Cut LLM latency and cost by 40-70% with semantic caching on Redis and Bedrock prompt caching. Config-driven cache policies, invalidation, and monitoring.

Agents are stateful. Store conversation history, session state, and tool results in DynamoDB. Add Global Tables for multi-region replication and DAX for caching.

Take your MCP server from localhost to production on AWS. ECS Fargate with ALB, auto-scaling, Secrets Manager, and a CI/CD pipeline so shipping is one git push away.

One agent is a feature. A platform for building agents is a capability. Unify tools, governance, approvals, deployment, and monitoring into an internal platform that every team can use.

Your agent is a single point of failure. Deploy across regions, handle region failover, replicate state, and keep the agent running when a data center goes dark.

Agents fail. Handle it gracefully. Implement retry with exponential backoff, circuit breakers, fallback chains, and graceful degradation — so your agent survives production chaos.

Ship changes to your agent without breaking production. Implement prompt versioning, gradual rollouts, A/B experiments, and automated evaluation pipelines with weight-based traffic splitting.

Stop paying for the same LLM call twice. Implement semantic caching, tool result caching, session-aware TTLs, and cache invalidation for AI agents — with Redis and embeddings.