Agent Servicing
Agents as production workloads
Agent servicing is the operations layer: run agents as jobs with queues, retries, timeouts, scaling, and durability — because long agent runs are workload, not single requests. Compute these as functions and services with production concerns.
What you will learn
- Run agents as durable jobs
- Queue and scale workers
- Set service-level timeouts and budgets
What is this?
Agent servicing treats agents as workloads: enqueue jobs (agent runs) into durable queues, execute on worker pools with retries/timeouts, persist state, and scale according to load. Long autonomy needs production machinery — queues, observability, budgets, graceful degradation — not a background thread in a web server.
Why do you need it?
- Long-running agents outlive HTTP request lifetimes.
- Queues give retry, throttle, and observability for free.
- Workers scale independently; one busy session doesn't stall others.
- Budgets/timeouts become enforceable SLAs per job.
Mental model
A package-sorting hub
Agent servicing
Parcels (agent jobs) enter at the dock (queue), travel on belts (workers) with scan checkpoints (durable state), get re-routed on issues (retries), and ship on schedule — a hub runs like a well-instrumented production line, not one courier driving everywhere.
How it works
- Enqueue agent runs as jobs into a durable queue.
- Queue marks progress; workers poll and claim.
- Apply per-job timeouts, retries, and spend budgets.
- Persist state per job; support resume on worker loss.
- Scale workers by queue depth; cap concurrency.
Job pipeline.
In production
- Batch migration agents run as queued jobs per tenant.
- Long research or report agents execute server-side, answer later.
- Resumable worker pools serve many concurrent agent sessions.
Best practices
- Idempotent resume: a worker retry must not double-execute steps.
- Per-job budgets (tokens, time) written into the job record.
- Durable queue + durable state; nothing lives only in memory.
- Observe queue depth and failure classes like any workload.
Common mistakes
- Running long agents in HTTP request handlers.
- In-memory job lists lost on restart.
- Double-execution on retry (non-idempotent steps).
Common issues
Workers crash mid-jobState ephemeral
Persist per-job state with checkpoints; workers resume.
Budget audits surpriseBudgets not per-job
Attach token/time budgets to the job record and enforce at claim.
Debugging tips
- Queue backlog grows → worker scale below rate or slow steps; inspect.
- Lost jobs after restart → jobs not durable; move to a real queue/DB.
- Retries duplicate side effects → steps lack idempotency keys.
Interview checkpoint
Quick quiz
Long agent runs belong in…
Retried jobs may need idempotency keys.