AI Agents - Building & Deploying

Take Your AI Agents From Prototype to Production Scale

Building an AI agent is only the beginning. Once an agent reaches production, a new set of problems appears: traffic spikes, growing API costs, slow processing, multiple specialised tasks, changing tools, reliability issues, monitoring requirements, and the need to evolve the system without breaking it.

AI Agents: Building & Deploying takes the next step in the journey. Following the production system introduced in Book 1, this practical guide shows you how to scale, coordinate, optimise, and harden AI agents for real-world workloads.

Using the evolving NexaCore platform as a running engineering project, you will move beyond a single-agent architecture and learn how production systems handle concurrency, cost, multi-agent coordination, advanced tooling, human oversight, observability, testing, and disaster recovery.

Inside the Book

• Build asynchronous agents with Python asyncio
• Process large workloads with concurrent and batch pipelines
• Optimise token usage and control LLM costs
• Implement caching strategies for faster and more efficient agents
• Design multi-agent systems with specialised agents
• Build agent-to-agent communication
• Implement supervisor and worker architectures
• Share memory and information across agents safely
• Generate and manage tools dynamically
• Version tools without breaking production systems
• Integrate human-in-the-loop workflows
• Add advanced observability and distributed tracing
• Run A/B tests on agent versions
• Design disaster recovery and failover strategies

This is not a book about building impressive demos. It is about solving the engineering problems that appear after your AI agent starts being used for real.

Whether you have already deployed an agent or are ready to move beyond the foundations, this book gives you the architecture and practical patterns needed to build AI systems that can handle scale, complexity, and operational pressure.

Your agent works. Now learn how to make it work at scale.

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