The Production Guide to Building AI Agents That Actually Work
Stop building toy demos. Start shipping production-grade AI agent systems.
This comprehensive technical guide bridges the gap between research papers and real-world deployments, teaching you to architect, implement, and scale autonomous multi-agent systems that handle millions of requests in production.
What Makes This Book Different:
Written by Ant�nio Fontoura-AI engineer with 10 years of production experience, inventor of two patent-pending AI systems, and founder of Arcannia, an AI memory infrastructure platform processing 50,000+ agent executions daily.
This isn't another intro to AI. This is the field manual for senior engineers who need to design agent systems that scale, stay within budget, and don't hallucinate into production incidents.
You'll Learn:
- How agents differ from chatbots-and why they cost 13-33x more to run
- Production-tested memory architectures: episodic, semantic, procedural, and symbiotic
- Real implementations with complete Python/TypeScript code-not pseudocode
- Multi-agent coordination: hierarchical, collaborative, swarm-based
- Tool integration and sandboxing that prevents agents from breaking things
- Advanced planning algorithms: ReAct, HTN, GOAP, and neuromorphic reasoning
- Cost optimization with actual dollar amounts and ROI calculations
- Monitoring, evaluation, and safety patterns for production deployments
- Three deep case studies including Arcannia's production architecture
Real Numbers, Real Systems:
Every chapter includes concrete metrics:
- Infrastructure costs per execution
- Latency targets (p50, p95, p99)
- Memory retrieval accuracy rates
- Token consumption optimization
- Failure modes and mitigation patterns
462 Pages Covering:
- 29 chapters across 8 major parts
- Complete code examples in Python and TypeScript
- Architectural diagrams for every major pattern
- Cost-benefit analysis for each approach
- Common mistakes and how to avoid them
- Comprehensive appendices: framework comparisons, reference implementations, design patterns
Who This Book Is For:
- Senior AI/ML engineers moving agents to production
- System architects designing multi-agent platforms
- Technical leads evaluating agent frameworks
- CTOs planning AI infrastructure investments
- Researchers transitioning from papers to deployments
Prerequisites: Strong programming background, APIs and distributed systems knowledge, LLM familiarity.
Patent-Pending Innovations:
- Symbiotic Multimodal Memory System (BR 10 2025 021087 8): How humans and agents share memory universes
- Symbolic Neuromorphic System (BR 10 2025 021346 0): Combining symbolic reasoning with neural networks
Plus deployment patterns from Arcannia: 200,000+ memory retrievals daily, 85% relevance, sub-200ms latency.
From the Author:
"I wrote this book as my own technical reference. When you hyperfocus on a problem, go deep, then step away for months, you need documentation you can trust. Every pattern, cost calculation, and failure mode comes from real production systems."
The Bottom Line:
If you're building AI agents for production-systems that need to scale, stay reliable, and justify their cost-this book will save you months of trial and error.
Stop guessing. Start building agents that work.
