AI Agents and Systems Engineering

Architecture, Design Patterns, and Production Practices for Autonomous Systems

Introduction

1. What You Will Learn
2. The Running Example
3. How to Use This Book
4. A Note on Technology and Timeliness

Chapter 1: What Are AI Agents and What Problem Do They Solve

1. Definitions and Terminology: Agents, Tools, Autonomy, and Orchestration
2. The Agent Capability Spectrum: From Stateless Calls to Autonomous Loops
3. Agents vs Chatbots vs Pipelines vs Microservices: Where Each Fits
4. The Engineering Problem: Why Agents Are Harder Than Traditional Systems
5. Real-World Problem Spaces: What Agents Are Actually Good For
6. A Running Example: The DesignOps Agent
7. Summary

Chapter 2: Historical Foundations from Symbolic AI to LLM Agents

1. Classical Agents: BDI Architectures, SOAR, and Symbolic Planning
2. The Rise of Web Agents and AutoAgents (1990s–2000s)
3. Early Autonomous Systems: ROS, Swarm Intelligence, and Multi-Agent Systems
4. Deep Learning and the End of the Symbolic Era
5. GPT-3 and the Birth of Modern LLM Agents
6. Summary

Chapter 3: Core Architectural Components of Modern Agents

1. The Cognitive Stack: Perception, Reasoning, Action, and Reflection
2. LLMs as Inference Engines: Capabilities, Limits, and Integration Patterns
3. Tools and Functions: The Agent’s Interface to the Real World
4. Memory and State: Short-Term Context vs Long-Term Knowledge
5. Planning and Decomposition: From Single-Turn to Multi-Step Reasoning
6. The Minimal Viable Agent Architecture
7. Summary

Chapter 4: Reasoning Patterns Prompt Architectures and Cognitive Workflows

1. System Prompts and Role Framing: Setting Behavior Bounds
2. Chain of Thought and Structured Reasoning: Design and Trade-offs
3. Task Decomposition: Hierarchical and Flat Planning Approaches
4. Reflection and Self-Correction: Critique Loops and Verification
5. Determinism Engineering: Constrained Generation and Output Formatting
6. Failure Modes in Reasoning: Hallucination, Drift, and Mode Collapse
7. Summary

Chapter 5: Tool Use, Function Calling, and External Integration

1. Tool Definition Standards: JSON Schema, OpenAPI, and MCP Schemas
2. Function Calling APIs: How Modern LLMs Bind Tools to Generation
3. Tool Discovery and Selection: When the Agent Chooses What to Call
4. Error Handling and Recovery: Tool Failures, Timeouts, and Retries
5. Security and Sandboxing: Running Agent Tools Safely
6. Building Your Own Tool Server: From Simple APIs to Complex Services
7. Summary

Chapter 6: Memory Systems Context Management and Knowledge Retrieval

1. Conversation Memory and Context Windows: The Naive Approach and Its Limits
2. Context Engineering: Summarization, Sliding Windows, and Key-Fact Extraction
3. Vector Stores and Semantic Search: Architecture and Trade-offs
4. Retrieval-Augmented Generation as a System: Not Just a Prompt Trick
5. Hybrid Memory: Combining Episodic, Semantic, and Procedural Storage
6. State Persistence and Recovery: Making Memory Survive Crashes and Restarts
7. Summary

Chapter 7: Multi-Agent Systems and Orchestration Patterns

1. Why Multiple Agents: Specialization, Scale, and Separation of Concerns
2. Coordinator-Worker and Manager-Worker Patterns
3. Peer-to-Peer Agent Communication and Consensus
4. Debate and Critique Architectures: Agents That Challenge Each Other
5. Hierarchical Agent Organizations: Hierarchical Task Networks Reborn
6. Orchestration Frameworks: LangGraph, CrewAI, AutoGen, and Alternatives
7. Summary

