apply billing on a request blocked by WAF, when the terminating rule action in WAF is BLOCK, regardless of the custom response configured in WAF. For example, you could configure a custom response with a 200 OK for a graceful HTML for blocked request.

Custom Error Responses: CloudFront will also not bill for custom error responses triggered by WAF’s BLOCK actions. This means if WAF blocks a request and triggers an error response configured in CloudFront, those error-handling responses won’t incur charges.

Extra Protection with Shield Advanced: Customers who are subscribed to Shield Advanced gain even more financial protection. It protects you against the costs of CloudFront requests that were not blocked by WAS WAF during a DDoS attack. It also cover other AWS services that had to scale to absorb the attack.

Amazon Web Services (AWS) has announced today a new edge location in #Qatar

Fady Nabil — Mon, 04 Nov 2024 08:12:37 +0000

Amazon Web Services (AWS) has announced today a new edge location in #Qatar, opening up exciting opportunities for businesses based in the region! With this edge location, companies in Qatar can now benefit from Amazon CloudFront’s powerful content delivery network, which ensures faster, more reliable access to data and applications.

All Amazon CloudFront edge locations are protected against infrastructure-level DDoS threats and against common web exploits and bot attacks by enabling AWS Web Application Firewall (WAF).

https://aws.amazon.com/about-aws/whats-new/2024/11/aws-announces-edge-location-qatar/

AWS #WAF #Cloudfront

Unlocking the Power of AWS Console to Code: A Game-Changer for DevOps and Infrastructure as code (IaC)

Fady Nabil — Fri, 18 Oct 2024 18:30:47 +0000

AWS is announcing the general availability of Console to Code, powered by Amazon Q Developer. which streamlines the transition from prototyping within the AWS Management Console to creating production-ready code. This tool allows users to easily generate code for console actions in their chosen format with just one click. By providing starter code, it helps users quickly set up and initiate automation pipelines, simplifying the path to production deployment.

The tool allows users to easily translate actions performed in the console into reusable code in a language of their choice. While customers often use the AWS Management Console for learning and prototyping, Console to Code captures these actions and converts them into code. It supports CLI, CloudFormation, and CDK formats. The CLI code is generated in real-time as users interact with the console, following AWS best practices. Additionally, Amazon Q Developer GenAI capability generates CDK and CloudFormation code, which adheres to guided AWS best practices for reliable deployments. Customers can download or copy the code, making it easy to customize and adapt for production use. This eliminates the need to choose between the console or Infrastructure-as-Code (IaC) solutions.

Console to Code, powered by Amazon Q Developer, is generally available in commercial regions for Amazon Elastic Compute Cloud (EC2), Amazon Virtual Private Cloud (VPC) and Amazon Relational Database Service (RDS).**** Learn more about Console-to-Code.

AWS Console to Code?
AWS Console to Code enables users to automatically convert manual console configurations into infrastructure-as-code (IaC). It streamlines the process by transforming AWS Console settings and configurations into code snippets in formats like JSON, YAML, or Python (Boto3), which can be integrated with AWS CloudFormation or other IaC tools.

Rather than configuring AWS resources manually through the Console for each task, developers and engineers can now generate reusable code. This approach speeds up deployments, ensures consistency across environments, and promotes automation.

How AWS Console to Code Benefits DevOps
The DevOps approach emphasizes automation, collaboration, and continuous integration/continuous deployment (CI/CD) pipelines to optimize development and operational workflows. AWS Console to Code is particularly beneficial in this context due to its potential to:

Accelerate Automation: By generating code for cloud resources, DevOps teams can speed up the automation process. Instead of manually recreating infrastructure configurations, engineers can quickly turn console actions into code, which can be reused across multiple environments, promoting efficiency in DevOps pipelines.
Consistency Across Environments: Code-generated configurations ensure that infrastructure is consistently deployed across development, staging, and production environments. This consistency minimizes configuration drift and errors due to manual intervention, enhancing overall system reliability.
User-Friendly Infrastructure Setup: For users less familiar with IaC languages, AWS Console to Code provides an intuitive way to configure infrastructure directly from the console, then generate the corresponding code. This reduces the learning curve for infrastructure automation, making it more accessible to a broader audience.
Seamless Code Generation: Users can leverage the tool to manage small to medium-sized cloud infrastructures without needing deep knowledge of code writing. Once the desired infrastructure is created via the AWS Console, it can be exported as reusable code that can be integrated into existing IaC templates.

