Forem: Fleeks

The Last Infrastructure Problem AI Will Ever Face

Fleeks — Mon, 16 Mar 2026 06:00:47 +0000

by Victor M, Co-Founder at Fleeks

We didn't build a faster deployment tool. We built the environment AI was always supposed to think inside.

We are witnessing a fundamental mismatch in the stack.

We are building the most sophisticated "brains" in history and plugging them into a nervous system that responds in minutes, not milliseconds.

If you give a 160-IQ AI agent a task, but it has to wait 5 minutes for a Docker build or a CI/CD pipeline every time it wants to test a hypothesis, you haven't hired an engineer. You've hired a genius and locked them in a room with a 56k dial-up connection.

The bottleneck isn't reasoning anymore. It is the latency of reality. Until the infrastructure moves at the speed of the model’s thought, "Autonomous Engineering" is just a marketing slogan. We didn't build Fleeks to be another deployment tool; we built it to be the first environment that doesn't make the agent wait.

The Wrong Question the Industry Is Asking
The Handoff: From Local Terminal to Cloud Execution
Agents Propose. Humans Approve. Infrastructure Executes.
What Makes Instant Execution Possible
Approval Is Not the Friction. Infrastructure Is.
What Becomes Possible
This Scales Across Every Team Size
The Real Question
Key Takeaways
Stop Waiting. Start Executing.

Imagine shipping a feature at 2am. Not because you pulled an all-nighter, but because an agent did.

It found the bottleneck. It proposed the fix. It deployed, tested, measured, and iterated while you slept. By morning, your service is faster, your infrastructure is leaner, and your backlog has a closed ticket where there used to be a problem.

No standups about it. No sprint planning around it. No deploy pipeline that made everyone wait.

We are at an inflection point that most people have not fully registered yet. AI agents can already reason, debug, refactor, and optimize at a level that would have seemed like science fiction five years ago. The models are extraordinary. The intelligence is genuinely, undeniably here.

But we have been deploying that intelligence into infrastructure designed for humans.

Deploy pipelines. Container cold starts. CI queues. Health checks. DNS propagation. Systems built for a world where a five minute wait was fast, because the person waiting was a person.

The agent finishes thinking in 30 seconds.

Then it waits four and a half minutes for the world to catch up.

Think about what that actually costs. An agent needs five iterations to solve a problem. Each loop takes five minutes. That is 24 minutes of infrastructure wait for what should have been a 2.5 minute fix. Multiply that across every agent, every task, every team building on top of AI and the number gets staggering. We are burning engineering hours, compute budgets, and developer trust on latency that has nothing to do with intelligence.

Agent reasons (30s)
CI/CD Pipeline & Docker Build (4m)
Container starts & fails (30s) ...repeat 5 times.

The Legacy Stack (24 minutes):

Agent reasons (30s)
CI/CD Pipeline & Docker Build (4m)
Container starts & fails (30s)
...repeat 5 times.
Agent reasons (30s)
Fleeks executes (200ms)
Container fails instantly (0s)
...repeat 5 times.

The Fleeks Runtime (2.5 minutes):

Agent reasons (30s)
Fleeks executes (200ms)
Container fails instantly (0s) ...repeat 5 times.

We are burning engineering hours, compute budgets, and developer trust on latency that has nothing to do with intelligence.

This is the problem no one is building loudly enough against. Not the models. Not the reasoning. Not the benchmarks. The invisible layer between what an agent decides and what actually happens in the world. That layer is broken, patched together from infrastructure that was never meant to move at agent speed, and almost nobody is rebuilding it from first principles.

We did.

Fleeks is a container system built for full context. Agents do not just run in isolation. They operate inside a live, aware, persistent runtime that holds the entire state of your project. They know what is deployed. They know what changed. They know what broke and when. And they can act on that knowledge in seconds, not minutes, because the infrastructure underneath them was designed to move as fast as they think.

This is not a faster deployment tool. This is not a better CI pipeline. This is a different model entirely. One where the environment evolves with the agent, where iteration is measured in seconds, and where a developer's relationship with infrastructure shifts from managing it to approving what the agent already figured out.

The future we are building looks like this: developers who move faster than any team their size should be able to. Startups that operate with the infrastructure leverage of companies ten times their headcount. Agents that do not just assist, they execute, inside a runtime built specifically to let them.

Not someday.

Right now, for the teams already building on Fleeks.

Here is how it works.

The Wrong Question the Industry Is Asking

Most platforms building for AI agents have organized themselves around a single question.

How do we safely let agents control infrastructure?

Sandboxing. Permission layers. Isolation. Lockdown.

Reasonable instinct. Wrong frame.

Control is not the constraint. Latency is.

