AI Agents

The most useful agent writing right now is surprisingly unflashy. The serious teams are writing down tool permissions, handoff rules, and trace review habits because that is where production reliability shows up long before the marketing language catches up.

Three signals I would keep in view:
- Separate orchestration from the underlying model so systems can evolve without a full rewrite.
- Start with human review around high-risk actions before chasing full autonomy.
- Treat memory and retrieval as explicit product decisions, not default checkboxes.

Read first:
- OpenAI Agents SDK for JavaScript: openai.github.io/openai-agents-js/
A clean look at agents, handoffs, guardrails, and tracing in one place.
- OpenAI Agents SDK for Python: openai.github.io/openai-agents-python/
Useful when your team wants the same concepts with more backend-heavy examples.

Documents worth saving:
- OpenAI agent guide: platform.openai.com/docs/guides/agents
A practical guide to agents, tools, handoffs, and traces from the product side.
- Model Context Protocol specification: modelcontextprotocol.io/specification/2025-06-18
Useful when readers need the actual protocol details instead of summaries.

Watch next:
- OpenAI video archive: youtube.com/@OpenAI/videos
Talks and demos are a fast way to compare patterns before you commit to one runtime.

If this post is useful, the next contribution should add a real example, a worked document, or a failure case someone else can learn from.