At a glance
WHAT IT’S REALLY ABOUT
Demystifying AI harnesses: build a bug-triage agent around Claude SDK
- A harness is simply code wrapped around an AI agent to make it more effective for a specific job by enforcing context, allowed actions, and expected outcomes.
- Harnesses are most valuable when a workflow repeats with consistent setup and deliverables, especially when you want a mix of deterministic steps and non-deterministic agent reasoning.
- Claire’s example harness targets Sentry bug triage at ChatPRD, integrating Sentry/Vercel/Linear/GitHub and producing consistent investigation artifacts instead of relying on ad-hoc prompting.
- Compared to using Claude Code or Codex directly, the custom harness increases speed and reliability by encoding intent, permissions (e.g., investigate-only vs fix), tool policies, and output formats.
- The build approach uses an Agent SDK for agentic planning plus opinionated API adapters and an artifact store, and was iterated with help from both Codex and Claude despite initial “too-deterministic” codegen tendencies.
IDEAS WORTH REMEMBERING
5 ideasA harness is “structure around agency,” not a mysterious new model trick.
Claire frames a harness as straightforward wrapper code that supplies specific context, constrains actions, and standardizes outcomes so an agent performs reliably for one job-to-be-done.
Build a harness when you’re repeating the same workflow and deliverables.
If a task needs the same setup, tools, and outputs each time (and has both deterministic steps and open-ended reasoning), encoding it into a harness reduces re-explaining and babysitting.
Encode intent and permissions so you don’t have to prompt for them every run.
Her TUI supports explicit modes (e.g., “Investigate” vs “Fix”), preventing file edits unless allowed—control that would otherwise rely on fragile, repeat prompting.
Opinionated tool adapters can outperform general tool access for a narrow job.
Instead of letting an agent “wander” via generic MCP calls, she builds Sentry/Linear/Vercel/GitHub adapters that fetch only the bug-triage-relevant fields and actions, improving consistency and speed.
Artifacts turn one-off agent work into reusable organizational memory.
Each run saves evidence, summaries, and a structured report (including an HTML view) to a file-based artifact store, making investigations auditable and useful for future debugging.
WORDS WORTH SAVING
5 quotesA harness is some code around an AI agent that makes it more effective.
— Claire Vo
Everybody's saying, "It's not the model, it's the harness," but you know what not everybody is saying? What is a harness?
— Claire Vo
It is just writing more code around your AI to make it more useful for a specific use case.
— Claire Vo
So the harness is the whole experience, including the human experience that makes it more useful and easier to use.
— Claire Vo
I've realized that these agents can help us solve very, very specific problems using other agents, and by constraining that work, we can actually get specific jobs done really efficiently, and then use the general purpose agent to sort of orchestrate it.
— Claire Vo
High quality AI-generated summary created from speaker-labeled transcript.