Agents do not need root access. They do not need to own the system. They need an environment that moves at the speed they think, where iteration is cheap, feedback is immediate, and the infrastructure is not the dominant cost in every cycle.

We asked a different question.

How do you build infrastructure that operates at agent speed?

That question leads somewhere completely different.

The Handoff: From Local Terminal to Cloud Execution

You don't need to rewrite your app to use this infrastructure. The bridge is the CLI.

Start building locally in your terminal, then hand off complex, iterative, or long-running tasks to the Fleeks cloud runtime. Your agent gets the same project context, the same code, and the same infrastructure—just at agent speed.

Install the Fleeks CLI

curl -sSL https://releases.fleeks.dev/cli/install.sh | bash
fleeks auth login
fleeks workspace create my-api --template microservices

Start your agent on the task, watch it work in real time
fleeks agent watch my-api

fleeks agent start --task "Optimize database queries for high traffic"
fleeks agent watch my-api

Agents Propose. Humans Approve. Infrastructure Executes.

The model we built is not about giving agents more control.

It is about collapsing the distance between a decision and its execution.

In Fleeks, agents do not deploy directly. They propose changes. Specific, reviewable, approvable changes. You stay in the loop. But the infrastructure beneath that loop is engineered to execute the moment you say go.

No pipeline. No queue. No wait.

import asyncio
from fleeks_sdk import FleeksClient, AgentType

async def main():
    client = FleeksClient(api_key="fleeks_sk_your_key_here")

    # Spin up a workspace in under 200ms
    workspace = await client.workspaces.create(
        project_id="my-api",
        template="python"
    )

    # Agent proposes an optimization, you stay in control
    agent = await workspace.agents.execute(
        task="Optimize this service for high traffic",
        agent_type=AgentType.CODE,
        auto_approve=False  # You review before anything executes
    )

    print(f"Agent proposal ready for review: {agent.proposal_url}")

asyncio.run(main())

That agent might propose scaling container memory, adjusting service concurrency, modifying resource allocation, or deploying optimized code. You review it like a pull request. You approve it. The runtime applies it in seconds.

That is not a workflow improvement. That is a different category of infrastructure.

Prefer TypeScript? The SDK works the same way. Install @fleeks-ai/sdk and you are one import away from the same runtime.

import { FleeksClient } from '@fleeks-ai/sdk';

const client = new FleeksClient({ apiKey: 'fleeks_...' });

const workspace = await client.workspaces.create({
    projectId: 'my-api',
    template: 'node',
});

// Execute a command inside the live container instantly
const result = await workspace.terminal.execute('npm run build');
console.log(result.stdout);

What Makes Instant Execution Possible

Speed without structure is chaos. We built several architectural systems specifically so that fast execution does not mean reckless execution.

Pre-Warmed Execution Pools

Containers do not spin up when you need them. They are already running. Fleeks maintains pre-warmed container pools across regions so that when an agent requests resources, the environment already exists.

Execution start time: under 200ms. Not eventually. Every time.

# This workspace is ready before you finish reading this line
workspace = await client.workspaces.create(
    project_id="performance-test",
    template="python"
)

health = await workspace.get_health()
print(f"Status: {health.status}")            # running
print(f"Started in: {health.startup_ms}ms")  # <200

Dynamic Infrastructure Mutation

Most platforms redeploy an entire service just to change its runtime configuration. Fleeks allows live infrastructure mutation. Memory, concurrency, routing, deployment config applied directly through the runtime scheduler without triggering a new deployment. The service keeps running. The configuration just changes.

CRIU-Based Environment Hibernation

Agents work in bursts. They reason, act, then wait for feedback before acting again. Fleeks uses CRIU-based checkpointing to pause environments mid-execution and resume them with full state intact. No rebuild. No context loss. The agent picks up exactly where it left off.

# Hibernate a workspace mid-task, resume it later with full state
await workspace.hibernate()

# Later, same session or a new one
await workspace.resume()  # Full context, zero rebuild

Workspace-Scoped Isolation

Every agent in Fleeks operates inside a workspace-scoped environment. It can deploy code, modify containers, adjust resources, but only inside its own isolated runtime. It cannot touch global infrastructure. It cannot affect other users.

Fast execution and safe execution are not in tension here. They are designed together.

A Scheduler Built for Agent Workloads

Kubernetes is exceptional at keeping long-running services stable and alive. It was not designed for high-frequency, short-lived, rapid-iteration compute bursts.

Agent workloads are a different shape entirely. Fleeks uses a custom runtime scheduler organized around that shape. Fast task execution, frequent environment changes, compute that appears and disappears in seconds.

Explore the full runtime model in the Fleeks docs.

Approval Is Not the Friction. Infrastructure Is.

