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Coding Agent CI Feedback Loop

Problem

When a coding agent tackles multi-file refactors or feature additions, running tests and waiting for test feedback synchronously ties up compute and prevents the agent from working on parallel tasks. The agent cannot easily improve code if it must halt until the entire suite finishes.

  • Traditional CI loops block further edits; the agent "babysits" the build until tests pass.
  • Long test suites introduce idle periods, leading to underutilized GPUs and inflated RL training times.

Solution

Run the coding agent asynchronously against CI (local or remote), allowing it to:

1. Push a Branch & Trigger Tests - When the agent proposes a patch, it commits to a branch and triggers the CI pipeline (e.g., git push && github_action_run).

2. Ingest Partial CI Feedback - As tests begin, the agent periodically polls CI results. - Failed Tests Partial Report: Receive a small subset of failures (e.g., 10% of failures flagged first).

3. Iterative Patch Refinement - Use test failure outputs (stack traces, error messages) as machine-readable feedback. - Agent autonomously applies fixes to specific files or functions without human intervention.

4. Ping on Final Green - When all tests pass, send a notification (e.g., chat or pull request comment) that the PR is ready for review.

Example

sequenceDiagram Agent->>GitRepo: create branch coding-agent-refactor Agent->>CI: trigger tests remotely loop every 30s CI-->>Agent: partial failures or success chunk Agent->>Files: patch specific failures Agent->>CI: re-run only failing tests end CI-->>Agent: ✅ all tests green Agent-->>User: "PR is ready to merge"

How to use it

  • CI Integration: Provide the agent with a CLI or API key to push branches and trigger tests (e.g., via GitHub Actions or Jenkins).
  • Error Parsing Modules: Implement a small parser that translates CI logs into structured diagnostics (e.g., {file: "auth.py", line: 42, error: "Expected status 200"}).
  • Prioritized Test Runs: When re-running, only run tests in files that were patched, to reduce CI time.

Trade-offs

  • Pros:
  • Compute Efficiency: Overlaps code generation and test runs across multiple agents or branches.
  • Faster Iteration: Agent spends less time waiting and more time generating code.
  • Autonomy: Reduces need for human intervention until final green.
  • Cons/Considerations:
  • CI Flakiness: Intermittent test failures can mislead the agent into unnecessary patches unless flakiness detection is in place.
  • Security: Agent requires permission to push and read CI logs, which may expose sensitive data if misconfigured.

References

  • Inspired by "Background Agent with CI Feedback" pattern, adapted for coding-specific workflows.
  • Will Brown's emphasis on asynchronous pipelines to avoid idle compute bubbles.