AI Agents

Before scaling an agent system, I want to see evidence that the team can replay failures, constrain tools, and prove that the automated path beats a careful human baseline on at least one meaningful workflow. If that evidence is still fuzzy, more surface area usually makes the system worse, not better.

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. Before scaling an agent system, I want to see evidence that the team can replay failures, constrain tools, and prove that the automated path beats a careful human baseline on at least one meaningful workflow. If that evidence is still fuzzy, more surface area usually makes the system worse, not better.

The clearest signals usually live in reliability under messy real-world inputs, cost per completed task and retry pattern, and clarity of escalation when confidence drops. A good archive helps future-you compare decisions over time instead of restarting each month from a vague sense that things are improving.

Keep these nearby while you evaluate:
- Model Context Protocol introduction: modelcontextprotocol.io/introduction
Worth reading so tool access and context plumbing stop feeling hand-wavy.
- 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.