There is a belief that human-in-the-loop slows agents down and that full autonomy is the only path to real speed.

We disagree.

Approval only creates friction when the infrastructure under it is slow. When execution is instant, approval becomes a natural part of the feedback loop. It adds seconds of intentionality, not minutes of delay.

Agents propose. Humans review. Infrastructure executes.

The developer stays in control. The agent keeps iterating. That is not a compromise. That is better than full autonomy on slow infrastructure.

What Becomes Possible

When infrastructure latency disappears, the workflows that open up are not just faster versions of what you already do. They are new things entirely.

Five optimization cycles in under a minute. An agent proposes a database change. You approve it. It deploys. The agent measures, proposes another adjustment. What used to take a sprint now takes a conversation.

Real-time scaling, not reactive scaling. An agent detects rising traffic and proposes increased concurrency. Pre-warmed containers allocate immediately. The service scales before users notice anything.

Memory adjustments without restarts. An agent detects memory pressure and proposes increasing container allocation. The scheduler adjusts runtime resources directly. The service never goes down.

Local work handed off to cloud agents. Start building locally, hand off to agents running inside the Fleeks cloud runtime with full project context and infrastructure access already loaded. Build locally, approve, execute, observe, iterate. No pipeline in sight.

This Scales Across Every Team Size

Individual developers get infrastructure that responds the way their AI tools think. Fast, iterative, no waiting on pipelines.

Startups get agents that propose and execute scaling strategies in real time, without a dedicated DevOps function.

Enterprises get full auditability. Every agent action logged, every change approved, every execution cryptographically attested, without sacrificing speed.

The model adapts. The principle does not.

The Real Question

The debate around AI agents keeps circling control.

Should agents have root access? Should they deploy autonomously?

Real questions. Just not the most important one.

Can your infrastructure keep up with the speed agents think at?

Because the difference between a 5 minute deploy loop and a 2 second execution loop is not a developer experience improvement.

It changes what agents are capable of doing at all.

Agents do not control the system.

They propose changes to it.

The infrastructure executes them instantly.

That is not just a better way to build with AI.

It is the only way that scales.

Key Takeaways

Infrastructure latency is the real bottleneck. Models think in seconds. Infrastructure responds in minutes. That gap determines what agents can actually accomplish.
Agents propose, humans approve, infrastructure executes. Full autonomy on slow infrastructure is worse than human-in-the-loop on fast infrastructure.
Pre-warmed execution changes iteration economics. Sub-200ms container acquisition means 50 iterations cost seconds, not hours.
The production lifecycle is the substrate. Agents that cannot deploy autonomously are scripts. Agents that can are operational systems.

Stop Waiting. Start Executing.

Stop treating infrastructure as the thing developers manage. Start treating it as the thing agents move through. The future is not just faster—it’s fundamentally different.

Install the SDK:

pip install fleeks-sdk

Quick Example:

from fleeks_sdk import FleeksClient

async with FleeksClient(api_key="your_key") as client:
    workspace = await client.workspaces.create(
        project_id="demo",
        template="python"
    )
    await workspace.files.create("app.py", "print('Hello')")
    result = await workspace.terminal.execute("python app.py")
    print(result.stdout)

[Boost]

Fleeks — Wed, 04 Mar 2026 13:14:37 +0000

The Agentic Substrate: Why the Production Lifecycle Matters for Autonomous Systems.

Fleeks ・ Mar 4

#ai #infrastructure #devops #architecture

The Agentic Substrate: Why the Production Lifecycle Matters for Autonomous Systems.

Fleeks — Wed, 04 Mar 2026 10:10:05 +0000

By Victor M, Co-Founder at Fleeks

Most AI agents stay in development because production deployment is too slow. At Fleeks, we built infrastructure where agents deploy autonomously in 31 seconds—from code generation to production URL to shareable embed. Zero human intervention.

Core Infrastructure: Sub-200ms Stateful Execution
Orchestration: The MCP Standard
The Structural Foundation: Production Lifecycle
Resource Management: CRIU-Based Hibernation
Real-World Applications
System Architecture
Resources

1. Core Infrastructure: Sub-200ms Stateful Execution

The Problem: Standard serverless cold starts: 3-8 seconds. For an agent doing 50 iterations, that's 150-400 seconds of waiting. Agents give up early because iteration is expensive.

Our Solution: Pre-warmed container pool.

We maintain 1,000+ initialized containers. Agent needs one? Grab from pool in sub-200ms.

for iteration in range(50):
    ws = await client.workspaces.create(f"test-{iteration}")
    await ws.terminal.execute("python test.py")
    result = await ws.files.read("output.json")

Technical implementation:

Metric	Value
Pool size	1,000+ containers per region
Isolation	gVisor for multi-tenant security
Hit rate	>95% under production load
Latency	Sub-200ms (P95)

Tradeoff: Higher baseline cost vs predictable speed. Worth it for agent workloads where iteration speed determines solution quality.

