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Sources

Below is the main set of primary sources used by the current version of the book. Last editorial source review: July 24, 2026.

How to read this list

It is useful to separate these sources not only by topic, but also by the strength of support they provide:

  • Normative frame: NIST, OWASP, CISA, and related documents that define stable governance contours;
  • Platform practice: OpenAI, Anthropic, LangGraph, Google Cloud, Microsoft, and similar material showing how teams assemble those contours in production;
  • HCI, HITL, and human oversight: sources that show where automation fails and how to keep a human in the loop;
  • Research frontier: newer papers on memory, observability, verifier design, and multi-agent reliability.

If you need the strongest base for Parts I, V, and VIII, start with the normative frame and the HCI/HITL layer. If you need current engineering practice, read the platform docs and recent research, but always pay attention to publication dates.

Canonical source routes

Use the sources as a fast route for the three canonical cases. Support triage starts with OWASP, OpenAI agent guides, HITL sources, policy/approval material, trace grading, and incident cases. Internal knowledge assistant starts with LangGraph memory, OpenAI Agent memory, retrieval/eval sources, provenance-oriented governance, and the memory research frontier. Incident coordination starts with NIST/AI RMF, Google/Microsoft governance, observability sources, multi-agent reliability research, incident review, and rollout/control-plane material.

Normative Frameworks and Governance Contours

Agent-specific security

Governance and baseline controls

Agent Architecture and Platform Patterns

Observability, Evals, and Verifier Design

HCI, HITL, and Human Oversight

Governance, Security, and Operational Assurance

Incidents and Cases

Research Frontier: Memory, Observability, and Multi-Agent Reliability

Publishing, Build, and the Book Platform Layer

Rust and the Infrastructure Layer of Agent Runtimes

How To Use This List

If you extend the book further, this order is convenient:

  1. Risk and control framing: NIST, OWASP, CISA.
  2. Architectural patterns and runtime discipline: Anthropic, OpenAI, LangGraph, Google Cloud, Microsoft.
  3. Observability, evals, and verifier layers: OpenAI, Microsoft, arXiv, GitHub.
  4. HCI, HITL, and cases: Microsoft Research, OpenReview, ABA.
  5. Research frontier: memory, consistency, observability, and multi-agent failure modes.

For reading the book itself, one more split is useful:

  • Stable core: normative frameworks, architecture, policy, execution, and observability;
  • Fast-moving layer: eval tooling, verifier design, inventory governance, frontier research, and newer cases.