Production agents need bounded permissions, durable state, idempotent writes, regression evaluations, full tracing and a fast way to stop them.
Treat the agent as a software system
A working agent demo proves that a model can select tools and produce plausible actions in a constrained setting. Production means those actions are observable, authorized, repeatable when necessary, recoverable on failure, and limited to the scope of the user’s intent.
Define the work and success criteria
Specify which tasks the agent may perform, where it must stop, which systems it can change, and what constitutes successful completion. Include cases where the correct answer is to refuse, escalate, or ask a human to confirm an action. Set measurable acceptance thresholds rather than relying on anecdotal demos.
Bound tool access and authority
Use least-privilege credentials for every integration. Validate tool parameters on the server, separate read and write actions, and require human approval for high-impact operations. Log meaningful tool requests and results while minimizing sensitive content in telemetry.
Plan state, retries, and recovery
Agents cross service boundaries, so duplicate calls and partial failures are ordinary engineering problems. Apply idempotency keys for write operations, persist durable workflow state, decide which errors are retryable, and create explicit dead-letter or human recovery paths.
Evaluate outcomes and regressions
Create a representative evaluation set containing common tasks, ambiguous inputs, missing permissions, tool outages, malformed data, and adversarial content. Measure end-to-end task success as well as tool selection, error rate, cost, latency, and human escalation. Run regression evaluations when changing prompts, models, tools, or retrieval behavior.
Observe the full execution path
Trace the user request, model decisions, retrieved context, tool invocation, external API responses, and final outcome with a sensible retention and redaction policy. Dashboards should help an on-call engineer identify where a task failed and whether a side effect occurred.
Make shutdown and rollback operational
Provide ways to pause risky tool actions, revoke credentials, roll back deployments, and recover pending work. Keep clear owners for incidents, access controls, and business escalation. An agent should be easier to stop than the workflow it replaces.
Frequently asked questions
Is a LangGraph or LangChain implementation production-ready by default?+
No. Frameworks help with orchestration but do not automatically deliver authorization, failure recovery, auditability, security, or operational evaluation.
Should every AI action require human approval?+
Not necessarily. Use risk-based boundaries. Low-impact read-only actions may run automatically, while sensitive or irreversible writes often require stronger approval and authorization.
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