We've optimized LLM reasoning. We've optimized the prompt. But the moment an agent needs to actually do something, it hits a wall. Here's why, and how to break through it.
By Victor M, Co-Founder at Fleeks
The models got fast. The reasoning got sharp. The prompts got surgical.
And yet, the moment an autonomous agent needs to actually do something (spin up a preview, run a test suite, connect to a live database), everything stops. Not because the AI is wrong. Because the world it's trying to act on isn't built for the speed it thinks at.
We call it the Infrastructure Latency Gap.
If your agent takes 4 seconds to think but 45 seconds to deploy a container to verify its work, the agent isn't autonomous. It's just another thing you're waiting on.
The intelligence is not the bottleneck anymore. The infrastructure is.
This is the problem we set out to solve. And the way we solved it changes not just how agents deploy. It changes what they can become.
Table of Contents
- The Deployment Wall: What It Actually Is
- The Infrastructure Latency Gap by the Numbers
- The Fatal Flaw Shared by Every AI Coding Tool
- Cursor + Fleeks: From Code to Cloud in One Command
- Claude Code + Fleeks: Giving Anthropic's Brain Enterprise Infrastructure
- Aider + Fleeks: The Write → Commit → Deploy Loop
- Windsurf + Fleeks: Publishing at the Speed of Flows
- The Architecture That Closes the Gap
- What Becomes Possible When Execution Catches Up to Thought
- Resources
1. The Deployment Wall: What It Actually Is
Here is a scenario every team building with AI agents has lived through.
You give an agent a task: "Refactor the payments service to handle async retries and deploy a preview."
The agent reasons. It architects. It generates code. All of this takes roughly 8 seconds. Genuinely impressive.
Then it has to verify its work.
And then you wait.
Docker builds. CI queues. Container cold starts. Health checks. DNS propagation. Four minutes later, you have a preview URL. The agent makes another decision. You wait again.
By the time ten iterations are complete, the agent has spent maybe 90 seconds thinking and more than 40 minutes waiting.
This is the Deployment Wall. It is not a failure of the model. It is a failure of the infrastructure the model is trying to work inside.
The core insight: Traditional infrastructure was designed for human-paced workflows. A five-minute deploy pipeline was fast when a human developer needed time to grab coffee, check Slack, and review the diff anyway. But an agent doesn't need that time. An agent is ready to act again the moment it has a result. And every second of infrastructure latency is a second the agent's reasoning sits idle, burning money and momentum.
Production-grade agents require production-grade infrastructure: ephemeral, instant, and as fast as the tokens themselves.
2. The Infrastructure Latency Gap by the Numbers
Before we talk about solutions, let's make the problem concrete.
A typical agentic iteration loop, unoptimized:
| Step | What Happens | Latency |
|---|---|---|
| Agent reasons about task | LLM inference, planning | ~4–8 seconds |
| Code is written to disk | File I/O, context update | ~1–2 seconds |
| Docker image builds |
docker build with layer cache |
~45–120 seconds |
| Container starts | Cold start, port binding | ~5–15 seconds |
| Health check passes | Readiness probe, retry window | ~5–10 seconds |
| Preview URL resolves | DNS + TLS negotiation | ~5–20 seconds |
| Total per iteration | ~65–175 seconds |
Now run ten iterations. That's 18–49 minutes of infrastructure wait for a task that took the model under 2 minutes to reason through.
The same loop on Fleeks:
| Step | What Happens | Latency |
|---|---|---|
| Agent reasons about task | LLM inference, planning | ~4–8 seconds |
| Code is written to live container | Direct file sync | ~0.2 seconds |
| Pre-warmed container picks up changes | No rebuild, snapshot model | ~0.1 seconds |
| HTTPS preview URL is live | Pre-provisioned edge routing | ~0.5 seconds |
| Total per iteration | ~5–9 seconds |
The intelligence didn't change. The infrastructure did.
3. The Fatal Flaw Shared by Every AI Coding Tool
Right now you are probably using one of the major AI coding assistants: Cursor, Claude Code, Aider, or Windsurf. They are extraordinary at generating code, understanding project context, and refactoring logic at a depth that would have taken a senior engineer hours to produce.
But they all share one fatal structural flaw.
They are trapped in your local environment.
When they generate a full-stack application, they hand it back to you. You figure out the Docker containers. You configure the ports. You manage the local dependencies. You string together Model Context Protocol JSON files just to connect a database. You run the tests manually, watch them fail, copy-paste the error back, and wait for the next iteration.
The agent wrote the code in 12 seconds. You spent 20 minutes making it run.
This is not an AI problem. It is a handoff problem. The moment the model finishes thinking and the environment has to respond, the loop collapses.
The way to fix it is not to make the AI smarter. It is to make the environment faster.
