Agent systems look simple at first. You connect a model, add tools, and build a workflow. But once these systems run in production, new problems appear. Behavior becomes hard to predict. Memory drifts. Retries stack up and create load. Failures spread across the system in ways that feel closer to distributed systems than application code.

This book takes a systems view of agent design. It treats agents as parts of a distributed control system with clear boundaries and responsibilities. Instead of focusing on prompts or frameworks, it explains how real systems behave under load and failure. You learn how to design memory models, structure retrieval pipelines, and choose orchestration patterns that stay stable over time.

The book also focuses on how things break. It introduces a clear set of failure patterns such as retry storms, silent drift, and loss of provenance. Each pattern is explained with real examples and practical fixes. You see how design decisions affect reliability, cost, and trust in production environments.

As you move forward, you explore observability, evaluation, and release practices for agent systems. Topics such as debugging, rollback, and system visibility are treated as core design concerns. The approach stays vendor‑neutral, so the ideas apply across tools and platforms.

This book is built for teams moving beyond prototypes. It provides a structured way to design, operate, and improve agent systems that need to work at scale.

What You Will Learn:

• Design agent systems with clear control boundaries
• Handle non‑determinism, retries, and state consistency
• Build reliable memory and retrieval pipelines
• Recognize and fix common production failure patterns

Who This Book Is For:

Backend engineers, platform teams, and architects building or operating agent systems in production. It is especially useful for teams moving from prototypes to reliable, large‑scale deployments.

Rithul Kamesh is a systems-focused engineer and builder working on agent-intensive systems and AI infrastructure. His work explores how large language model-based systems behave under real-world constraints, including reliability, memory consistency, orchestration, and failure modes.

He is the Co-Founder and CEO of Devsper, where he focuses on designing execution layers for persistent AI workflows. Alongside this, he develops systems such as veclite and continuum, exploring embedded vector storage and unified runtime abstractions for agent systems.

Rithul began working with software systems at a young age and has spent several years building across distributed systems, AI tooling, and developer infrastructure. This book emerged from his interest in the gap between building agent-based applications and operating them reliably in production. His goal is to bring a systems-engineering perspective to a space that is currently dominated by capability-driven approaches.