AI Coding Agents: Deploy a Secure Dev Agent Team

Autocomplete suggests the next line. AI coding agents finish the ticket. They are autonomous agents that read an issue, plan the work, edit files across a repo, run the test suite, fix what breaks, and open a pull request - while you do something else. OpenLegion runs them the way you would run any code that executes untrusted commands: each agent in its own container, with credentials it never directly holds.

What is an AI coding agent?

An AI coding agent is an autonomous AI system that takes a software task, plans the steps, writes and edits code, runs tests and tools, and iterates toward a working result - operating across many steps rather than completing a single line or function on request.

TL;DR

• AI coding agents do software work end to end: plan, write, test, review, and open a pull request. Not just suggest the next line.
• A coding assistant augments a human in the editor. A coding agent runs the loop on its own toward a defined task.
• The exciting capability and the dangerous one are the same: executing code and holding repo credentials. Isolation is not optional.
• OpenLegion's Dev Team template ships a PM, an Engineer, and a Reviewer agent, each in its own container with its own budget.
• It is self-hostable under BSL 1.1, so coding agents can run inside your own network for private repositories.

What a Coding Agent Actually Does

The word "agent" raises the bar past suggestion. A coding agent owns a whole task, not a single completion. A typical run reads like a junior engineer's afternoon:

• Read the issue or request and restate the goal in its own words.
• Explore the repository to find the files that matter and learn the surrounding context.
• Plan the change as a sequence of edits and checks.
• Write and modify code across multiple files.
• Run the build and tests, read the failures, and fix them.
• Open a pull request with a description, or hand the diff to a reviewer agent.

The language model supplies judgment at each step; the agent loop and tool access turn that judgment into committed work. For the mechanics of that loop, see what is an AI agent.

Why Isolation Is the Whole Story

Here is the uncomfortable truth the "autonomous engineer" demos skip: an agent that executes code is, by definition, running untrusted commands on a machine, and an agent that pushes to your repo is holding your most sensitive credentials. That is precisely the workload you would never run unsandboxed if a human wrote it.

When coding agents share a host or a process, the failure modes are not hypothetical:

• A generated command wipes files outside the intended workspace.
• A prompt-injected dependency or README convinces the agent to exfiltrate secrets.
• One agent's runaway loop starves another of CPU and memory.
• Repo tokens and cloud keys sit in plain environment variables the agent can simply read.

OpenLegion closes each of these structurally. Every coding agent runs in its own Docker container with resource caps, a non-root user, and a read-only base filesystem. Repository tokens and API keys live in a vault proxy on the trusted host and are injected at the network layer, so a compromised agent never sees the raw credential. The full model is laid out in AI agent security.

Multi-Agent Coding: The Dev Team Template

One agent is fine for a small fix. Bigger work benefits from the same thing human teams rely on: separation of concerns. OpenLegion's Dev Team template runs three agents that each own a job:

• PM agent turns a request into scoped tasks with acceptance criteria.
• Engineer agent implements each task, writing code and running tests.
• Reviewer agent checks the diff against the criteria, then approves or sends it back.

They coordinate through OpenLegion's orchestration model - a shared blackboard, a pub/sub event bus, and a structured handoff - with no language model sitting in the control plane deciding who does what. Each agent gets its own container, budget, and permissions, so the Engineer can execute code while the Reviewer cannot, and neither can blow past its spend ceiling.

Coding Agents vs Coding Assistants

The two get conflated constantly, and they solve different problems.

Aspect	AI coding assistant	AI coding agent
Where it runs	Inside your editor	As an autonomous process
Human role	Drives every keystroke	Sets the goal, reviews the result
Scope	Line and function completion	Whole task: plan, edit, test, PR
Executes code	No	Yes, in a sandbox
Holds credentials	Editor session	Vault-proxied, agent never sees raw keys
Best for	Making a developer faster	Taking a whole task off the plate

An assistant makes you faster. An agent does a unit of work while you are busy with another. Most teams will run both, and the smart ones know which job to hand to which.

OpenLegion's Take

The race to ship "autonomous software engineers" keeps burying the unglamorous truth: the dangerous capability is code execution, and the dangerous asset is your repository credentials. An agent that can run shell commands and push to your repo is a production security boundary wearing a productivity costume. Treat it like a gadget - shared host, keys in environment variables, no budget ceiling - and a helpful agent becomes an incident report. Our position is simple: coding agents belong in the same isolation and credential model as any untrusted workload, and that should be the default, self-hostable, and auditable, not a feature you upgrade to buy.

Frequently Asked Questions

What is an AI coding agent?

An AI coding agent is an autonomous system that performs software tasks end to end. Given an issue or request, it plans the work, explores the codebase, writes and edits code across files, runs tests, fixes failures, and opens a pull request, iterating on its own instead of completing a single suggestion. A large language model supplies the reasoning, and the agent loop plus tool access turn that reasoning into committed changes.

How are AI coding agents different from coding assistants like autocomplete?

A coding assistant works inside your editor and speeds up a developer who is driving every keystroke. A coding agent runs as an autonomous process: you give it a task and review the result while it plans, edits, executes, and tests on its own. Assistants augment a human in the loop; agents take a whole task out of the loop.

Is it safe to let an AI agent execute code and access my repository?

Only with proper isolation. An agent that runs code is executing untrusted commands, and one that pushes to your repo holds sensitive credentials. OpenLegion runs each coding agent in its own container with resource limits and a read-only base filesystem, and keeps repository tokens and API keys in a vault proxy the agent never directly accesses. That containment is what makes autonomous code execution acceptable in production.

Can AI coding agents work as a team rather than one agent?

Yes. OpenLegion's Dev Team template runs a PM agent, an Engineer agent, and a Reviewer agent that coordinate through a shared blackboard and a structured handoff. Each has its own container, budget, and permission set, so responsibilities and risk stay separated: the Engineer runs code, the Reviewer checks the diff, and neither can exceed its spending limit.

Can I run AI coding agents on my own infrastructure?

Yes. OpenLegion is source-available under BSL 1.1 and runs on a single machine with Python and Docker, so coding agents can operate entirely inside your own network. That matters for private repositories and regulated environments where code and credentials cannot leave your infrastructure. A managed hosting option exists for teams that prefer not to run it themselves.

What does it cost to run AI coding agents?

OpenLegion charges a flat platform fee with no markup on model usage. You bring your own LLM API keys or use included credits and pay providers at their published rates. Per-agent daily and monthly budget caps stop a stuck agent from running up an unbounded bill. Managed plans start at $19/month with a 7-day money-back guarantee, and self-hosting the engine is available under BSL 1.1.