Overview
Part II treats software development itself as a discovery process and shows how AI transforms every phase of the engineering lifecycle. Starting with vibe coding and prompt-driven programming, the chapters progress through context engineering, MCP servers, requirements and architecture discovery, multi-agent teams, testing, debugging, security, and operations. The part culminates in evaluating agents and building fully autonomous software systems.
Chapters
Chapter 7
Software Development As a Discovery Process
Reframing software engineering as iterative exploration, hypothesis testing, and knowledge creation.
Chapter 8
Foundations of AI-Assisted Software Engineering
Core principles, tools, and workflows for integrating AI into the development process.
Chapter 9
Vibe Coding
Natural-language-driven development where intent replaces syntax and AI handles implementation details.
Chapter 10
Prompting to Programming
The spectrum from conversational prompts to structured specifications that guide AI code generation.
Chapter 11
Context Engineering
Designing and managing the information environment that shapes AI behavior and output quality.
Chapter 12
Building MCP Servers
Creating Model Context Protocol servers that give AI agents structured access to tools and data.
Chapter 13
Requirements Discovery
Using AI to elicit, analyze, and validate software requirements from stakeholders and domains.
Chapter 14
Architecture Discovery
AI-driven exploration of system architectures, design patterns, and structural trade-offs.
Chapter 15
Algorithm Discovery
Automated search for novel algorithms, data structures, and computational strategies.
Chapter 16
AI Implementation
Translating designs into working code with AI pair programming and code generation.
Chapter 17
Multi-Agent Software Teams
Orchestrating multiple AI agents to collaborate on complex software projects.
Chapter 18
AI Testing
Automated test generation, mutation testing, property-based testing, and AI-driven QA workflows.
Chapter 19
AI Debugging
Root cause analysis, fault localization, and automated repair powered by language models.
Chapter 20
Software Security
AI-assisted vulnerability detection, secure code generation, and threat modeling.
Chapter 21
DevOps
AI-augmented CI/CD pipelines, infrastructure automation, and intelligent deployment strategies.
Chapter 22
MLOps, LLMOps, and AgentOps
Operationalizing machine learning models, large language models, and autonomous agents at scale.
Chapter 23
Evaluating Agents
Benchmarks, metrics, and evaluation frameworks for measuring AI agent capabilities and reliability.
Chapter 24
Autonomous Software
Self-writing, self-maintaining, and self-improving software systems driven by AI agents.