Forem: Ajaykumar Yavagal

Hermes Agent — The System That Doesn’t Stop When the Task Ends

Ajaykumar Yavagal — Fri, 15 May 2026 23:49:59 +0000

Hermes Isn't a Chatbot. It's an Agent Runtime.

This is a submission for the Hermes Agent Challenge

The first time you run Hermes, nothing about it feels unusual.

A CLI.
A loop.
A few commands.

Another agent.

And that’s why most people will underestimate it.

Because if you stop there, you miss what’s actually happening.

Hermes is not optimizing responses.

It is beginning to remember.

The Misunderstanding

Most people encountering Hermes will interpret it as:

a coding assistant
a tool wrapper
a prompt loop with memory
a nicer interface over LLMs

All reasonable conclusions.

All incomplete.

Hermes is not fundamentally a chatbot.

It is an agent runtime.

And more importantly:

It is structured like something that expects to stay alive.

The Shift: From Responses to Runtime

Most AI systems today operate like this:

Input → Prompt → Model → Output → End

Hermes does something fundamentally different:

State → Context → Reason → Act → Store → Continue

This is the shift from:

answering → operating
stateless → persistent
reactive → continuous

What Hermes Actually Builds

At the center of Hermes is not an interface.

It is a loop.

A managed, long-lived, stateful loop.

Everything else orbits that loop:

CLI
messaging gateways
schedules
batch jobs
protocol adapters

This is not how you design a chatbot.

This is how you design a runtime.

Architecture That Reveals Intent

User / External Surface
→ Interfaces (CLI, Gateway, MCP, Scheduler)
→ Agent Runtime
→ Context Engine + Memory Manager
→ Tools + Integrations
→ Providers
→ Persistent State

Every layer isolates responsibility.

Every layer can evolve.

Hermes is not an app.

It is a system that can host intelligence.

Memory Is Not a Feature — It's a Foundation

Hermes separates memory into distinct layers:

curated long-term memory
searchable session history
external memory providers

It distinguishes between:

what must persist
what can be retrieved
what should be summarized

That is not prompt engineering.

That is information architecture.

Context Is Treated Like Lifecycle

Hermes does not treat context overflow as failure.

It treats it as evolution.

compresses intelligently
preserves critical context
rotates sessions
maintains lineage

Context becomes a managed lifecycle rather than a limitation.

Tools Are Capabilities

Hermes defines a structured tool system:

tools register themselves
define schemas
execute safely

The model does not just generate text.

It selects actions within a system.

Delegation Changes Everything

Hermes can spawn sub-agents.

Those sub-agents:

run in isolation
have bounded context
use restricted tools
return results

This shifts intelligence from linear to distributed.

Agents as Processes

Hermes treats agents not as calls, but as processes.

Not something invoked once.

Something that runs.

while alive:
    observe()
    reason()
    act()
    update()

This loop is the system.

Why This Matters

AI is moving from:

response systems

to:

runtime systems

Where the system itself:

holds memory
coordinates actions
persists over time

The Bigger Shift

The useful unit of AI is no longer the prompt.

It is the runtime.

Not isolated responses.

Persistent systems.

Final Thought

Hermes is not important because of what it does today.

It is important because of what it implies.

The moment AI systems stop resetting...

They stop behaving like assistants.

They begin to persist.

Gemma 4 and the End of API-Dependent AI

Ajaykumar Yavagal — Fri, 15 May 2026 16:06:22 +0000

This is a submission for the Gemma 4 Challenge: Write About Gemma 4

For years, we built AI systems by renting intelligence — this explores what happens when we finally start owning it.

We called APIs.

We paid per token.

We accepted latency, outages, pricing changes, and vendor lock-in as normal.

And eventually, we stopped questioning it.

If you wanted serious AI capability, you didn’t own it.

You leased it.

The API Era Shaped How We Build

Modern AI systems were designed around centralized intelligence.

Your application didn’t contain intelligence.

It depended on it.

That decision shaped everything:

Architecture
Cost structure
Performance
Privacy
Scalability

A large number of “AI products” became thin layers over external models.