Why custom orchestration instead of Kubernetes? K8s pod startup: 10-30s. Too slow for agent iteration needing sub-200ms. We built a custom scheduler for container pool management. Still use K8s for stateless services.

Performance benchmark:

Operation	Latency
Container acquisition	Sub-200ms
Cold provision fallback	4-5s
Pool hit rate	>95%

2. Orchestration: The MCP Standard for Autonomous Tool Integration

Agents need external systems (GitHub, databases, Slack). We use Model Context Protocol for standardized integration:

{
  "servers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {"GITHUB_PERSONAL_ACCESS_TOKEN": "..."}
    }
  }
}

How it works: Agent asks "list repositories" → MCP translates to GitHub API → Agent gets data.

Integration scope:

270+ community MCP servers available
Protocol: Standardized JSON-RPC over stdio
Configuration: Declarative, not programmatic

Why this scales: Adding tools is configuration, not custom code. Same interface for all external systems.

3. The Structural Foundation: The Production Lifecycle

Traditional deployment takes 20+ minutes with manual steps. For autonomous agents, this breaks the core premise.

A. Polyglot Runtime Execution

# Agent switches languages per task, same workspace
await ws.files.create("analyze.py", ml_code)
await ws.terminal.execute("python analyze.py &")

await ws.files.create("api.js", server_code)
await ws.terminal.execute("node api.js &")

preview = await ws.get_preview_url()
# One URL, multiple services

Tech: 11+ runtime templates (Python, Node.js, React, Go, Rust, Java, Vue, Svelte). Pre-configured dependency management. Single workspace, multi-process execution.

Why this matters: Agent selects optimal language per task. Python for ML, Node for APIs, React for UI—orchestrated autonomously without manual environment switching.

B. Instant Preview URLs

await workspace.terminal.execute("python app.py &")
preview = await workspace.get_preview_url()
# https://workspace-abc.fleeks.run (~30ms)

Tech: Wildcard SSL, Envoy proxy, Cloudflare CDN. Agent validates against real production infrastructure.

Performance: Preview URL generation ~30ms (measured average).

C. Embeds for Distribution

embed = await client.embeds.create(
    name="Demo",
    template=EmbedTemplate.REACT,
    files={"src/App.js": code},
    layout_preset="side-by-side"
)

What you get:

Code editor + live preview
Working runtime (not a screenshot)
100+ concurrent users per embed
Shareable URL or iframe

Use cases: Portfolio sites with runnable demos. Documentation with editable examples. Twitter demos that actually work.

D. Persistent State Architecture

Serverless wipes disk on shutdown. Agents need memory that survives restarts.

Container (ephemeral) → /workspace (persistent)

# Agent writes learned patterns
await workspace.files.create(
    "/workspace/memory.json",
    json.dumps(learned_patterns)
)

# Container restarts, state persists

# Agent reads accumulated knowledge
memory = json.loads(
    await workspace.files.read("/workspace/memory.json")
)

Tech: Distributed filesystem, <10ms writes, replicated across 3 zones.

Impact: Agents solve problems requiring 100+ iterations of accumulated learning.

Why persistent volumes instead of S3? Agents expect normal filesystem operations. S3 has no atomic operations, higher latency, non-POSIX semantics.

4. Resource Management: CRIU-Based Hibernation

Some agents run for hours. Keeping containers up 24/7 is expensive. Stopping them loses process state.

Our solution: CRIU hibernation.

await workspace.terminal.start_background_job("python monitor.py")

await workspace.containers.hibernate()  # ~2s, then $0
await workspace.containers.wake()       # ~2s, exact state

What CRIU preserves:

Process memory (exact state)
Open file descriptors
Network connections
Process IDs

Performance:

Operation	Value
Checkpoint creation	~2 seconds
Restore time	~2 seconds
Success rate	>99% for CPU workloads

Constraint: GPU state not supported (CRIU limitation). CPU workloads fully supported.

5. Real-World Application: Solving Engineering Friction

Self-Healing Infrastructure

Agent that monitors Kubernetes and auto-fixes issues:

async def autonomous_remediation():
    async with create_client() as client:
        agent = await client.workspaces.create("monitor", "python")

        await agent.files.create("monitor.py", """
import json

memory = json.load(open('/workspace/fixes.json'))

for pod in failing_pods:
    issue = analyze(pod)

    if issue in memory:
        apply_fix(memory[issue])  # 10 seconds
    else:
        fix = investigate_and_fix(pod)  # 3-5 minutes
        memory[issue] = fix
        json.dump(memory, open('/workspace/fixes.json', 'w'))
""")

        await agent.terminal.start_background_job("python monitor.py")

Outcome:

Occurrence	Resolution Time
First occurrence	3-5 minutes
Second occurrence	30 seconds
After 50 occurrences	10 seconds

Agent learns and gets faster over time. Persistent state enables learning. Fast provisioning enables validation environments. Production URLs enable fix testing before deployment.

