Lukman Nuriakhmetov
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References · Version 1.0

Bibliography

Canonical source list for the manifesto, with stable reference anchors and links back to the chapters that cite each work.

Selection policy

This is not a general bibliography of AI agents. A source is included only when it supports a material distinction, mechanism, comparison, or research boundary in the manifesto. Source priority:

  1. original paper, book, specification, or standard;
  2. official documentation or original engineering report;
  3. authoritative bibliographic record when primary metadata is incomplete. The bibliography distinguishes intellectual ancestry from contemporary system evidence. Inclusion does not imply that a source describes the complete proposed architecture.

Philosophy of action, intention, and planning agency

  • Bratman, Michael E. Intention, Plans, and Practical Reason. Cambridge, MA: Harvard University Press, 1987. Reissued by CSLI Publications, 1999. Foundational account of intention, partial plans, stability, and cross-temporal coordination.
  • Bratman, Michael E.; David J. Israel; Martha E. Pollack. “Plans and Resource-Bounded Practical Reasoning.” Computational Intelligence 4, no. 3 (1988): 349–355. DOI 10.1111/j.1467-8640.1988.tb00284.x. Cited in Ch. 3, Ch. 12
  • Stanford Encyclopedia of Philosophy. “Intention.” Current scholarly reference reviewed 17 October 2026. Cited in Ch. 1, Ch. 3, Ch. 12
  • Stanford Encyclopedia of Philosophy. “Action.” Current scholarly reference reviewed 17 October 2026.
  • Rao, Anand S.; Michael P. Georgeff. “BDI Agents: From Theory to Practice.” In Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), 312–319. AAAI Press, 1995. Official PDF. Cited in Ch. 12

Hierarchical, partial-order, and systems planning

Truth maintenance, cognitive architectures, and metacognition

  • Doyle, Jon. “A Truth Maintenance System.” Artificial Intelligence 12, no. 3 (1979): 231–272. DOI 10.1016/0004-3702(79)90008-0. Cited in Ch. 1, Ch. 7, Ch. 12
  • de Kleer, Johan. “An Assumption-Based TMS.” Artificial Intelligence 28, no. 2 (1986): 127–162. DOI 10.1016/0004-3702(86)90080-9. Cited in Ch. 1, Ch. 7, Ch. 12
  • de Kleer, Johan. “Problem Solving with the ATMS.” Artificial Intelligence 28, no. 2 (1986): 197–224. DOI 10.1016/0004-3702(86)90082-2.
  • Laird, John E. The Soar Cognitive Architecture. Cambridge, MA: MIT Press, 2012.
  • Soar Group, University of Michigan. “The Soar Architecture,” “Semantic Memory,” and “Episodic Memory.” Official architecture documentation, reviewed 17 October 2026. Cited in Ch. 3, Ch. 4, Ch. 12
  • Anderson, John R.; Daniel Bothell; Michael D. Byrne; Scott Douglass; Christian Lebiere; Yulin Qin. “An Integrated Theory of the Mind.” Psychological Review 111, no. 4 (2004): 1036–1060. DOI 10.1037/0033-295X.111.4.1036.
  • ACT-R Research Group, Carnegie Mellon University. “ACT-R.” Official cognitive-architecture site and publications index, reviewed 17 October 2026. Cited in Ch. 4, Ch. 12
  • Nelson, Thomas O.; Louis Narens. “Metamemory: A Theoretical Framework and New Findings.” In The Psychology of Learning and Motivation, vol. 26, 125–173. Academic Press, 1990.
  • Fleming, Stephen M. “Towards an Integrative Neuroscience of Metacognition.” Nature Reviews Neuroscience (2026). DOI 10.1038/s41583-026-01081-x. Cited in Ch. 6, Ch. 12

Distributed cognition, external cognition, and transactive memory

  • Hutchins, Edwin. Cognition in the Wild. Cambridge, MA: MIT Press, 1995. Publisher page. Cited in Ch. 6, Ch. 12
  • Clark, Andy; David Chalmers. “The Extended Mind.” Analysis 58, no. 1 (1998): 7–19. DOI 10.1111/1467-8284.00096. Cited in Ch. 6, Ch. 12
  • Ren, Yuqing; Linda Argote. “Transactive Memory Systems 1985–2010: An Integrative Framework of Key Dimensions, Antecedents, and Consequences.” Academy of Management Annals 5, no. 1 (2011): 189–229. DOI 10.5465/19416520.2011.590300. Cited in Ch. 6, Ch. 12
  • Rummel, Jan; Lia Kvavilashvili. “Current Theories of Prospective Memory.” Nature Reviews Psychology 2 (2023). DOI 10.1038/s44159-022-00121-4. Cited in Ch. 4, Ch. 12
  • Guo, Yufei; Qun Ye. “Meta-Cognitive Insights into Cognitive Offloading: Mechanisms, Interventions, and Educational Implications.” Humanities and Social Sciences Communications 13, article 772 (2026). DOI 10.1057/s41599-026-06621-5. Cited in Ch. 6

