# Production Agent Systems
All Production Agent Systems notes →1. Agent Runtime
The execution substrate that hosts an agent's reasoning loop — process model, container vs. serverless tradeoffs, cold-start latency, and how the runtime enforces max-iteration and timeout limits so a stuck agent doesn't run (and bill) forever.
2. Session Management
How an agent tracks conversation identity across turns and channels — session ID generation, TTL and idle-timeout policy, session affinity in a load-balanced fleet, and the handoff problem when a user resumes a stale session on a different agent instance.
3. State Persistence
Where agent state lives between turns and after a crash — durable stores for message history and working memory (Redis, Postgres, DynamoDB), write-ahead patterns for mid-tool-call failures, and the tradeoff between snapshotting full state vs. replaying an event log.
4. Event Streaming
Publishing agent lifecycle events (tool calls, state transitions, token usage) onto a stream like Kafka or Kinesis so downstream consumers — observability, billing, audit — can react without coupling to the agent's request path.
5. Message Queues
Decoupling long-running agent tasks from the synchronous request path using queues (SQS, RabbitMQ, Celery) — at-least-once delivery semantics, idempotency keys for tool calls, dead-letter queues for tasks that exhaust retries, and backpressure when the LLM provider rate-limits.
6. Workflow Engines
Orchestrating multi-step, long-running agent workflows with durable execution engines (Temporal, AWS Step Functions, LangGraph persistence) so a workflow survives process restarts and resumes exactly where it left off after a crash.
7. Distributed Execution
Running agent workloads across multiple nodes — sharding by session or tenant, coordinating shared state without a single point of failure, and the consistency tradeoffs when two agent instances could act on the same conversation concurrently.
8. Scheduling
Placing agent workloads onto compute — priority queues for interactive vs. batch agent runs, autoscaling triggers tied to queue depth or token throughput rather than CPU, and preemption policy when a high-priority request needs capacity held by a long-running agent.
9. Scaling Strategies
Stateless vs. stateful agent design, offloading long-running work to queues and background tasks, horizontal scaling, and rate limiting against the LLM provider.
10. Multi-Tenant Architectures
Isolating tenants sharing an agent platform — per-tenant rate limits and token budgets, noisy-neighbor containment, data isolation for prompts, memory, and logs, and the pool-vs-silo tradeoff for LLM provider capacity.
11. High Availability
Designing an agent platform to survive component failure — redundant LLM provider routing with failover, health checks that account for degraded (slow but not down) model endpoints, and graceful degradation to a smaller model or cached response under partial outage.
12. Disaster Recovery
RTO and RPO targets for an agent platform's stateful components — conversation history, vector memory, prompt and model version registry — cross-region failover for the control plane, and the recovery drill that validates a full region loss doesn't silently corrupt in-flight tool calls.