New features in GA

Support for more services – During preview, the only supported service was Amazon EC2. At GA, AWS Console-to-Code has extended support to include Amazon Relational Database Service (RDS) and Amazon Virtual Private Cloud (Amazon VPC).
Simplified experience – The new user experience makes it easier for customers to manage the prototyping, recording and code generation workflows.
Preview code – The launch wizards for EC2 instances and Auto Scaling groups have been updated to allow customers to generate code for these resources without actually creating them.
Advanced code generation – AWS CDK and CloudFormation code generation is powered by Amazon Q machine learning models.

Getting started with AWS Console-to-Code
Let’s begin with a simple of launching an Amazon EC2 instance. Start by accessing the Amazon EC2 console. Go to the AWS Console-to-Code widget on the right and choose Start recording to initiate the recording.

Now, launch an Amazon EC2 instance using the launch instance wizard in the Amazon EC2 console. After launched, choose Stop to complete the recording.

In the Recorded actions table, review the actions that were recorded. Use the Type list to filter by write actions (Write). Choose the RunInstances action. Select Copy CLI to copy AWS CLI command.

This is the CLI command that I got from AWS Console-to-Code:

aws ec2 run-instances \
  --image-id "ami-066554784287e358dad1" \
  --instance-type "t2.micro" \
  --network-interfaces '{"AssociatePublicIpAddress":true,"DeviceIndex":0,"Groups":["sg-1aa"]}' \
  --credit-specification '{"CpuCredits":"standard"}' \
  --tag-specifications '{"ResourceType":"instance","Tags":[{"Key":"Name","Value":"c2c-demo"}]}' \
  --metadata-options '{"HttpEndpoint":"enabled","HttpPutResponseHopLimit":2,"HttpTokens":"required"}' \
  --private-dns-name-options '{"HostnameType":"ip-name","EnableResourceNameDnsARecord":true,"EnableResourceNameDnsAAAARecord":false}' \
  --count "1"

This command can be easily modified. Updated it to launch two instances (--count 2) of type t3.micro (--instance-type). This is a simplified.
I executed the command using AWS CloudShell and it worked as expected, launching two t3.micro EC2 instances:

The one-click CLI code generation is driven by the API commands triggered during actions, such as when launching an EC2 instance. Notably, the companion screen displays the recorded actions in real-time as you complete them in the console. With its interactive UI, which includes start and stop controls, you can easily define and limit the scope of actions for more efficient prototyping.

IaC generation using AWS CDK
AWS CDK is an open-source framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. With AWS Console-to-Code, you can generate AWS CDK code (currently in Java, Python and TypeScript) for your infrastructure workflows.
Lets continue with the EC2 launch instance. If you haven’t done it already, in the Amazon EC2 console, Go to AWS Console-to-Code widget on the right, choose Start recording, and launch an EC2 instance. After the instance is launched, choose Stop to complete the recording and choose the RunInstances action from the Recorded actions table.
To generate AWS CDK Python code, choose the Generate CDK Python button.

You can use the code as a starting point, customizing it to make it production-ready.

You can also generate CloudFormation template in YAML or JSON format:

Preview code
You can also directly access AWS Console-to-Code from Preview code feature in Amazon EC2 and Amazon EC2 Auto Scaling group launch experience.
The steps to create an Auto Scaling group using a launch template. However, instead of Create Auto Scaling group, click Preview code. You should now see the options to generate infrastructure code or copy the AWS CLI command.

Things to know
Here are a few things you should consider while using AWS Console-to-Code:

Anyone can use AWS Console-to-Code to generate AWS CLI commands for their infrastructure workflows. The code generation feature for AWS CDK and CloudFormation formats has a free quota of 25 generations per month, after which you will need an Amazon Q Developer subscription.
It’s recommended that you test and verify the generated IaC code code before deployment.
At GA, AWS Console-to-Code only records actions in Amazon EC2, Amazon VPC and Amazon RDS consoles.
The Recorded actions table in AWS Console-to-Code only display actions taken during the current session within the specific browser tab, and it does not retain actions from previous sessions or other tabs.

AWS Community Builders partnership with Techne Summit Cairo 2024

Fady Nabil — Thu, 25 Apr 2024 20:03:42 +0000

AWS Community Builders are proud to announce our partnership with Techne Summit Cairo 2024. Techne Summit is coming to you on MAY. 25 - 27. There will be lots of exciting things planned for you with a lot of Content planned, over several Industry-Focused Tracks! Hurry up and book your ticket now.
https://cairo.technesummit.com/2024