Memory consolidation, reconsolidation, and forgetting

  • Tonegawa, Susumu; Mark D. Morrissey; Takashi Kitamura. “The Role of Engram Cells in the Systems Consolidation of Memory.” Nature Reviews Neuroscience 19 (2018): 485–498. DOI 10.1038/s41583-018-0031-2. Cited in Ch. 12
  • Nader, Karim; Oliver Hardt. “A Single Standard for Memory: The Case for Reconsolidation.” Nature Reviews Neuroscience 10 (2009): 224–234. DOI 10.1038/nrn2590. Cited in Ch. 4, Ch. 12
  • Ryan, Tomás J.; Paul W. Frankland. “Forgetting as a Form of Adaptive Engram Cell Plasticity.” Nature Reviews Neuroscience 23 (2022): 173–186. DOI 10.1038/s41583-021-00548-3. Cited in Ch. 4, Ch. 12

Social commitments, interaction, and delegation

  • Singh, Munindar P. “An Ontology for Commitments in Multiagent Systems.” Artificial Intelligence and Law 7, no. 1 (1999): 97–113. DOI 10.1023/A:1008319631231. Cited in Ch. 1, Ch. 3, Ch. 5, Ch. 12
  • Smith, Reid G. “The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver.” IEEE Transactions on Computers C-29, no. 12 (1980): 1104–1113. DOI 10.1109/TC.1980.1675516. Cited in Ch. 1, Ch. 5, Ch. 12
  • King, Thomas C.; Akın Günay; Amit K. Chopra; Munindar P. Singh. “Tosca: Operationalizing Commitments Over Information Protocols.” In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 256–264, 2017. DOI 10.24963/ijcai.2017/37. Cited in Ch. 5
  • Singh, Munindar P.; Amit K. Chopra. “Clouseau: Generating Communication Protocols from Commitments.” Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 5 (2020): 7244–7252. DOI 10.1609/aaai.v34i05.6215.
  • Telang, Pankaj R.; Munindar P. Singh; Neil Yorke-Smith. “Maintenance of Social Commitments in Multiagent Systems.” Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11369–11377. DOI 10.1609/aaai.v35i13.17355. Cited in Ch. 3, Ch. 12

Obligations, accountable coordination, and closed-loop governance

  • Zhang, Zuyuan; Hanqing Yang; Carlee Joe-Wong; Tian Lan. “Auditing Emergent LLM-Agent Collaboration through Cooperation-Obligation Coupling.” 2026. arXiv:2607.27429. iCORE couples observable cooperation, evolving work/assignment Obligations, and Evidence-bearing audit maps. Cited in Ch. 5, Ch. 7, Ch. 12
  • Feng, Youwei; Yitong Zhang; Yuetong Liu; Jia Li. “Safe Actions Alone Do Not Ensure Safe Agents: Identifying Unfulfilled Obligations with Guard Models.” 2026. arXiv:2610.11773. Introduces ObligationBench and demonstrates unfulfilled required actions as a distinct safety failure class. Cited in Ch. 8, Ch. 12
  • Medda, Francesca; Hui Gong. “Governed AI-Agent Coordination for Dementia Care: Architecture, Safety Contracts, and Evidence-Derived Workflow Verification.” 2026. arXiv:2609.25956. Domain-specific closed-loop architecture separating Observation, governed Memory, planning, policy enforcement, Execution, outcome monitoring, human handoff, and Closure. Cited in Ch. 12

