Case Study: Coding Agent
A production case study for a coding agent that reads repos, plans edits, runs tools, and validates changes before handoff.
Prerequisites
- 1Comfort reading code and thinking in repo-level workflows
- 2Recommended: Tool Use, Workflow Reliability, and Evaluation coverage
What you will learn
- How coding agents combine search, planning, editing, and verification
- Where sandboxing, test execution, and human review matter most
- Why repo context and state management dominate coding-agent quality
- What failure patterns are specific to code-editing systems
Problem Shape
A coding agent is not just a text generator for code snippets. It is a system that must understand repository state, make coordinated edits, run tools, and verify that the change is actually safe.
Architecture
A practical coding-agent loop looks like this:
issue or request
-> search codebase
-> form change plan
-> edit files
-> run tests/lints/build
-> summarize diff and hand off
The system is strong when every step is observable. Search results, chosen files, edits, and verification outputs should all be inspectable.
Operational Controls
Coding agents need controls different from chat systems:
- workspace isolation and file-scope awareness
- non-destructive defaults for existing changes
- structured verification through tests, typecheck, and build commands
- explicit review output that explains what changed and what remains risky
Useful metrics include verified-change rate, test pass rate, rollback rate, and number of human follow-up edits required.
Failure Modes
Common coding-agent failures include:
- editing the wrong file because search context was shallow
- passing compilation while breaking behavior
- overwriting unrelated in-progress work
- fixing one issue by creating untested regressions elsewhere
That is why coding agents should be judged on verified repo outcomes, not just plausible-looking diffs.
Common Mistakes to Avoid
- !Treating code generation as the whole problem instead of repo understanding plus verification
- !Skipping tests because the diff looks small or obvious
- !Letting the agent overwrite unrelated work in a dirty tree
- !Using one giant prompt instead of explicit search, edit, and verify stages
- !Evaluating snippets instead of full change outcomes
Recommended Next Steps
Explore Related Content
Tool Use & Function Calling
How agents interact with external tools, APIs, and services to take action in the real world.
ConceptWorkflow Reliability
Retries, idempotency, fallback chains, and bounded autonomy for reliable multi-step agent workflows.
GuideProduction Deployment
Ship agents to production with proper architecture, containerization, scaling, cost optimization, and reliability.