Appendix C: Further Reading & References
Foundational articles
| Title | Author | Topic |
|---|---|---|
| “AI fatigue is real and nobody talks about it” | Siddhant Khare | Developer burnout from AI tools |
| “AI Doesn’t Reduce Work - It Intensifies It” | HBR / UC Berkeley Haas | Research on AI and workload |
| “Context Engineering: The Infrastructure Challenge in Production LLM Systems” | Siddhant Khare | Why RAG is insufficient |
| “Securing Agentic AI: Authorization Patterns for Autonomous Systems” | Siddhant Khare | Agent security threat model |
| “Effective Context Engineering for AI Agents” | Anthropic | Context engineering best practices |
| “Context Engineering for Agents” | LangChain | Context engineering patterns |
| “Logging Sucks” | Boris Tane | Wide events and canonical log lines for observability |
| “Skills: The Open Agent Skills Ecosystem” | Vercel Labs | Reusable procedural knowledge for AI agents |
Research papers
| Paper | Authors | Year | Key Finding |
|---|---|---|---|
| “Zanzibar: Google’s Consistent, Global Authorization System” | Pang et al. | 2019 | ReBAC at global scale |
| “Lost in the Middle” | Liu et al. | 2023 | LLMs struggle with information in the middle of long contexts |
| “AI Doesn’t Reduce Work - It Intensifies It” | Ranganathan & Ye | 2026 | AI creates cognitive overload despite perceived productivity |
| “ReAct: Synergizing Reasoning and Acting in Language Models” | Yao et al. | 2023 | Foundation for agent reasoning patterns |
| “MCPMark: Benchmarking LLMs on Real-World MCP Tasks” | NUS TRAIL / LobeHub | 2026 | Real-world tool use is much harder than synthetic benchmarks |
| “CASTER: Context-Aware Strategy for Task Efficient Routing” | ETH Zurich | 2026 | Dual-signal routing reduces costs by up to 72.4% |
| “Agentic AI Risk-Management Standards Profile” | UC Berkeley CLTC | 2026 | First formal framework for agentic AI risk management |
| “Toolformer: Language Models Can Teach Themselves to Use Tools” | Schick et al. | 2023 | Foundation for tool-using language models |
| “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models” | Wei et al. | 2022 | Explicit reasoning improves model performance |
| “Constitutional AI: Harmlessness from AI Feedback” | Bai et al. | 2022 | Self-improvement through AI feedback (related to backpressure) |
Standards & specifications
| Standard | Organization | URL |
|---|---|---|
| Model Context Protocol (MCP) | AAIF / Anthropic | modelcontextprotocol.io |
| Agent2Agent Protocol (A2A) | github.com/google/A2A | |
| AGENTS.md | AAIF / OpenAI | github.com/agentsmd/agents.md |
| OpenTelemetry | CNCF | opentelemetry.io |
| OpenFGA | CNCF | openfga.dev |
Books
| Title | Author | Year | Focus |
|---|---|---|---|
| “Building LLM Apps” | Valentino Gagliardi | 2025 | Practical LLM application development |
| “AI Engineering” | Chip Huyen | 2025 | Production AI systems |
| “Designing Data-Intensive Applications” | Martin Kleppmann | 2017 | Distributed systems fundamentals (still relevant) |
Podcasts & talks
| Title | Host/Speaker | Topic |
|---|---|---|
| Latent Space | Swyx & Alessio | AI engineering deep dives |
| Software Engineering Daily | Various | Engineering practices |
Online communities
| Community | Platform | Focus |
|---|---|---|
| r/LocalLLaMA | Open-source models, self-hosting, benchmarks | |
| AI Engineering | Discord | Agent infrastructure, MCP, tooling |
| LangChain Community | Discord | LangChain/LangGraph framework usage |
| Anthropic Developer Forum | Forum | Claude API, MCP, agent patterns |
| OpenAI Developer Forum | Forum | GPT API, function calling, structured outputs |
| Hacker News | Web | Industry news, technical discussions, launches |
Recommended reading order
If you’re new to agentic engineering, read the chapters in this order for the fastest path to production:
- Chapter 1 (Landscape) - understand what agents are and why they matter
- Chapter 4 (Context Windows) - understand the fundamental constraint
- Chapter 13 (AGENTS.md) - set up your first codebase context file
- Chapter 23 (Your First Agent) - deploy your first agent
- Chapter 32 (Backpressure) - set up automated feedback
- Chapter 24 (Security Checklist) - secure your deployment
- Chapter 25 (Measuring Impact) - measure whether it’s working
This seven-chapter path takes you from zero to a production agent deployment with security and measurement. The remaining chapters provide depth on specific topics as you need them.
If you’re an engineering leader evaluating agent adoption, read these chapters first:
- Chapter 2 (Capability Jump) - understand the current state of the art
- Chapter 20 (AI Fatigue) - understand the organizational risks
- Chapter 21 (Conductor Model) - understand the new engineering paradigm
- Chapter 33 (Adoption Playbook) - get a week-by-week implementation plan
- Chapter 25 (Measuring Impact) - understand how to measure ROI