Provenance, workflow semantics, and process conformance

  • W3C Provenance Working Group. “PROV-DM: The PROV Data Model.” W3C Recommendation, 30 April 2013. Cited in Ch. 3, Ch. 4, Ch. 7, Ch. 12
  • W3C Provenance Working Group. “PROV Model Primer.” W3C Working Group Note, 30 April 2013.
  • Tax, Niek; Natalia Sidorova; Reinder Haakma; Wil M. P. van der Aalst. “Event Abstraction for Process Mining Using Supervised Learning Techniques.” In Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 251–269. Lecture Notes in Networks and Systems 15. Springer, 2018. DOI 10.1007/978-3-319-56994-9_18. Establishes supervised abstraction from low-level event logs to higher-level interpretations. Cited in Ch. 3, Ch. 12
  • Bakullari, Bianka; Wil M. P. van der Aalst. “High-Level Event Mining: A Framework.” In 2022 4th International Conference on Process Mining (ICPM), 136–143. IEEE, 2022. DOI 10.1109/ICPM57379.2022.9980816. Defines detection and logging of system-level high-level events from ordinary event data. Cited in Ch. 3, Ch. 12
  • Microsoft Azure Architecture Center. “Event Sourcing Pattern.” Official architecture pattern, updated 28 March 2026. Establishes replayable event histories and materialized projections while documenting the pattern's consistency and governance trade-offs. Cited in Ch. 12
  • van der Aalst, Wil M. P.; Arthur H. M. ter Hofstede; Bartek Kiepuszewski; Alistair P. Barros. “Workflow Patterns.” Distributed and Parallel Databases 14 (2003): 5–51. DOI 10.1023/A:1022883727209. Cited in Ch. 12
  • Russell, Nick; Arthur H. M. ter Hofstede; Wil M. P. van der Aalst; Nataliya Mulyar. Workflow Patterns: The Definitive Guide. Cambridge, MA: MIT Press, 2016.
  • van der Aalst, Wil M. P. Process Mining: Data Science in Action, 2nd ed. Berlin: Springer, 2016. DOI 10.1007/978-3-662-49851-4. Cited in Ch. 7, Ch. 12

