Scaling Autonomous AI Agents: Engineering for Production at Real-World Scale - Scaling· State· Memory· Caching

A working agent in a demo is not a production system. Real workloads bring concurrent users, growing state, unbounded token costs, and downstream services that fail in ways no notebook ever reveals. This book is the engineering playbook for taking autonomous AI agents from prototype to production scale. You will design horizontally scalable agent fleets, manage durable state across sessions, build vector-backed memory and semantic caches that cut latency and cost, and operate the result reliably, with working code you can deploy, not illustrations of concepts. Inside:Scaling fundamentals — stateless agent design, load balancing, queue-based architectures (Redis, RabbitMQ, SQS), and autoscaling for workloads where a single request can run for minutesDurable state — externalized conversation state, optimistic locking with compare-and-set, multi-agent shared state with vector clocks, and the trade-offs across CP and AP backing storesRetrieval and caching — vector database fundamentals (HNSW, product quantization, hybrid search with BM25 and RRF), self-evaluating RAG, LLM response caching, semantic caches with break-even analysis, and cache stampede preventionTwo end-to-end case studies — an enterprise customer-service platform with triage, order, technical, billing, and escalation agents under quality assessment and feedback loops, and an enterprise procurement system with intake, analysis, approval, and fulfillment agents under policy gateways and human-in-the-loop reviewForward look — how reasoning-model improvements, falling inference costs, and emerging context-length capabilities will reshape these patternsEngineering rigor throughout:Worked numerical examples for every formula (Amdahl's Law, Little's Law, Pollaczek-Khinchine, semantic-cache break-even)Primary-source citations (Amdahl, Karger, Brewer, Malkov HNSW, Robertson BM25, Cormack RRF, Lamport, Fidge)Illustrative-not-measured disclaimers on every quantitative anchor, so you know what to benchmark in your own environmentProduction-hardened companion Python repository mirroring every chapter listing — bounded resources, specific exception handling, retries with backoff, circuit breakers, and graceful shutdownPrerequisite knowledge: Comfortable with Python and basic LLM API calls. Production foundations (identity, audit, observability) are assumed from Book 2 of this series or equivalent experience. About the author: Dr. Vijay Raghavan writes the Agentic AI series, an engineering-first treatment of building, shipping, and scaling autonomous AI systems. Read more