How do you deploy and monitor AI agents in production environments?

Production AI agent deployment requires infrastructure for execution, monitoring, and reliability. Deployment architecture: agent runtime (executes agent logic, manages tool calls, handles conversation state), model serving (LLM inference endpoint with load balancing), tool infrastructure (APIs, databases, and services the agent accesses), and observability stack (logging, tracing, metrics). Infrastructure: containerize agent runtime (Docker), deploy on Kubernetes for scaling, implement horizontal scaling based on concurrent agent sessions, and use queue-based architecture for async task processing. Monitoring: trace every agent step (thought, action, observation) with correlation IDs, measure latency per step and end-to-end, track token usage and cost per interaction, monitor tool call success rates, and alert on anomalies (unusual step counts, high error rates, cost spikes). Logging: structured logs including sessionid, stepnumber, actiontype, toolname, inputsummary, outputsummary, latencyms, and tokencount. Implement log levels (debug for full prompts/responses in development, info for actions and outcomes in production). Cost management: implement per-user and per-session cost limits, track cost per task type for budgeting, implement cost-aware model routing (use cheaper models for simple tasks), and alert when cost exceeds expected ranges. Reliability: implement graceful degradation (fall back to simpler logic when LLM is unavailable), circuit breakers for external tools, session recovery from checkpoints (resume interrupted tasks), and timeout handling at every level. A/B testing: run multiple agent versions simultaneously, route traffic based on user cohorts, compare performance metrics, and gradually roll out improvements. Incident response: implement kill switches for immediate agent shutdown, maintain human escalation paths, and have rollback procedures for agent updates.