User Input → Backend → API → Model → Response → Cost

This created a strange reality:

Core product capabilities were external
Margins depended on someone else’s pricing
Reliability depended on another company
Scaling increased dependency instead of reducing it

We accepted it because we had no meaningful alternative.

Gemma 4 Changes the Assumption

Gemma 4 doesn’t matter because it wins every benchmark.

It matters because it changes something deeper:

You can now own capable AI instead of permanently renting it.

That single shift changes how modern software gets designed.

For the first time, developers can realistically ask:

How much of my system actually needs a remote model?

That’s a very different question from:

Which model is best?

Benchmarks Don’t Build Systems

AI discussions are increasingly dominated by:

Benchmark scores
Reasoning rankings
Throughput metrics

But products don’t ship benchmarks.

They ship systems.

And real systems care about:

Latency
Cost predictability
Deployment flexibility
Privacy
Control

The question is no longer:

“Is this the smartest model?”

It’s:

“Is this model sufficient to own the stack?”

Capability vs Practicality

Frontier models still lead in:

Deep reasoning
Complex planning
Advanced synthesis

But most real-world workloads don’t need maximum intelligence.

They need:

Summarization
Transformation
Structured outputs
Log analysis
Lightweight reasoning

In these scenarios, a model that is:

Local
Fast enough
Private
Low-cost

can create a better overall system, even if it scores lower on benchmarks.

Local AI Changes the Development Experience

This shift is not just technical — it’s experiential.

With APIs, every interaction introduces friction:

Network calls
Latency gaps
Rate limits
Cost awareness

With local models:

Responses begin immediately
Iteration becomes effectively free
Experimentation accelerates

Even if raw speed is slower, the system often feels faster.

AI stops being a service and becomes part of the system.

Privacy Becomes Structural

Privacy is often treated as a feature.

Local AI makes it architectural.

Entire categories of software become easier to build:

Internal tools
Proprietary code analysis
Security systems
Regulated environments
Offline applications

You’re no longer asking:

“Can we send this data out?”

Because you don’t have to.

From Renting Intelligence to Owning It

The biggest shift is economic.

API-first AI

Pay per request
Costs scale with usage
Dependency increases

Local-first AI

Costs stabilize
Control increases
Systems become customizable

This moves AI from:

a metered service → to infrastructure

A Small but Real Example

While exploring this shift, I built a local-first system using Gemma 4

to process and interpret security events.

Instead of sending logs to external services, the system:

Analyzes event patterns locally
Generates structured threat explanations
Provides actionable recommendations

What stood out was not just capability — but the workflow shift:

No concern about API cost
Faster iteration loops
Full control over sensitive data

It revealed something subtle but powerful:

Owning the intelligence changes how the system behaves.

Real-World Scenario

Consider a small hospital or organization without a dedicated security team.

A sequence of events occurs:

Multiple failed login attempts
A successful login from an unusual source
Execution of a suspicious script
Persistence mechanisms being installed

In most systems, these appear as isolated log entries.

No clear narrative.

No immediate action.

But when processed locally by a system like this:

The sequence is recognized as a coordinated attack
The risk is clearly explained
Immediate response actions are generated

Instead of raw logs, the system produces:

A clear threat explanation
Context-aware insights
Actionable remediation steps

This is the difference between:

detecting events and understanding threats

And importantly, all of this happens locally — without sending sensitive system data outside the organization.

AI Watchdog in Action

Below is a real example of AI Watchdog analyzing a multi-stage attack using Gemma 4 running locally:

This example shows:

A sequence of suspicious events across a single host
Real-time threat classification (Critical, High, Medium)
Structured AI-generated insights
Actionable response recommendations

The system transforms fragmented logs into a coherent attack narrative — locally, without external APIs.

This Shift Has Happened Before

Computing has always moved in cycles:

Mainframes → centralized
PCs → decentralized
Cloud → centralized again

AI is beginning its own shift.

For years, advanced intelligence lived in remote systems.

Now, it’s moving closer to developers again.

Frontier Models Still Matter

This is not the end of APIs.

Frontier models still lead in:

Advanced reasoning
Complex problem-solving
Research-grade tasks

But the gap between:

“best possible” and “good enough for real systems”

is shrinking quickly.

And that’s where disruption happens.

