Building a Production Agent with the OpenAI Agents SDK

Begin with one job and one completion definition

An agent needs a bounded job, approved inputs, a small tool set, and a definition of done. Write the evaluation cases before adding handoffs. If a single agent with two narrow tools can complete the task, a multi-agent graph adds cost and failure modes without adding value.

Design tools as application boundaries

The Agents SDK manages the model loop, tool calls, guardrails, sessions, and optional handoffs. Your application still owns authorization, input validation, idempotency, timeouts, retries, and the business effect of each tool.

• Use typed schemas and reject identifiers or values the user is not allowed to use.
• Separate read tools from write tools and require approval for consequential changes.
• Return structured errors that let the agent retry safely or escalate to a person.
• Prefer an agent-as-tool when a specialist should return control; use a handoff when the specialist should take over the conversation.

Trace the workflow without over-collecting data

Built-in tracing can record model generations, tool calls, handoffs, guardrails, and custom events. That is essential for debugging and evaluation, but traces may contain sensitive inputs and outputs. Decide what to capture, redact, retain, and restrict before production traffic arrives.

Release through regression tests and reviewed traces

Test successful tasks, missing permissions, ambiguous requests, tool failures, duplicate operations, prompt injection, and escalation. Compare prompt, model, and tool changes against the same versioned task set, then review sampled production traces to discover failures the offline set missed.