AGENTS.md for an AI meeting summarizer
Turns a meeting recording or transcript into a summary, decisions and action items. Paste this section into AGENTS.md or CLAUDE.md, and Claude Code, Cursor or Codex will build the AI parts so you can see which results users had to redo, and fix those first.
## AI features: AI meeting summarizer Turns a meeting recording or transcript into a summary, decisions and action items. ### Steps 1. **transcribe**: turn the recording into text with speakers 2. **summarize**: write the summary, decisions and action items ### Every AI call - Call models only from the server. API keys never reach the browser. - Give every AI result a row in `ai_results`: id, user_id, feature, step, model, prompt_version, settings, input reference, output reference, cost_usd, latency_ms, created_at. - Never overwrite a result. A retry or regeneration is a new row with `retry_of` set to the earlier result's id. - Prompts live in versioned files (`prompts/<feature>/<step>/v<N>.md`). Don't edit a version that has shipped; add a new one. ### What counts as "worked first time" in this app Record these as rows in `ai_events` (result_id, user_id, event, created_at), written by the server, not by a browser analytics script. Worked: - the summary was shared or the action items were assigned without edits Needed a redo: - the user edited the summary (store which part) - they regenerated it - they deleted an action item or added a missed one Where users give no signal: Each week, compare 10 summaries with their transcripts and record anything missing or wrong. ### Before changing a prompt or a model 1. Build a test set from the last 100 real transcripts, with the summaries people finally shared. 2. Replay it on the change. Compare how often results work first time and the cost per result that works, step by step. 3. Ship to 10% of users behind a flag. Keep it only if users redo less and the cost per result that works doesn't rise. 4. Write down what changed and why in `prompts/CHANGELOG.md`. ### Cost guardrails - A spending cap per user per day, checked before each AI call. - Rate limits on every AI endpoint. - Retries capped at 2, each with a timeout. - at most 3 hours of audio per meeting - a monthly minutes cap per workspace
What each part is for
- Every AI call
- One row per result, never overwritten, tied to its prompt version and model. Without it, nobody can say which change broke what.
- What counts as worked first time
- The signals an meeting summarizer gives when a result worked or needed a redo, recorded by the server. This is the number to improve.
- Before changing a prompt or a model
- Replay real requests first, then ship to a slice of users. Your agent stops changing prompts by feel.
- Cost guardrails
- Caps and limits so one heavy user or a retry loop can't run up the bill.
Run the AI launch checklistWork out your real cost per result
Questions
Where do I put this?
In AGENTS.md at the root of your repo; Codex, Cursor and most coding agents read it. Claude Code reads CLAUDE.md, so paste it there too, or have CLAUDE.md point to AGENTS.md.
Why does it say what counts as a redo for an meeting summarizer?
Because that's how you find out whether the AI worked without asking users to rate it. Each kind of app shows a failed result differently: asking again, editing, regenerating, overriding. Recording those as events, tied to the result's prompt version and model, is what lets your agent fix the cases that fail and prove the fix worked.
Do I need evals for this?
Not to start. Most of it runs on what users already do. Each week, compare 10 summaries with their transcripts and record anything missing or wrong.