Deploying Your Agent

Deployment is where most agents break. Local demos hide network latency, retries, and noisy inputs. Production requires operational discipline: queues, monitoring, and clear rollback paths.

Choose the runtime shape

For beginner deployments, use a server route or worker that receives a job payload and returns a structured result. Keep the interface stable from day one.

type AgentJob = {
  jobId: string;
  objective: string;
  payload: Record<string, unknown>;
  requestedBy: string;
};

type AgentJobResult = {
  jobId: string;
  status: 'success' | 'failed' | 'partial';
  output?: unknown;
  errorCode?: string;
  stepCount: number;
  durationMs: number;
};

Add observability first

Log at the step level, not just request level. You need to know which decision failed, which tool timed out, and how many retries happened.

function logStep(jobId: string, step: number, event: string, data: Record<string, unknown>) {
  console.info(JSON.stringify({
    ts: new Date().toISOString(),
    jobId,
    step,
    event,
    ...data,
  }));
}

Track these metrics from launch day:

• Success rate per objective
• Average steps per run
• Error rate by tool
• P95 runtime
• Tokens and cost per successful job

These numbers make stakeholder updates honest and easy to prioritize.

Safe deployment checklist

• Environment variables scoped to server only
• Hard step/token/time budgets enforced in runtime
• Idempotency keys on retried jobs
• Rate limits on public endpoints
• Feature flag to disable write actions fast

Rollout strategy

Deploy in phases:

1. Internal users only
2. Small beta cohort
3. Full release with alerting

At each phase, review logs weekly and tighten tool schemas when errors repeat.

Exercise

Wrap your agent in a route handler, emit structured logs, and run a staged load test of 50 jobs. Record success rate, average runtime, and top three failure reasons. If you can explain those numbers clearly, your agent is ready for real users.