Methodology & Bibliography

How this quick-reference glossary is built, what counts as a canonical citation, how this site relates to the deep glossary, and how we keep entries fresh.

Data last refreshed: 2026-05-07. Next scheduled refresh: Q3 2026 (or sooner on regulator / vendor trigger).

What this site is — and isn't

This is the quick-reference sibling of the Agentic Glossary. The deep glossary publishes 31 hand-curated flagship terms with paragraph-long entries and full primary-source block-quotes. This site publishes 88 terms with one-to-three-sentence definitions and one canonical citation each — optimized for fast lookup, copy-paste into agent contexts, and clean LLM citation.

For flagship terms (Agent, MCP, A2A, RAG, Constitutional AI, the 8 Primitives, Reliability Gap, the Frameworks triad, ADLC, AX, Context Graph, Agent Management Platform, AI RMF, EU AI Act high-risk system, and others), each entry on this site carries a Compare with deep glossary → link to the deeper, narrative entry. The two sites are deliberate complements, not competitors.

What counts as a "term"

The bar for inclusion in this v1 build:

1. It is searched for, currently, by builders / operators / buyers of AI agents (validated via search-intent signals — Reddit, HN, Google Trends, search console).
2. At least one canonical primary source defines it directly — the source we'd cite if pressed.
3. It either (a) connects to one of the AgentsBooks 8 Primitives or a current pillar topic, or (b) appears recurrently in agentic-system architecture, evaluation, or compliance literature.

Sourcing rule

Every entry has at least one direct citation to a canonical primary source — meaning:

• The vendor's own published documentation, blog, or research paper (Anthropic, Google, OpenAI, Microsoft, Meta).
• The standards body's own specification (Linux Foundation A2A / MCP, NIST, ISO).
• The regulator's own publication (European Commission, NIST, FATF, FCA).
• The peer-reviewed paper of record (foundational arXiv papers; NeurIPS / ICLR / ACL proceedings).
• An analyst publication where the analyst is the primary source for the framing (Gartner Hype Cycle terminology, McKinsey adoption stats).

Wikipedia, secondary blogs, and content farms are never the primary citation.

Freshness flags

Every entry that needs one carries a freshness flag:

• Foundational — original peer-reviewed papers (e.g., 2017 Transformer, 2020 RAG, 2022 Constitutional AI, 2022 CoT, 2022 ReAct). Considered evergreen — refreshed only on substantive revision.
• In force — current, binding regulator text or in-effect specifications. Includes the in-force date where applicable (e.g., EU AI Act Annex III, 2 August 2026).
• Emerging 2026 — terms that entered mainstream discourse in 2026 (ADLC, AX, context graph, agent management platform, AI Agent Interoperability Profile). Quarterly review.
• Contested — entries with named, meaningful disagreement in the field (Agentic AI, Reliability Gap). We carry both positions.

Anything older than six months that does not carry one of the four flags is considered stale and gets retired or refreshed.

Refresh cadence

Bibliography (v1 — 2026-05-07)

The full canonical-source list backing the v1 entry set. Each line: source, URL, accessed date, role.

