AI Agents

A real agent workflow starts with a narrow job, an explicit list of allowed actions, and a replay loop for bad runs. If a teammate cannot open the transcript and explain why the system acted the way it did, the workflow is still too magical to trust.

The OpenAI Agents SDK and LangGraph are valuable for different reasons: one is great for getting to a clean runtime with guardrails and tracing, and the other is excellent when the team needs graph-shaped control over state. I would choose the tool that makes debugging clearer, not the one with the loudest launch thread. The numbers that matter here are about completion quality and operator burden, not total turns or model cleverness. Good teams look at success on representative jobs, intervention rate on irreversible actions, and how quickly they can explain a bad run to another engineer.

A grounded version usually starts with three moves: Define the narrow job the agent owns and the actions it is allowed to take.; Instrument every tool call so failures are visible before users feel them.; and Review transcripts weekly to tighten prompts, guardrails, and escalation paths.. Save the version that survived real constraints, not the one that only sounded elegant in a planning doc.

Useful operating references:
- 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 specification: modelcontextprotocol.io/specification/2025-06-18
Useful when readers need the actual protocol details instead of summaries.
- OpenAI Agents JS source: github.com/openai/openai-agents-js
Readable source for tool calling, handoffs, tracing, and guardrails.