Complete System Architecture

┌─────────────────────────────────────────┐
│ Agent Layer (Customer Code)             │
│ • Reasoning and decision-making         │
│ • Code generation and validation        │
│ • MCP tool integration                  │
│ • State management in /workspace        │
└──────────────┬──────────────────────────┘
               │
┌──────────────▼──────────────────────────┐
│ Fleeks Container Engine                 │
│ • Pre-warmed pool (sub-200ms)           │
│ • gVisor isolation                      │
│ • CRIU hibernation                      │
│ • Multi-template support                │
└──────────────┬──────────────────────────┘
               │
┌──────────────▼──────────────────────────┐
│ Fleeks Production Layer                 │
│ • Dynamic HTTPS (*.fleeks.run)          │
│ • Instant preview URLs (~30ms)          │
│ • Embeddable workspaces                 │
└──────────────┬──────────────────────────┘
               │
┌──────────────▼──────────────────────────┐
│ Fleeks Storage Layer                    │
│ • Persistent /workspace                 │
│ • Distributed filesystem                │
│ • Multi-AZ replication                  │
└─────────────────────────────────────────┘

Each layer enables the one above: Fast provisioning → rapid iteration. Instant URLs → production validation. Embeds → distribution. Persistent state → learning.

Performance Benchmarks

Operation	Latency	Impact
Container acquisition	Sub-200ms	Maintains reasoning flow
Preview URL	~30ms	Instant validation
File write	<10ms	Fast state updates
Embed creation	~1s	Immediate distribution
Hibernation	~2s	Cost-efficient

Infrastructure Comparison

Feature	Lambda	K8s	Fleeks
Cold start	1-8s	10-30s	Sub-200ms
Persistent state	❌	Manual	✅
Preview URLs	❌	Manual	✅
Embeds	❌	❌	✅
Hibernation	❌	❌	✅

Use Fleeks when: AI agents, rapid iteration (50+ cycles), need persistent memory, autonomous deployment.

Use Lambda when: Stateless APIs, infrequent traffic.

Use K8s when: Long-running services, have DevOps team.

Current Technical Constraints

Storage I/O: ~100MB/s per workspace. Sufficient for code/logs/state. Data-intensive workloads may hit limits.
GPU hibernation: Not supported (CRIU limitation). CPU workloads work fine.
Cross-region state: Can't checkpoint in US-East and restore in EU-West yet.
Embed sessions: ~100 concurrent per embed. Higher traffic needs different pooling.

Working on all of these.

Resources

Get Started

Install:

pip install fleeks-sdk

Quick example:

from fleeks_sdk import create_client

async with create_client(api_key="your_key") as client:
    ws = await client.workspaces.create("demo", "python")
    await ws.files.create("app.py", "print('Hello')")
    await ws.terminal.execute("python app.py")

    preview = await ws.get_preview_url()
    print(f"Live: {preview.preview_url}")

Self-improving agent:

async def learning_agent():
    async with create_client() as client:
        ws = await client.workspaces.create("learning")

        try:
            memory = json.loads(await ws.files.read("/workspace/memory.json"))
        except FileNotFoundError:
            memory = {"patterns": [], "iteration": 0}

        for i in range(50):
            memory["iteration"] += 1
            result = await ws.terminal.execute("python task.py")

            if result.exit_code == 0:
                memory["patterns"].append(extract(result.stdout))

            await ws.files.create("/workspace/memory.json", json.dumps(memory))

        return await ws.get_preview_url()

Benchmark It Yourself

import time
from fleeks_sdk import create_client

async def benchmark():
    timings = []
    async with create_client() as client:
        for i in range(10):
            start = time.time()
            ws = await client.workspaces.create(f"bench-{i}")
            elapsed = (time.time() - start) * 1000
            timings.append(elapsed)
            await ws.delete()

    print(f"Avg: {sum(timings)/len(timings):.0f}ms")

Key Takeaways

Infrastructure shapes agent behavior. Fast provisioning (200ms) enables deep exploration. Slow provisioning (5s) forces simple solutions.

State persistence enables learning. Agents accumulate knowledge over 100+ iterations instead of resetting to zero.

Production lifecycle is the substrate. Agents that can't deploy autonomously are experimental scripts, not operational systems.

MCP standardizes tools. 270+ integrations via configuration, not custom code.

