Agent-Oriented Software Engineering MTA
From requirements to maintenance: lifecycle management for AI agent systems.

About this book:

*Agent-Oriented Software Engineering* provides a comprehensive framework for transitioning AI agents from experimental prototypes to robust, production-ready systems. The book advocates for a rigorous lifecycle that begins with precise requirements elicitation and stakeholder analysis, emphasizing that agents must navigate complex trade-offs between goals, safety, and cost. By modeling organizational roles, norms, and domain ontologies, developers can align autonomous behavior with business logic and regulatory standards from the outset.

The text addresses the unique challenges of non-determinism in AI by proposing hybrid architectures that combine symbolic reasoning—such as Belief-Desire-Intention (BDI) models—with statistical components like Large Language Models (LLMs). This modular approach is supported by structured observability, where cognitive logging and distributed tracing allow engineers to audit an agent’s "thought process." Verification is further strengthened through simulation-based testing, which enables the safe exploration of edge cases and emergent behaviors that traditional testing cannot capture.

To ensure long-term reliability, the book details specialized CI/CD pipelines tailored for AI, featuring version control for prompts, datasets, and models. It explores diverse deployment topologies across cloud, edge, and on-premises environments, while integrating Site Reliability Engineering (SRE) practices like Service Level Objectives (SLOs) and incident response. Security and privacy are treated as first-class concerns, with specific focus on resisting adversarial attacks and prompt injection.

Ultimately, the book emphasizes the necessity of human-in-the-loop operations and adaptive governance. It provides a pragmatic operating model for managing the evolution of agents as they learn from new data and encounter shifting regulations. Through real-world case studies and the identification of architectural anti-patterns, the work offers a roadmap for building transparent, accountable, and highly performant multi-agent systems that function as dependable members of a production ecosystem.

What You'll Find Inside:

• A complete lifecycle framework for AI agent systems, covering requirements, design, testing, deployment, and long-term maintenance.
• Core modeling techniques: goal, task, and constraint specification to drive agent behavior and ensure alignment with stakeholder intent.
• Architectural patterns for agents (reactive, BDI, learning-augmented) and modular design principles for maintainable, scalable systems.
• Observability, simulation-based verification, and testing strategies tailored to the non-deterministic nature of agent systems.
• Safety, alignment, security, privacy, compliance, and operational practices including CI/CD, monitoring, and human-in-the-loop governance.

Who's It For:

This book is intended for software engineers, machine learning practitioners, product managers, site reliability engineers, and compliance professionals who need to build, deploy, and maintain reliable AI agent systems. It provides a shared language and actionable processes that bridge technical and non-technical stakeholders, enabling teams to move from experimental prototypes to production-grade agent software.