How AI Coding Agents Work: A Technical Deep Dive

Autonomous coding agents are the most powerful development tool since version control. But how do they actually work under the hood?

The Agent Loop

At its core, a coding agent runs in a loop:

1. Read the task — Parse the user story, acceptance criteria, and codebase context

2. Plan the approach — Decide which files to create/modify

3. Write code — Generate implementation using an LLM

4. Test — Run tests or verify the code compiles

5. Iterate — Fix issues found in step 4

6. Report — Mark the story complete and submit for review

The Model Context Protocol (MCP)

MCP is an open protocol that lets AI tools communicate with external systems. In Codepylot, the agent uses MCP to:

• Read stories — Get the full story details, acceptance criteria, and dependencies
• Update status — Move the story from TODO → IN_PROGRESS → REVIEW
• Add notes — Log progress and blockers
• Complete stories — Mark as done with a commit hash and summary

This bidirectional communication means the agent isn't just writing code in isolation — it's participating in the project workflow.

Branch Management

When an agent picks up a story:

1. Checks out the main branch and pulls latest

2. Creates a feature branch: feat/SF-001-login-page

3. Makes commits with conventional prefixes: feat: implement login page [SF-001]

4. The branch is ready for review when the agent completes

Concurrency and Queue Management

Codepylot supports up to 5 concurrent agents per project. The queue system:

• Prioritizes stories: CRITICAL → HIGH → MEDIUM → LOW
• Respects dependencies (blocked stories are skipped)
• Atomically claims agent slots to prevent race conditions
• Automatically processes the next story when an agent finishes

Safety and Review

Every agent output goes through review before merging:

1. AI Code Review — Automated scoring (0-100) with issue-by-issue breakdown

2. Diff Viewer — See exactly what the agent changed

3. Deploy Preview — Test the changes on a live preview server

4. Human Approval — You approve, request changes, or revert

This safety layer means agents can work autonomously without risking your codebase.