You Can't Scale Teams With Fragmented AI (Fleeks Changes That)

Fleeks — Mon, 02 Feb 2026 07:32:47 +0000

The Problem You Know

You hired the best developers. They're using the best AI tools: Cursor, Claude, Copilot.

Your codebase is falling apart.

Not because they're bad developers. Not because your process is broken. Because each AI optimizes for something different, and they have no idea what the others are doing. Developer A designs REST APIs. Developer B expects GraphQL. Developer C builds something different entirely. By Thursday, you're in meetings explaining why incompatibilities exist that shouldn't.

The pattern repeats: a 5-person team wastes hours every week resolving architectural conflicts that shouldn't exist. A 10-person team loses significantly more time to the same problem. A 15-person team needs a dedicated architect just to maintain basic coherence.

And this is the trap: the larger your team grows, the slower everything moves.

What The Real Problem Is

This isn't a documentation problem. It's not a code review problem. It's structural.

Here's what's actually happening: Each AI tool is built to optimize for one thing—speed, accuracy, breadth. None of them know about the decisions the other AI tools are making. When five developers use five different AI tools, you end up with five different architectural visions being built simultaneously, and nobody coordinating between them.

You're manually gluing incompatible pieces together and calling it "alignment."

The invisible cost? Your best engineer stops writing code and starts managing conflicts. Your architecture drifts. Your scaling velocity doesn't just slow—it inverts. You get slower the bigger you grow.

What Actually Solves This

One unified AI that understands your entire system.

Not one tool that's marginally better. Not one process that's slightly stricter.

One AI that holds your entire architectural context.

When Developer A designs the API, the AI understands the mobile and web constraints. When Developer B builds the mobile client, the AI already knows the API contract, the naming conventions, the database schema. When Developer C builds the frontend, everything fits because one intelligence designed it all thinking about all three platforms.

No incompatibility meetings. No re-negotiated contracts. No debugging sessions that take three days to figure out that one team designed REST and another expected GraphQL.

One coherent system. From the start.

How This Changes Your Workflow

Before (Fragmented):

Developer A uses Cursor and builds REST APIs. Fast. Clean. Cursor optimizes for speed.

Developer B uses Claude and designs the mobile client expecting GraphQL. Claude thinks in terms of data graphs.

Developer C uses Copilot and builds frontend components assuming REST, but with different patterns than A.

By Thursday: You're in a meeting explaining why B's client can't talk to A's API. C's frontend breaks on B's assumptions. Three hours spent re-negotiating architectural decisions that shouldn't need re-negotiating.

Each week, this repeats. Each new developer amplifies the problem.

After (Unified):

Developer A: "Build user authentication API"

Unified AI: Understands this needs to work with mobile, web, and CLI. Designs the API with all those constraints in mind.

Developer B: "Build the mobile client"

Unified AI: Already knows A's API design, the naming conventions, the patterns being used. B doesn't have to re-negotiate. B doesn't have to guess.

Code integrates immediately. Everything works. The team scales coherently.

Why This Matters

For growing startups:

You hire your first 5 developers and everything works. They're small enough to talk to each other constantly. Everyone knows the architecture.

Then you hire 5 more. Now you have 10 developers spread across time zones. With fragmented AI tools, coordination overhead doesn't just grow—it grows faster than the headcount. You add 10 developers and lose productivity on 20. With unified infrastructure, new developers onboard in a day because the architectural understanding lives in the AI, not trapped in one person's head.

For technical founders:

You stop building product and start managing architectural conflicts. That DevOps engineer you were thinking about hiring? That budget goes to infrastructure friction instead of product features. Unified infrastructure means you redirect that entire investment to velocity.

For engineering leaders:

Your 10-person team shipped more than your 15-person team under fragmentation. This reverses that. You scale without chaos.

The Hidden Metrics

Metric	Fragmented	Unified
Cross-platform incompatibilities	Common	Eliminated
Architectural conflict meetings/week	Hours wasted	None
Bug fix time	Days of debugging	Minutes of fixing
Developer onboarding time	Weeks	1 day
Scaling velocity	Decreases with team size	Increases with team size
Architectural coherence	Fragments under growth	Stays coherent at scale

What Production-Grade Infrastructure Actually Means

Most engineers think it means: "Doesn't crash. Scales. Has monitoring."

That's operational maturity.

Production-grade infrastructure actually means: The system is coherent. All parts understand each other. New people can join and immediately make decisions that fit the existing architecture.

You can't achieve that if your architectural thinking is fragmented across five AI tools.

When This Problem Becomes Critical

You don't notice it at 5 developers. The team is small enough that alignment happens naturally.