Here is exactly how integrating Fleeks into each major coding tool closes that gap.
4. Cursor + Fleeks: From Code to Cloud in One Command
What Cursor does best
Cursor is currently the leading AI-native GUI editor. As a fork of VS Code, it has exceptional context awareness of your open files and terminal. It is unmatched for inline code generation (Cmd+K) and conversational codebase editing (Cmd+L). Cursor understands your project deeply enough to generate multi-file features in a single prompt.
The wall Cursor hits
Cursor is exceptional at writing code. It is terrible at executing it in the real world.
If Cursor writes a Python FastAPI backend and a React frontend, you still have to manually boot both servers locally, manage environment variables, figure out why they can't talk to each other, and then, if you want to share it, figure out how to expose it publicly.
The agent wrote code. A human does DevOps.
The multiplier: Cursor + Fleeks
When you add Fleeks, you close that loop from inside the Cursor terminal.
# Cursor generates your app. You type one command:
fleeks deploy
# Output:
# ? Snapshotting environment...
# ? Building in cloud (pre-warmed)... 180ms
# ? HTTPS preview live:
# ? https://my-api-p42.deploy.fleeks.ai
Fleeks bypasses your local Docker setup entirely. It snapshots the current environment state, builds the containers in the cloud using pre-warmed pools, and returns a live, shareable HTTPS URL directly to your Cursor terminal, before you've finished reading the output.
Your agent went from writing code on your laptop to having a deployed preview in under 200 milliseconds of build time.
Case study: A solo founder building a SaaS dashboard
A developer building a client analytics dashboard for a B2B SaaS was using Cursor to generate data visualization components. Each time they wanted to show a client a progress update, they had to manually run the build locally, use ngrok to expose it, and hope the tunnel didn't drop mid-demo.
After integrating Fleeks: every time Cursor finished a feature, one fleeks deploy gave them a persistent HTTPS URL. They started shipping five client preview links per day instead of one. The feedback loop with clients collapsed from weekly to same-day.
How to integrate
# 1. Open your Cursor terminal
# 2. Install the Fleeks CLI
npm install -g fleeks-cli
# 3. Authenticate
fleeks auth login
# 4. Tell Cursor's AI to use it:
# "Run `fleeks deploy` to push this code and test it live."
5. Claude Code + Fleeks: Giving Anthropic's Brain Enterprise Infrastructure
What Claude Code does best
Claude Code is a command-line tool built directly by Anthropic. It shines in autonomous terminal workflows. Because it runs directly in your CLI, it can execute shell scripts, read file trees, manipulate git history, and orchestrate multi-step engineering tasks with the full reasoning capacity of Claude Sonnet behind it.
It is arguably the most autonomous general-purpose coding agent available today.
The wall Claude Code hits
Claude Code is autonomous in reasoning. But it struggles the moment it needs to talk to real infrastructure.
Connecting to a local PostgreSQL database involves writing configuration, managing connection strings, setting up environment variables, and hoping the local service is actually running. Ask Claude Code to write a database migration and it will write excellent SQL. But then you're the one running psql manually, watching errors scroll by, copying them back.
Autonomous reasoning. Manual infrastructure.
The multiplier: Claude Code + Fleeks
Fleeks has a native MCP ecosystem. Instead of Claude Code trying to construct brittle bash scripts to talk to local services, you give it access to Fleeks' cloud integrations.
# See everything available
fleeks mcp list
# Output:
# 200+ integrations available:
# - postgres Connect to any PostgreSQL database
# - mysql Connect to MySQL / MariaDB
# - redis Redis key-value access
# - github Read/write repos, issues, PRs
# - stripe Payment data and billing management
# - slack Channel messaging and notifications
# - s3 Object storage read/write
# [... 193 more]
# Install one:
fleeks mcp install postgres
Now Claude Code has a standardized, secure interface to your database. It can query schemas, analyze query performance, write migrations, and verify the output, all without you touching a terminal.
# Inside Claude Code session:
# > "Use the Fleeks Postgres MCP to analyze the users table
# and write a migration to add soft deletes."
# Claude Code calls the MCP, inspects the schema, writes:
# ALTER TABLE users ADD COLUMN deleted_at TIMESTAMP;
# CREATE INDEX idx_users_deleted_at ON users(deleted_at) WHERE deleted_at IS NULL;
# Then runs the migration and confirms success.
You gave Anthropic's reasoning engine access to enterprise-grade infrastructure. That is not an incremental improvement. That is a capability unlock.
Case study: Automated schema migration for a startup
An early-stage team had been manually writing and approving database migrations every sprint cycle, a process that took 3 hours per sprint between writing, reviewing, and running. After connecting Claude Code to their staging database via fleeks mcp install postgres, they gave Claude Code the task of auditing all tables for missing indexes. It analyzed the live schema, generated seventeen migration files, ran them in staging, confirmed query performance improved, and opened a PR. Total time: 23 minutes.
