Deep Dive
The problem OpenAI is solving
Building a production agent requires more than a capable model. You need to wire up tools, track state across interactions, manage context windows, and run secure infrastructure. OpenAI's Agents API moves all that burden onto their platform. The API brings a hosted version of what they call the Codex harness — the orchestration layer that handles sessions, context, and tool coordination so you can write business logic instead of infrastructure.
How it works in practice
The demo walks through an incident investigation agent that needs access to observability data, recent code changes, and runbook instructions. You wire tools through MCPs (Model Context Protocol) and pass operational knowledge as skills. The agent can then process large log files by filtering results in code rather than dumping raw data into the model, conserving tokens for what actually matters. For bigger tasks, you can spin up sub-agents to work in parallel — one on code changes, one on telemetry — then have a lead agent synthesize their findings into a single report.
Built-in efficiency features
Long-running sessions stay efficient through context compaction, which summarizes all prior work so the model can continue without replaying every step. You maintain control over what tools the agent can access and where execution happens — whether through OpenAI's sandbox, a third-party provider, or your own infrastructure. Once work completes, findings are packaged into a shareable report with root cause, supporting evidence, and next steps.