At 10 developers, it becomes visible. You start seeing patterns: meetings about API contracts that were already decided. Bugs that shouldn't exist because the teams are building toward different assumptions. New developers taking weeks to understand why things work the way they do.

At 15 developers, it's exponential. You need a dedicated architect just to maintain basic coherence. Your velocity inverts. You're moving slower than you were with 10.

By 20 developers, you've spent so many resources managing architectural chaos that you realize—way too late—that the problem wasn't the people or the process. It was the infrastructure thinking itself.

The Question You Need To Ask Right Now

Is your entire team using the same AI tool? If not—and be honest with yourself—are you confident they're building toward the same architectural vision?

If the answer is no, you've got an architectural fragmentation problem. You might not feel it yet. But it's there, compounding. It's the invisible drain on your startup velocity that will sneak up on you around 10-12 developers.

How Fleeks Actually Solves This

Single persistent AI agent that holds your entire project context.

Architecture → development → testing → deployment. One AI across all of it. The AI that designed your database schema is the same AI writing your code, validating your tests, handling your deployments. No context resets. No architectural amnesia between steps.

It reads your existing codebase. Point it at your GitHub repo and it learns your patterns, your naming conventions, your architectural decisions. New developers join. The AI already understands your entire system.

fleeks agent "add payments integration with Stripe"

The AI knows:

Your database schema (it reads it automatically)
Your API patterns (from analyzing your existing code)
Your authentication flow (it studied your architecture)
Your naming conventions and style

It designs the payment system to fit seamlessly into what you've already built. Generates code that integrates immediately. No re-negotiation. No architectural conflict.

Multi-developer workspace context. Developer A implements auth. Developer B is building payments. They're not working in separate vacuums—they're working in a shared architectural space where the AI already understands all previous decisions. Integration happens automatically because everything was designed with everything else in mind.

One deploy command for 50+ targets. Web, mobile, desktop, CLI, blockchain. Your unified architecture deploys everywhere. One pipeline. One source of truth.

What This Means In Practice

Before (Fragmented):

10 developers spread across 5 different AI tools
Weekly meetings spent aligning on API contracts and architectural decisions
Most bugs are cross-platform: A's design assumption breaks B's implementation
A three-day bug that could be fixed in an hour if everyone understood the architecture
New developers take weeks to even understand why the system is structured the way it is
You hire a DevOps engineer to manage the chaos

After (Fleeks):

10 developers, one unified AI thinking about the entire system
No alignment meetings needed
Incompatibilities eliminated because everything was designed with everything else in mind
Bugs fix in minutes instead of days because the problem isn't in the code—it was never introduced
New developers understand the architecture in 24 hours because it lives in the AI, not in someone's head
That engineering budget redirects entirely to features

The Path Forward

The solution isn't to ban Cursor. Or forbid Claude. Or restrict Copilot.

The solution is to ensure that whoever—or whatever—is making architectural decisions across your codebase understands your entire system simultaneously.

One coherent architectural vision. One AI that thinks about all of it.

Not five AIs optimizing locally while you manually glue pieces together and call it "alignment."

If your team is fragmenting across multiple AI tools. If you're burning hours in meetings that shouldn't exist. If you need infrastructure that scales coherently instead of inverts.

Fleeks. Unified architectural intelligence. One persistent AI agent. Coherent across all platforms. Production-grade from the start.

Fleeks: The Universal Development Platform with One Unified AI Agent

Fleeks — Sat, 31 Jan 2026 10:02:09 +0000

Deploy to 50+ Platforms From a Single Codebase

The Problem You Know

You built a web app. Now you need iOS and Android. That's three codebases, three deployment pipelines, three times the debugging when something breaks in production.

Or you're tired of context-switching between your architect's design docs, your own code, test frameworks, and deployment configs. Information gets lost. Decisions get re-made.

What We Built

Single persistent AI agent that holds your entire project context across all phases: architecture → development → testing → debugging → deployment. No context resets between steps. The AI that designed your schema is the same AI writing your code and validating your tests against actual requirements.

Pre-warmed container pool with 50+ tech stacks ready to run in 0.2 seconds instead of 8-30s of boot time.

One deploy command that handles web bundling, iOS/Android signing, backend containerization, and store submission. No manual platform-specific pipeline configurations.

How It Actually Works

fleeks agent "build marketplace with user auth and payments"

The AI:

Designs your database schema
Generates React + Express + React Native code
Writes tests that actually validate against your design
Checks for OWASP vulnerabilities
Creates your deployment configs for web, iOS, Android
Breaks it into tasks

fleeks deploy --targets vercel,app-store,google-play

Your code is on all three platforms, signed, with store metadata, ready for review.

Why This Matters

For solo developers:
Web + mobile + backend simultaneously. Deploy to 50+ targets. No hiring.