Chapter 8: Workflow Engines and Deterministic Boundaries

1. The Determinism Problem: Why Pure Agent Loops Fail in Production
2. Workflow as the Execution Backbone: Temporal, Dagster, and Friends
3. State Machines and Directed Acyclic Graphs for Agent Workflows
4. Idempotency, Retries, and Compensation in Agent Workflows
5. Human Approval Gates and Escalation Paths
6. Mixing Deterministic and Non-Deterministic: The Hybrid Architecture
7. Summary

Chapter 9: Agent Runtime Architectures Process Container and Cluster

1. Agent as a Process: Long-Running Services vs Ephemeral Workers
2. Containerizing Agent Systems: Dockerfiles, Dependencies, and Base Images
3. Kubernetes for Agent Workloads: Deployments, Jobs, and Custom Resources
4. Serverless and FaaS for Agents: When It Works, When It Breaks
5. Local and Private AI Infrastructure: Running Agents Without Cloud Providers
6. Summary

Chapter 10: Model Serving and Inference Infrastructure

1. Model Serving Options: Cloud APIs, Open-Source Models, and Fine-Tuned Models
2. Inference Optimization: Quantization, Speculative Decoding, and Batch Processing
3. Caching Strategies: Prompt/Response Caching, Semantic Caching, and Tool Result Caching
4. Multi-Model Routing: Choosing the Right Model for the Right Task
5. Cost Architecture: Token Budgeting, Rate Limiting, and Predictive Scaling
6. Local Model Serving: Ollama, vLLM, TGI, and Self-Hosted Stacks
7. Summary

Chapter 11: Asynchronous Execution Queues and Event-Driven Architectures

1. Why Agents Need Async: Latency, Concurrency, and Long-Running Operations
2. Message Queues for Agent Communication: RabbitMQ, Kafka, NATS
3. Event Sourcing and CQRS with Agents
4. Scheduling and Cron-like Behavior: When Agents Should Run
5. Webhooks and Callbacks: Triggering Agents from External Systems
6. Handling Timeouts, Dead Letters, and Orphaned Tasks
7. Summary

Chapter 12: Communication Protocols and Interoperability

1. API Integration Patterns: REST, gRPC, GraphQL, and Async Patterns
2. The Model Context Protocol: Specification, Design, and Adoption
3. Agent-to-Agent Communication: Messages, Contracts, and Protocols
4. Event Bus and Pub/Sub for Agent Ecosystems
5. Standardizing Agent Contracts: Schema Validation and Versioning
6. Integrating Legacy Systems and Internal Services
7. Summary

Chapter 13: Reliability Engineering for Autonomous Agents

1. Defining Reliability for Non-Deterministic Systems
2. Circuit Breakers, Bulkheads, and Timeouts for Agent Operations
3. State Recovery: Checkpointing, Snapshots, and Rollback Strategies
4. Graceful Degradation: What Happens When the Model Is Down or Wrong
5. Resilience Patterns: Fallback Models, Degraded Modes, and Human Override
6. Chaos Engineering for Agents: Breaking Things on Purpose
7. Summary

Chapter 14: Observability Logging Tracing Metrics and Dashboards

1. The Observability Challenge: Non-Determinism Makes Debugging Harder
2. Structured Logging for Agent Systems: Events, Decisions, and Tool Calls
3. Distributed Tracing: End-to-End Visibility Across Agent Chains
4. Metrics That Matter: Latency, Token Usage, Success Rates, and Cost
5. Dashboards and Alerting: What Engineers Actually Need to See
6. Compliance Logging: Audit Trails for Regulated Environments
7. Summary

Chapter 15: Evaluation and Quality Engineering

1. Testing Agents: Unit Tests, Integration Tests, and End-to-End Scenarios
2. Evaluation Frameworks: LLM-as-Judge, Rule-Based Checks, and Human Review
3. Adversarial Testing: Prompt Injection, Jailbreak Attempts, and Attack Simulation
4. Regression Testing for Non-Deterministic Systems
5. Golden Datasets and Continuous Evaluation in Production
6. Cost and Performance Benchmarks: Measuring Efficiency, Not Just Accuracy
7. Summary

