How do you operate long-running autonomous agents in production?

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Run agents on a durable execution layer (checkpointed state, resumable steps, idempotent tools), in sandboxed environments with least-privilege credentials, under per-run budgets (steps, tokens, time, money) and policy enforcement on tool calls. Give them human-in-the-loop interrupts, full tracing, progress reporting, and fleet-level monitoring (success rates, stuck runs, costs, policy violations). Treat agent versions like any other release, with evals and staged rollout.

A task request flows through a task queue to a durable orchestrator, which drives an agent worker, human approval interrupts and a traces store; the agent worker runs in a sandbox, reaches business systems through a tool policy gateway, and is capped by a budget enforcer, while traces feed a fleet dashboard.

Operational concerns

Durability: a worker crash shouldn't lose 40 minutes of progress. Checkpoint after each step (Temporal, LangGraph checkpointers, or similar).
Idempotency: tool calls carry idempotency keys; resumed runs don't duplicate side effects.
Stuck-run detection: no progress for N minutes or repeated actions leads to an auto-pause and alert.
Budgets: hard stops with partial results; per-tenant budget limits.
Security: short-lived credentials per run; sandbox teardown after completion; egress allow-lists.
Concurrency control: limits per tenant and per downstream system (don't overwhelm the CRM API).
Observability: per-run trace trees; fleet metrics; sampling of runs for quality review.
Versioning: in-flight runs keep their agent version; new runs pick up new versions via the rollout.

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How do you operate long-running autonomous agents in production?

Operational concerns

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