For teams:
Developer A implements auth. Developer B starts payments. The AI already knows your auth API, database schema, naming conventions. No "wait, what endpoint did we use?" moments. Integration bugs drop dramatically.

For enterprises:
50+ deployment targets, shared context across teams, SOC2/GDPR/HIPAA compliance built in.

The Technical Reality

50+ platforms supported: Vercel, AWS Lambda, App Store, Google Play, Polygon, Solana, etc.
7 AI modes: Architect, Developer, Tester, Reviewer, Debugger, Planner, Supervisor (all one agent, shared context)
Multi-cloud: AWS, GCP, Azure (99.9% SLA)
Your code is yours: Git-integrated, export anytime, not locked in
Works with existing repos: Point it at GitHub/GitLab, it reads your patterns

Supported Tech

Web: React, Vue, Angular, Next.js, Svelte
Mobile: React Native, Flutter, Swift, Kotlin
Backend: FastAPI, Express, Spring Boot, Django, Go, Rust
Blockchain: Solidity, near, Cosmos
CLI, Desktop, IoT: Yes
Plus 40+ more stacks.

vs Competitors

Aspect	Fleeks	Cursor	Replit	Bolt.new
Container startup	0.2s (pre-warmed)	N/A (local)	8-30s (on-demand)	<5s (web-optimized)
AI architecture	Single agent, 7 modes, persistent context	Multi-model, per-file context	Basic chat + code	GPT-4 chat, instant deploy
Deployment targets	50+ with integrated pipelines	Manual setup required	Limited hosting options	Web hosting only
Multi-platform from one codebase	Yes (one repo, 50+ targets)	No (local dev only)	Limited (web focus)	Web only
Team context sharing	Yes (workspace-level AI)	No (per-user)	Limited	No
CLI/SDK/programmatic access	Yes (CLI, Python SDK, MCP)	No (UI only)	Limited	No

What We're Not

We won't replace your IDE (integrate with it instead)
We won't lock your code in (Git integration, export anytime)
We won't work if you need absolute on-premise (cloud-only for now, private cloud on request)
We won't eliminate code review (AI flags for manual approval before merge)

The Actual Difference

Most dev tools pick: simplicity OR power.

We picked consistency. Same AI, same context, same codebase across all platforms. Fewer bugs, faster shipping, less re-learning.

Fleeks. Unified development infrastructure. Platform-agnostic AI agent. Multi-platform deployment from single codebase.

Forem: Fleeks

The Last Infrastructure Problem AI Will Ever Face

Table of Contents

The Wrong Question the Industry Is Asking

The Handoff: From Local Terminal to Cloud Execution

Agents Propose. Humans Approve. Infrastructure Executes.

What Makes Instant Execution Possible

Pre-Warmed Execution Pools

Dynamic Infrastructure Mutation

CRIU-Based Environment Hibernation

Workspace-Scoped Isolation

A Scheduler Built for Agent Workloads

Approval Is Not the Friction. Infrastructure Is.

What Becomes Possible

This Scales Across Every Team Size

The Real Question

Key Takeaways

Stop Waiting. Start Executing.

Install the SDK:

Quick Example:

Links:

[Boost]

The Agentic Substrate: Why the Production Lifecycle Matters for Autonomous Systems.

Fleeks ・ Mar 4

The Agentic Substrate: Why the Production Lifecycle Matters for Autonomous Systems.

Table of Contents

1. Core Infrastructure: Sub-200ms Stateful Execution

2. Orchestration: The MCP Standard for Autonomous Tool Integration

3. The Structural Foundation: The Production Lifecycle

A. Polyglot Runtime Execution

B. Instant Preview URLs

C. Embeds for Distribution

D. Persistent State Architecture

4. Resource Management: CRIU-Based Hibernation

5. Real-World Application: Solving Engineering Friction

Self-Healing Infrastructure

Complete System Architecture

Performance Benchmarks

Infrastructure Comparison

Current Technical Constraints

Resources

Get Started

Benchmark It Yourself

Links

Key Takeaways

You Can't Scale Teams With Fragmented AI (Fleeks Changes That)

The Problem You Know

What The Real Problem Is

What Actually Solves This

How This Changes Your Workflow

Why This Matters

The Hidden Metrics

What Production-Grade Infrastructure Actually Means

When This Problem Becomes Critical

The Question You Need To Ask Right Now

How Fleeks Actually Solves This

What This Means In Practice

The Path Forward

Fleeks: The Universal Development Platform with One Unified AI Agent

Deploy to 50+ Platforms From a Single Codebase

The Problem You Know

What We Built

How It Actually Works

Why This Matters

The Technical Reality

Supported Tech

vs Competitors

What We're Not

The Actual Difference