How to integrate
# 1. Inside your terminal running Claude Code, install Fleeks
npm install -g fleeks-cli
fleeks auth login
# 2. Browse and add cloud integrations
fleeks mcp list
fleeks mcp install postgres # or any of the 200+ available
# 3. Prompt Claude Code:
# "Use the Fleeks CLI to connect to my database and write a migration script."
6. Aider + Fleeks: The Write → Commit → Deploy Loop
What Aider does best
Aider is the premier open-source CLI agent. Beloved by engineers who live in the terminal, it pairs flawlessly with git. It makes surgical edits to your codebase, understands file dependencies across a project, and automatically commits changes with sensible commit messages.
It is the ultimate pair programmer for engineers who prefer the command line.
The wall Aider hits
Aider edits files brilliantly. It does not run them.
After Aider finishes a refactor and commits it, the deployment and testing loop is entirely your responsibility. You run the tests. You watch for failures. You copy errors back into the Aider session. The feedback cycle is: Aider writes → Human runs → Human reports → Aider writes again.
Remove the human from that loop and you have autonomous engineering. Keep them there and you have a very good autocomplete.
The multiplier: Aider + Fleeks
Fleeks closes the loop Aider leaves open. After every commit, Fleeks can intercept it, provision a cloud container, run your full test suite in isolation, and feed the results back to Aider automatically.
The loop becomes: Aider writes → Fleeks deploys → Tests run → Results feed back → Aider fixes → Repeat.
# Initialize Fleeks in your project directory
fleeks init
# Aider session example:
# You: "Refactor the auth middleware to use JWT RS256."
# Aider: [makes changes, commits]
# After commit, from inside Aider chat:
/run fleeks deploy
# Output fed directly back to Aider:
# ? Deployed: https://auth-service-preview.deploy.fleeks.ai
# Test results:
# PASS tests/auth.test.ts (14 tests)
# FAIL tests/session.test.ts
# Error: RS256 public key path not found in environment
#
# Aider sees the failure and immediately fixes it.
The test runner becomes part of the agentic loop. The agent doesn't wait for a human to report failures. It discovers them, fixes them, and verifies the fix. Automatically.
Case study: A backend engineer refactoring an API layer
A senior engineer used Aider to refactor a legacy REST API. Previously, each refactor session required manually running pytest, reading 300-line test output, identifying the failures, and re-entering the session. After integrating Fleeks, the /run fleeks deploy command ran automatically after each Aider commit. The test suite executed in a clean cloud container and failures were fed back directly. A refactor that would have taken two days of back-and-forth took four hours.
How to integrate
# 1. In the same directory you run Aider, initialize Fleeks
fleeks init
# 2. Run your Aider session normally
# 3. After Aider completes a task, trigger Fleeks from inside the chat:
/run fleeks deploy
# Fleeks runs your tests in the cloud and feeds results back to Aider.
7. Windsurf + Fleeks: Publishing at the Speed of Flows
What Windsurf does best
Windsurf operates on "Flows", an architecture where the AI agent acts simultaneously as a copilot suggesting code and an autonomous agent executing tasks in the background. It is highly optimized for deep context retrieval across large codebases and maintains state across an entire feature development session without losing track of earlier decisions.
For teams building complex products with multiple interconnected modules, Windsurf's context window is its superpower.
The wall Windsurf hits
Windsurf is great at maintaining context. Terrible at publishing.
When you want to share a prototype with a stakeholder or a client, you are stuck taking screenshots or doing screen shares of your localhost. There is no clean path from "Windsurf just built this feature" to "here is a URL anyone can open."
The AI did the work. The delivery mechanism is still 2012.
The multiplier: Windsurf + Fleeks
Fleeks becomes Windsurf's publishing engine.
Because Fleeks provides instant cloud deployment with pre-provisioned HTTPS and CDN routing, you can instruct Windsurf's agent to treat fleeks deploy as a first-class step in every flow.
# Windsurf integrated terminal setup
fleeks auth login
# Instruct the Windsurf agent:
# "Build a real-time analytics dashboard for the marketing team,
# then use `fleeks deploy` to generate a shareable preview link."
# Windsurf builds the feature.
# Windsurf triggers: fleeks deploy
# Output:
# ? Preview live: https://analytics-dashboard-p91.deploy.fleeks.ai
# ? Share this URL directly with stakeholders.
Windsurf writes the code. Fleeks publishes it. Your stakeholder gets a real URL: not a screenshot, not a screen share, not a Loom. A live interactive application they can click through.