1. Anthropic, Building Effective Agents. www.anthropic.com/research/building-effective-agents — accessed 2026-05-07. Canonical definition of agent, workflow, plan-and-execute, routing.
2. Anthropic, Introducing the Model Context Protocol. www.anthropic.com/news/model-context-protocol — accessed 2026-05-07.
3. Anthropic, Measuring AI agent autonomy in practice. www.anthropic.com/research/measuring-agent-autonomy — accessed 2026-05-07.
4. Anthropic, Many-shot jailbreaking. www.anthropic.com/research/many-shot-jailbreaking — accessed 2026-05-07.
5. Hubinger et al. (Anthropic), Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, arXiv:2401.05566, 2024. arxiv.org/abs/2401.05566 — accessed 2026-05-07.
6. Bai et al. (Anthropic), Constitutional AI: Harmlessness from AI Feedback, arXiv:2212.08073, 2022. arxiv.org/abs/2212.08073 — accessed 2026-05-07. Foundational
7. Vaswani et al., Attention Is All You Need, arXiv:1706.03762, 2017. arxiv.org/abs/1706.03762 — accessed 2026-05-07. Foundational
8. Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, arXiv:2005.11401, 2020. arxiv.org/abs/2005.11401 — accessed 2026-05-07. Foundational
9. Wei et al., Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, arXiv:2201.11903, 2022. arxiv.org/abs/2201.11903 — accessed 2026-05-07. Foundational
10. Yao et al., ReAct: Synergizing Reasoning and Acting in Language Models, arXiv:2210.03629, 2022. arxiv.org/abs/2210.03629 — accessed 2026-05-07. Foundational
11. Yao et al., Tree of Thoughts: Deliberate Problem Solving with Large Language Models, arXiv:2305.10601, 2023. arxiv.org/abs/2305.10601 — accessed 2026-05-07.
12. Schick et al., Toolformer: Language Models Can Teach Themselves to Use Tools, arXiv:2302.04761, 2023. arxiv.org/abs/2302.04761 — accessed 2026-05-07.
13. Park et al., Generative Agents: Interactive Simulacra of Human Behavior, arXiv:2304.03442, 2023. arxiv.org/abs/2304.03442 — accessed 2026-05-07.
14. Shinn et al., Reflexion: Language Agents with Verbal Reinforcement Learning, arXiv:2303.11366, 2023. arxiv.org/abs/2303.11366 — accessed 2026-05-07.
15. Chen et al., Evaluating Large Language Models Trained on Code (HumanEval / Codex), arXiv:2107.03374, 2021. arxiv.org/abs/2107.03374 — accessed 2026-05-07. Foundational
16. Jimenez et al., SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, arXiv:2310.06770, 2023. arxiv.org/abs/2310.06770 — accessed 2026-05-07.
17. Mialon et al., GAIA: A Benchmark for General AI Assistants, arXiv:2311.12983, 2023. arxiv.org/abs/2311.12983 — accessed 2026-05-07.
18. Model Context Protocol, Specification (2025-11-25 revision). modelcontextprotocol.io/specification/2025-11-25 — accessed 2026-05-07. In force
19. Google Developers, Announcing the Agent2Agent Protocol (A2A). developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/ — accessed 2026-05-07.
20. A2A Protocol, Specification. a2a-protocol.org/latest/specification/ — accessed 2026-05-07. In force
21. OpenAI, Function calling — API documentation. platform.openai.com/docs/guides/function-calling — accessed 2026-05-07.
22. OpenAI, Evals framework. github.com/openai/evals — accessed 2026-05-07.
23. NIST, Artificial Intelligence Risk Management Framework (AI 100-1). nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf — accessed 2026-05-07. In force
24. NIST, AI Risk Management Framework — Generative AI Profile (AI 600-1). nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf — accessed 2026-05-07. In force
25. NIST, AI Risk Management Framework hub (AI Agent Standards Initiative announced February 2026 via CAISI; AI Agent Interoperability Profile planned Q4 2026). nist.gov/itl/ai-risk-management-framework — accessed 2026-05-07.
26. European Commission, EU AI Act Implementation Timeline. ai-act-service-desk.ec.europa.eu/en/ai-act/timeline/timeline-implementation-eu-ai-act — accessed 2026-05-07. In force 2 August 2026 (subject to Digital Omnibus deferral proposal).
27. ISO, ISO/IEC 42001:2023 — Information technology — Artificial intelligence — Management system. iso.org/standard/81230.html — accessed 2026-05-07. In force
28. Gartner, Hype Cycle for Agentic AI 2026. gartner.com/en/articles/hype-cycle-for-agentic-ai — accessed 2026-05-07. Source of "ADLC", "AX", "context graph", "agent management platform" framings.
29. McKinsey QuantumBlack, The state of AI in 2025: Agents, innovation, and transformation, November 2025. mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai — accessed 2026-05-07. Source of 23%-scaling and 39%-EBIT-impact stats.
30. Marcus, G., Six (or seven) predictions for AI 2026 from a Generative AI realist, Substack. garymarcus.substack.com/p/six-or-seven-predictions-for-ai-2026 — accessed 2026-05-07. Contested — paired with McKinsey's adoption data.
31. LangChain, LLM observability tools — comparison. langchain.com/articles/llm-observability-tools — accessed 2026-05-07.
32. Pecollective, AI agent frameworks compared (2026 survey). pecollective.com/blog/ai-agent-frameworks-compared/ — accessed 2026-05-07.
33. Firecrawl, Best vector databases (2026 survey). firecrawl.dev/blog/best-vector-databases — accessed 2026-05-07.
34. AgentsBooks, Anatomy of a Firm. agentsbooks.com/anatomy — accessed 2026-05-07. Canonical home of the eight-primitive vocabulary.
35. AgentsBooks, Tasks & Triggers. agentsbooks.com/features/tasks-triggers — accessed 2026-05-07.
36. AgentsBooks, Knowledge & Learning. agentsbooks.com/features/knowledge-learning — accessed 2026-05-07.
37. AgentsBooks, Control & Friends. agentsbooks.com/features/control-friends — accessed 2026-05-07.

Relationship to the deep glossary

The Agentic Glossary is the canonical narrative entry-point for AgentsBooks vocabulary; this site is its breadth layer and copy-paste surface. Where the two sites cover the same flagship term, the deep glossary's definition is the authoritative one — this site defers to it via a Compare with deep glossary → link on every flagship entry.

How to suggest a term or correction

Email hello@agentsbooks.com with the term, what you'd like it defined as, and the canonical source you want cited. We aim to triage within five business days; verified additions ship in the next quarterly refresh, with same-week handling for regulator / vendor news.

Updated 2026-05-07 — by the AgentsBooks team. Built per the build-authority-property workflow.