Key Takeaways
- The prototype-to-production gap is significant: prototypes demonstrate capability; production requires reliability, scalability, security, and operational maturity.
- Deploy agents as stateless API services behind load balancers with conversation state in Redis or a database for horizontal scaling.
- Canary deployments limit blast radius: route 5% of traffic to the new version, monitor metrics, and expand only when quality is confirmed.
- CI/CD must include AI-specific tests: unit tests (tools), integration tests (agent workflows), safety tests (prompt injection), and quality evaluation.
- Maintain instant rollback capability: the previous production version should be ready to receive 100% of traffic within minutes.
Production Infrastructure Design
| Component | Technology Options | Purpose | Scaling Strategy |
|---|---|---|---|
| API gateway | Kong, AWS API Gateway, nginx | Rate limiting, auth, routing | Horizontal |
| Agent service | FastAPI, Express (containerized) | Agent logic and orchestration | Horizontal (stateless) |
| State store | Redis, DynamoDB, PostgreSQL | Conversation state, checkpoints | Vertical + read replicas |
| Message queue | Redis, SQS, RabbitMQ | Async task processing | Horizontal |
| Vector store | Pinecone, Weaviate, pgvector | Long-term memory, RAG | Managed scaling |
| Monitoring | Datadog, Grafana, LangSmith | Metrics, tracing, alerting | Managed |
CI/CD Pipeline for AI Agents
- Code changes trigger: run linting, type checking, and unit tests on tools and utilities (2-3 minutes)
- Integration tests: run agent workflows against mock tools to verify correct behavior (5-10 minutes)
- Safety tests: run prompt injection and adversarial test suite to verify guardrails (5-10 minutes)
- Quality evaluation: run evaluation dataset against staging agent, compare quality scores to baseline (10-20 minutes)
- Deploy to staging: full deployment with production-equivalent configuration for manual review
- Canary deployment: route 5% of production traffic to new version, monitor for 30-60 minutes
- Full rollout: expand to 100% if metrics are healthy, or automatic rollback if degradation detected
Prompt Changes Are Deployments
Treat prompt changes as code changes that go through the full CI/CD pipeline. A single word change in a system prompt can dramatically alter agent behavior. Version control prompts, test changes against evaluation datasets, and deploy through the same pipeline as code changes.
Scaling and Performance
| Challenge | Solution | Implementation |
|---|---|---|
| LLM latency (1-10s/call) | Async processing + streaming | Message queues for async; SSE for streaming |
| Concurrent users | Horizontal scaling | Stateless services behind load balancer |
| Burst traffic | Request queuing | Queue with auto-scaling consumers |
| Memory costs at scale | Tiered storage | Hot (Redis), warm (PostgreSQL), cold (S3) |
| LLM rate limits | Provider load balancing | Distribute across multiple API keys/regions |
| Cold starts | Keep-warm strategies | Minimum replica count, scheduled warm-up |
Operational Readiness Checklist
- Rollback plan: previous version ready for instant rollback. Test rollback procedure monthly.
- On-call rotation: engineers familiar with agent architecture available 24/7 for critical issues
- Runbook: documented procedures for common operational tasks (restart, clear cache, update prompts, disable features)
- Incident response: defined severity levels, escalation paths, and communication templates for agent incidents
- Disaster recovery: recovery procedures for provider outage (failover to alternate LLM), database failure, and full service outage
- Cost controls: automated budget alerts, per-session token limits, and kill switches for runaway costs
Feature Flags
Use feature flags to control agent capabilities in production. This allows disabling specific tools, adjusting safety thresholds, or switching models without deploying code. Feature flags turn risky changes into instant, reversible configuration changes.
Frequently Asked Questions
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