Case study: A product team delivering daily previews to clients
A product agency building client dashboards used Windsurf to generate and iterate on data visualization features. Client review cycles were slow because sharing progress meant either scheduling a screen share or exporting static screenshots. After integrating Fleeks, the agency instructed Windsurf to automatically run fleeks deploy at the end of every flow. Clients received a new preview URL every morning with the latest build. Revision cycles dropped from weekly to daily. One client closed a contract renewal early after being able to see their dashboard evolving in real time.
How to integrate
# 1. Open the Windsurf integrated terminal
# 2. Authenticate Fleeks
fleeks auth login
# 3. Instruct the Windsurf agent:
# "When you finish building this feature, use `fleeks deploy`
# to generate a preview link for the team."
8. The Architecture That Closes the Gap
The integrations above work because Fleeks is not a deployment tool bolted onto an agent workflow. It is a runtime built from first principles to eliminate the gap between agent thought and system reality.
Three architectural decisions make this possible.
Pre-Warmed Container Pools
Containers do not spin up when you request them. They are already running.
Fleeks maintains pre-warmed container pools across regions. When an agent or CLI command requests an environment, it is grabbed from the pool in under 200 milliseconds. No build. No cold start. No queue.
from fleeks_sdk import FleeksClient
client = FleeksClient(api_key="fleeks_sk_...")
# Ready before you finish reading this line
workspace = await client.workspaces.create(
project_id="my-api",
template="fastapi"
)
health = await workspace.get_health()
print(f"Status: {health.status}") # running
print(f"Time: {health.startup_ms}ms") # <200
Pool performance under production load:
| Metric | Value |
|---|---|
| Pool size | 1,000+ containers per region |
| Pool hit rate | >95% under production load |
| Container startup (pool hit) | Sub-200ms (P95) |
| Container startup (cold provision) | 4–5 seconds |
| Isolation model | gVisor per container |
CRIU-Based Environment Hibernation
Agents work in bursts. They reason, act, then wait for feedback before the next cycle. Most infrastructure tears down the environment between cycles, forcing a full rebuild on the next iteration.
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 from exactly where it left off.
# Hibernate mid-task to preserve compute budget
await workspace.hibernate()
# Resume later: full state, zero rebuild
await workspace.resume()
Live Infrastructure Mutation
Most platforms redeploy a service to change its runtime configuration. Fleeks applies memory, concurrency, and routing changes directly to a running container through the runtime scheduler, without triggering a new deployment. The service keeps running. The change just applies.
For agents doing frequent infrastructure adjustments, this eliminates an entire class of wait.
9. What Becomes Possible When Execution Catches Up to Thought
When you eliminate the Deployment Wall, the nature of what agents can do changes fundamentally.
Before: Agents assist. You ship.
You use the AI to write the code. You run the deploy. You check the tests. You feed back errors. You are the nervous system connecting the agent's intelligence to the infrastructure.
After: Agents execute. You approve.
The agent writes the code. Fleeks runs it. The test suite runs in an isolated cloud container. Failures feed back into the agent's next iteration automatically. By the time you look at the task, there is a live preview URL, a passing test suite, and a proposed PR. Not a pile of generated files waiting for you to figure out how to run them.
This is not a better tool. This is a different relationship with infrastructure entirely.
The teams already building on Fleeks describe the same shift:
- A two-person team operating with the deployment throughput of a ten-person platform team
- A solo founder shipping five reviewable client previews per day instead of one
- An agency turning client feedback cycles from weekly to daily without adding headcount
- An engineering team where the hardest parts of a sprint are making product decisions, not environment management
The intelligence was always there. The infrastructure was the ceiling. Fleeks removes the ceiling.
Your AI assistant is the brain. Fleeks is the muscle. Don't let your agent get bogged down in local DevOps hell. Give it the infrastructure it needs to actually build, run, and ship at the speed of thought.
Resources
- Fleeks - Open a workspace free, no setup required.
- Fleeks Introduction - What is Fleeks and what does it do?
-
CLI Documentation - Full reference for
fleeks deploy,fleeks mcp,fleeks init - MCP Integrations - Browse 200+ tool integrations available to your agents
- SDK Documentation - Programmatic access via Python, TypeScript, Go
- AI Agent Deep Dive - How the 7-mode unified agent works under the hood
Top comments (1)
Wanted to add some context on why we built this the way we did.
The 45-second container problem isn't just a DX annoyance. It's a compounding tax. Every agent iteration that has to wait on infrastructure is an iteration that costs more, runs slower, and trains teams to distrust autonomous workflows.
We kept asking: what would the loop look like if the environment moved as fast as the tokens? That question drove every architectural decision behind Fleeks.
If you're currently using Cursor, Claude Code, Aider, or Windsurf and hitting this wall, I'd genuinely love to hear where it's breaking for you. Drop it below.
And if you want to try it: fleeks.ai - no setup, workspace ready in under a second.