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.

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. 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.

If you want a cleaner start, build your notes around ai-agents, agent-workflows, and the real examples behind separate orchestration from the underlying model so systems can evolve without a full rewrite.. Those records will outlast the summary you write about them later.

Open alongside this question:
- OpenAI Agents SDK for JavaScript: openai.github.io/openai-agents-js/
A clean look at agents, handoffs, guardrails, and tracing in one place.
- OpenAI agent guide: platform.openai.com/docs/guides/agents
A practical guide to agents, tools, handoffs, and traces from the product side.
- OpenAI video archive: youtube.com/@OpenAI/videos
Talks and demos are a fast way to compare patterns before you commit to one runtime.