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AI Agents
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A working approach to AI agents, from first signal to repeatable practice
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.
A sequence I would actually hand to a teammate:
1. Define the narrow job the agent owns and the actions it is allowed to take.
2. Instrument every tool call so failures are visible before users feel them.
3. Review transcripts weekly to tighten prompts, guardrails, and escalation paths.
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.
- OpenAI Agents JS source: github.com/openai/openai-agents-js
Readable source for tool calling, handoffs, tracing, and guardrails.
If your team has a better workflow, post it with the context around team size, constraints, and exactly where the process tends to break.
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