agents-sdk-production-workflow

name	agents-sdk-production-workflow
description	Use this skill when designing, reviewing, debugging, or productionizing OpenAI Agents SDK, Responses API, or similar agent systems with tools, handoffs, guardrails, tracing, evals, human approval, retries, and deployment safety.

Agents SDK Production Workflow

Build agent systems as explicit workflows with observable state, typed boundaries, and human review for risky actions.

Workflow

1. Define the user outcome, risk level, and allowed autonomous actions.
2. Choose orchestration shape: single agent, deterministic pipeline, supervisor, handoff graph, or planner/executor/reviewer.
3. Define every tool contract with typed inputs, least privilege, timeouts, idempotency, and redacted logs.
4. Add guardrails before irreversible actions: input validation, output schema checks, policy checks, confirmation gates, and rate limits.
5. Add tracing around model calls, tool calls, handoffs, retries, errors, cost, latency, and user-visible decisions.
6. Build evals from real tasks: golden cases, adversarial cases, tool failure cases, and regression thresholds.
7. Add fallback behavior: no-op, partial result, queue for human review, or deterministic implementation.
8. Validate locally with mocked tools before touching live APIs, billing, production data, emails, trades, or deployments.

Design Checklist

• Prefer deterministic code for parsing, routing, validation, and data transforms.
• Keep model prompts small and task-specific; move durable policy into code or skills.
• Use structured outputs for decisions that drive tools or persistence.
• Separate planner, executor, verifier, and user-communication responsibilities when a task spans multiple steps.
• Treat handoffs as typed state transitions, not vague "ask another agent" prompts.
• Store only necessary traces; redact secrets, tokens, cookies, PII, and regulated data.
• Add budget caps for tokens, wall time, retries, tool calls, and paid API actions.
• Version prompts, tool schemas, eval datasets, and production config together.

Review Questions

• What can the agent do without human approval?
• What happens when a tool returns stale, partial, malicious, or malformed data?
• Which actions are irreversible or expensive?
• Which eval would fail if the next model release changes behavior?
• Can a production incident be reconstructed from traces without exposing secrets?

Guardrails

• Do not let an LLM decide legal, medical, financial, hiring, trading, security, or compliance outcomes without human review.
• Do not grant broad filesystem, network, database, email, deployment, billing, or admin tools when narrow tools are enough.
• Do not log raw prompts or tool payloads when they may contain secrets or personal data.
• Do not deploy an agent workflow without tests for tool failures, prompt injection, and schema violations.