The quiet mistakes that slow people down in AI agents

The loudest failure mode is calling any multi-step prompt an agent and then discovering too late that nobody scoped the tool contract. The quieter one is letting memory, retrieval, and escalation defaults accrete into the system without someone owning them explicitly.

Common traps to watch:
- calling a single prompt chain an agent without defining real responsibilities
- letting the model discover tools that were never scoped or permissioned
- measuring demo fluency instead of production reliability

References that help correct the drift:
- OpenAI Agents SDK for Python: openai.github.io/openai-agents-python/
Useful when your team wants the same concepts with more backend-heavy examples.
- Model Context Protocol examples: modelcontextprotocol.io/examples
Reference implementations and diagrams that make the tool boundary more concrete.

This folio post is meant to be saved and revised. Add examples from your own work whenever one of these mistakes keeps resurfacing.