What Gemma 4 Represents

Gemma 4 is not just another model release.

It represents a change in assumption:

Powerful AI does not have to remain centralized.

And once developers realize capable AI can increasingly run locally, the economics and architecture of software start changing with it.

System design
Cost models
Developer workflows

Final Thoughts

For years, building with AI meant renting intelligence.

Gemma 4 suggests a different future:

Local
Private
Controllable
Deployable anywhere

Not perfect.

But increasingly sufficient.

In software, “sufficient and owned” often beats “perfect and rented.”

The real question is no longer:

Which model is smartest?

It’s:

Which model lets you build the best system?

Maybe the future of AI is not about accessing the smartest model on Earth.

Maybe it’s about owning intelligence that is:

Good enough
Always available
Fully under your control

And for the first time in a long while, that shift feels within reach.

ProdSeer — AI-Powered Production Failure Prediction™

Ajaykumar Yavagal — Fri, 08 May 2026 16:31:24 +0000

Building ProdSeer: AI-Powered Production Failure Prediction™

Modern systems rarely fail because of one bug.

They fail because of hidden operational complexity:

cascading dependencies
infrastructure bottlenecks
observability blind spots
external API failures
scaling assumptions

So for the MeDo Hackathon, I built ProdSeer — an AI-powered Production Failure Prediction™ platform.

ProdSeer analyzes GitHub repositories, simulates cascading infrastructure failures, and forecasts production survivability before deployment using structured AI reasoning workflows.

What ProdSeer Does

ProdSeer goes beyond static code analysis.

It:

analyzes repository architecture
infers production topology
identifies operational bottlenecks
simulates cascading failure scenarios
forecasts survivability under production pressure
generates infrastructure redesign recommendations

Some Features

⚡ Repository intelligence
🕸️ Infrastructure topology visualization
🔥 Cascading failure simulation
📉 Survival probability forecasting
🛡️ Operational risk analysis
💬 Conversational infrastructure reasoning
📄 Executive production-readiness reports

The Most Interesting Part

One of the wildest moments during development was watching AI reason about:

AI-agent architectures
degraded operational modes
LLM dependency bottlenecks
secure command execution
infrastructure survivability

The system began generating surprisingly believable operational redesigns involving:

Kubernetes
Redis
observability pipelines
secure execution sandboxes
API resilience layers

Tech Stack

MeDo AI Framework
Gemini 2.5 Flash

Final Thoughts

AI is making software development dramatically faster.

But as AI-generated systems become more complex, operational uncertainty also increases.

ProdSeer was an experiment in exploring what AI-native operational intelligence could look like.

And honestly… watching AI simulate infrastructure collapse scenarios was mind-bending 😄

Links

🚀 Live App: https://app-bhgywf5bbuv5.appmedo.com/

🎥 Demo Video: https://www.youtube.com/watch?v=y_sFMDpOYxs

Forem: Ajaykumar Yavagal

Hermes Agent — The System That Doesn’t Stop When the Task Ends

Hermes Isn't a Chatbot. It's an Agent Runtime.

The Misunderstanding

The Shift: From Responses to Runtime

What Hermes Actually Builds

Architecture That Reveals Intent

Memory Is Not a Feature — It's a Foundation

Context Is Treated Like Lifecycle

Tools Are Capabilities

Delegation Changes Everything

Agents as Processes

Why This Matters

The Bigger Shift

Persistent systems.

Final Thought

Tags

Gemma 4 and the End of API-Dependent AI

The API Era Shaped How We Build

Gemma 4 Changes the Assumption

Benchmarks Don’t Build Systems

Capability vs Practicality

Local AI Changes the Development Experience

Privacy Becomes Structural

From Renting Intelligence to Owning It

API-first AI

Local-first AI

A Small but Real Example

Real-World Scenario

AI Watchdog in Action

This Shift Has Happened Before

Frontier Models Still Matter

What Gemma 4 Represents

Final Thoughts

Tags

ProdSeer — AI-Powered Production Failure Prediction™

Building ProdSeer: AI-Powered Production Failure Prediction™

What ProdSeer Does

Some Features

The Most Interesting Part

Tech Stack

Final Thoughts

Links

BuiltWithMeDo