Chapter 16: Security Architecture for Agent Systems

1. The Expanded Attack Surface: What Makes Agents Harder to Secure
2. Prompt Injection and Indirect Prompt Injection: Vectors and Defenses
3. Tool Abuse and Escalation: Preventing Agents from Doing Harm
4. Sandboxing and Isolation: Containers, Wasm, and Restricted Runtimes
5. Least-Privilege Execution: Identity, Roles, and Permission Models
6. Secrets Management and Credential Handling in Agent Systems
7. Summary

Chapter 17: Data Security Privacy and Compliance

1. Data Classification and Handling: What Agents See and Where It Goes
2. PII, PHI, and Regulated Data: Requirements and Architectural Responses
3. Encryption: At Rest, In Transit, and In Use
4. Data Minimization and Retention Policies for Agent Memory
5. Audit Trails and Provenance: Tracing What Data Was Used for What Decision
6. Regulatory Landscape: EU AI Act, SOC2, HIPAA, GDPR, and Industry-Specific Rules
7. Summary

Chapter 18: Governance Guardrails and Responsible Operation

1. The Governance Problem: Who Is Responsible When the Agent Acts?
2. Policy Enforcement: Guardrails, Constraints, and Allow/Deny Lists
3. Human-in-the-Loop Design: Approval, Override, and Escalation Patterns
4. Model Governance: Versioning, Testing, and Deployment of Foundation Models
5. Incident Response: What to Do When an Agent Goes Rogue
6. Organizational Practices: Teams, Processes, and Decision Rights
7. Summary

Chapter 19: Scalability and Performance Optimization

1. Scaling Patterns: Horizontal Scaling, Sharding, and Load Balancing for Agents
2. Concurrency Models: Handling Many Agents and Many Tools Simultaneously
3. Performance Tuning: Reducing Latency, Token Overhead, and Tool Call Chains
4. Rate Limiting and Backpressure: Protecting Models and Tools from Overload
5. Capacity Planning: Sizing Infrastructure for Agent Workloads
6. Cost Optimization: Architectural Choices That Reduce Expense
7. Summary

Chapter 20: CI/CD Infrastructure as Code and DevOps for Agent Systems

1. Versioning Agent Code, Prompts, and Configurations
2. CI/CD Pipelines for Agent Systems: Testing, Building, and Deploying
3. Infrastructure as Code: Terraform, Pulumi, and Kubernetes Manifests
4. Configuration Management: Environment-Specific Config and Feature Flags
5. Blue-Green and Canary Deployments for Agent Systems
6. Rollback Strategies: Reverting Agents and Their State
7. Summary

Chapter 21: Reference Architectures From Prototype to Enterprise

1. Architecture Level 1: Single Agent, Single Model, Simple Tools
2. Architecture Level 2: Multi-Tool Agent with State and Memory
3. Architecture Level 3: Orchestrated Multi-Agent System with Workflow Engine
4. Architecture Level 4: Enterprise Platform with Security, Observability, and Governance
5. Evolution Paths: How to Grow Without Rewriting
6. Technology Stack Recommendations by Use Case and Scale
7. Summary

Chapter 22: Conclusion The Future of Agent-Centric Software Architecture

1. Lessons Learned: Architectural Principles for Longevity
2. What We Still Do Not Know: Open Research and Engineering Problems
3. How Agents Change Software Architecture: A Structural Shift
4. Agents in Developer Tooling: Self-Improving Systems and AI-Assisted Engineering
5. Agents in IT Operations: Autonomous SRE and Self-Healing Infrastructure
6. Final Thoughts: Engineering Discipline in an Age of Autonomy

References