A pre-scale review for AI agents before expanding the scope

Before scaling an agent system, I want to see evidence that the team can replay failures, constrain tools, and prove that the automated path beats a careful human baseline on at least one meaningful workflow. If that evidence is still fuzzy, more surface area usually makes the system worse, not better.

Three evaluation axes to compare:
- reliability under messy real-world inputs
- cost per completed task and retry pattern
- clarity of escalation when confidence drops

Review materials:
- Model Context Protocol introduction: modelcontextprotocol.io/introduction
Worth reading so tool access and context plumbing stop feeling hand-wavy.
- OpenAI agent guide: platform.openai.com/docs/guides/agents
A practical guide to agents, tools, handoffs, and traces from the product side.
- OpenAI Agents JS source: github.com/openai/openai-agents-js
Readable source for tool calling, handoffs, tracing, and guardrails.

Save the strongest examples, scorecards, and decision memos in this folio so future teammates can see what good evaluation looked like at the time.