9.9.1 Deployment Roadmap: Runtime, Persistence, Recovery

Deploying an Agent means more than putting code on a server. You need model calls, tool services, queues, state storage, traces, permissions, cost limits, and rollback paths.

See the Runtime Loop First

The production question is not “did it work once?” It is “can it keep working, fail safely, and recover?”

Run a Deployment Readiness Check

This check highlights missing production basics.

service = {
    "api_entry": True,
    "state_store": True,
    "trace_log": True,
    "cost_limit": True,
    "rollback": False,
}

missing = [name for name, ok in service.items() if not ok]

print("ready:", not missing)
print("missing:", missing)

Expected output:

ready: False
missing: ['rollback']

If the system cannot roll back or recover, do not call it production-ready.

Learn in This Order

Step	Read	Practice Output
1	Deployment architecture	Draw frontend, backend, model service, tool service, storage
2	Runtime management	Handle sync, async, long-running tasks, queues, interruption
3	Persistence and recovery	Save task state, memory, traces, intermediate results
4	Cost optimization	Track model calls, tool calls, caching, batching, routing
5	Production practices	Add monitoring, alerts, canary release, rollback, permissions

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Runtime

queues, workers, state store, tool services, and model endpoint

Persistence

checkpoints, event log, memory store, and recovery path

Ops Signal

latency, cost, error rate, trace coverage, and saturation

Failure Check

stuck run, duplicate action, partial failure, or runaway cost

Recovery Action

resume, rollback, cancel, human handoff, or degrade gracefully

Pass Check

You pass this chapter when a local Agent demo becomes a small service with API entry, state persistence, trace logs, error responses, cost records, and deployment instructions.

Check reasoning and explanation

1. A passing answer describes the agent loop: goal, plan, tool call, observation, memory or state update, and stop condition.
2. The evidence should include a trace that another developer can inspect, not only the final answer.
3. A good self-check names one safety or reliability control such as tool schemas, permission boundaries, retries, evaluation cases, or a human-review point.