Capability security and authorization

  • Birgisson, Arnar; Joe Gibbs Politz; Úlfar Erlingsson; Ankur Taly; Michael Vrable; Mark Lentczner. “Macaroons: Cookies with Contextual Caveats for Decentralized Authorization in the Cloud.” In Network and Distributed System Security Symposium, 2014. DOI 10.14722/ndss.2014.23212. Cited in Ch. 5, Ch. 9, Ch. 12
  • Clever Cloud. “Biscuit Authorization.” Official specification and documentation, reviewed 17 October 2026. Cited in Ch. 5, Ch. 12
  • Myers, Andrew C. Mostly-Static Decentralized Information Flow Control. PhD thesis, Massachusetts Institute of Technology, 1999. Official MIT record. Introduces decentralized labels and principal-controlled declassification. Cited in Ch. 9, Ch. 12
  • Sabelfeld, Andrei; David Sands. “Declassification: Dimensions and Principles.” Journal of Computer Security 17, no. 5 (2009): 517–548. DOI 10.3233/JCS-2009-0352. Organizes controlled release around what, who, where, and when information may be released. Cited in Ch. 9, Ch. 12
  • Byun, Ji-Won; Elisa Bertino; Ninghui Li. “Purpose Based Access Control for Privacy Protection in Relational Database Systems.” CERIAS Technical Report 2004-52, Purdue University, 2004. Official record. Associates data with intended purposes, explicit prohibitions, and defined labeling granularity. Cited in Ch. 9, Ch. 12
  • Staicu, Cristian-Alexandru; Daniel Schoepe; Musard Balliu; Michael Pradel; Andrei Sabelfeld. “An Empirical Study of Information Flows in Real-World JavaScript.” 2019. arXiv:1906.11507. Provides empirical evidence for loss of permissiveness, label creep, and runtime cost under aggressive dynamic IFC. Cited in Ch. 9, Ch. 12
  • Siddiqui, Shoaib Ahmed; Radhika Gaonkar; Boris Köpf; David Krueger; Andrew Paverd; Ahmed Salem; Shruti Tople; Lukas Wutschitz; Menglin Xia; Santiago Zanella-Béguelin. “Permissive Information-Flow Analysis for Large Language Models.” 2024. arXiv:2410.03055. Propagates labels from influential inputs rather than assigning every output the most restrictive input label. Cited in Ch. 9, Ch. 12
  • Kravchenko, Arseny; Vadim Liventsev; Innokentii Konstantinov; Ildar Iskhakov; Matvey Kukuy. “Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents.” 2026. arXiv:2607.24625. APPA uses prospective enforcement, isolated child trajectories, registered sanitizers, and bounded return channels to contain taint without permanently disabling downstream work. Cited in Ch. 9, Ch. 12
  • Shim, Minsun; Ramisha Raida Karim; Ruthwik Jakkula; Kaiwen Zhou; Xin Liu; Xin Eric Wang; Zhou Li. “Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control.” 2026. arXiv:2609.14003. FLOWSEAL places provenance- and IFC-based enforcement outside the LLM Context and supports controlled declassification. Cited in Ch. 9, Ch. 12
  • Sharma, Reshabh K.; Linxi Jiang; Zhiqiang Lin; Shuo Chen. “PAuth — Precise Task-Scoped Authorization for Agents.” 2026. arXiv:2603.17170. Used as emerging evidence for purpose- and task-bound authorization. Cited in Ch. 5, Ch. 9, Ch. 12
  • Tallam, Krti. “Authorization Propagation in Multi-Agent AI Systems: Identity Governance as Infrastructure.” 2026. arXiv:2605.05440. Used as emerging evidence for transitive authorization and workflow-level identity governance. Cited in Ch. 5, Ch. 9, Ch. 12
  • Li, Tianxiao; Yixing Ma; Haiquan Wen; Zhenglin Huang; Qianyu Zhou; Zeyu Fu; Guangliang Cheng. “Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems.” 2026. arXiv:2605.10481. Introduces Constraint State Governance and constraint drift across memory, delegation, communication, tool use, audit, and optimization. Cited in Ch. 9, Ch. 12
  • Zou, Zhenhua; Sheng Guo; Qiuyang Zhan; Lepeng Zhao; Shuo Li; Zhuotao Liu. “AgentKernel: The Trust-Native Agentic Operating System.” 2026. arXiv:2609.29647. Proposes mandatory enforcement for identity, input mediation, memory governance, and execution control beneath agent runtimes. Cited in Ch. 9, Ch. 11, Ch. 12
  • Salas, Jesus. “Authority at Commit Time: Reject-and-Rerun Semantics for Governed Agentic Systems.” 2026. arXiv:2609.31490. Treats agents as proposal producers and an authoritative service as the commit authority for governed effects, with dependency checks, selective stale-work rejection, outbox dispatch, and reconciliation. Cited in Ch. 7, Ch. 9, Ch. 12
  • Santos-Grueiro, Igor. “Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents.” 2026. arXiv:2607.10487. Formalizes freshness, causal priority, effect binding, and eligibility of authority evidence at the durability boundary. Cited in Ch. 9, Ch. 12
  • Shariff, Parviz. “The Authority Resolution Framework: A Five-Domain Ontology for Governing Who and What Decides, at Scale.” 2026. arXiv:2608.15832. Represents actor, action, object, bounded context, and justification chain as authority-resolution semantics.
  • Das. “Computation Is Not Authority: Hardware-Enforced Execution-Finality for Agentic AI, MCP Tool Calls, and Industrial Agents.” IETF Internet-Draft, version 02, 9 September 2026. IETF Datatracker. Proposes a non-effective candidate-act state and independent finality check before consequential effects.
  • Shen, Zhihong. “Neither Layer Alone: Epistemic Integrity Requires Hierarchical Joint Design for Long-Running AI Agents.” 2026. arXiv:2606.04017. Frames persistent goal, belief, capability, and failure semantics as an explicit model–harness interface contract.

Long-term and personalized agent memory

  • Park, Young Bin. “Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures.” 2026. arXiv:2603.17244. Kumiho combines immutable revisions, typed dependency edges, and formal belief-revision semantics for memory and versioned agent-produced work. Cited in Ch. 4, Ch. 11, Ch. 12
  • Bhardwaj, Varun Pratap; Garima Singh; Arun Pratap Bhardwaj. “SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents.” 2026. arXiv:2608.08253. Governed local-first memory with admission, policy, multi-scope access, verifiable transactions, compensation, erasure, and audit. Cited in Ch. 4, Ch. 11, Ch. 12
  • Lacasse, Serge; Jérémie Hatier; Alex Baker. “Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture.” 2026. arXiv:2607.04391. Model- and storage-agnostic structured memory with symbolic, reproducible, inspectable retrieval. Cited in Ch. 4, Ch. 11, Ch. 12
  • Chen, Yaoqi; Haibin Lai; Yuru Feng; et al. “Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents.” 2026. arXiv:2606.06090. MAGE manages long-horizon state through a revisable hierarchical state tree. Cited in Ch. 4, Ch. 11, Ch. 12
  • Zhao, Haoyu; Zhengxu Yu; Zhiyuan He; et al. “Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks.” 2026. arXiv:2610.11794. Maintains revisable persistent semantic rulebooks, compiles them into executable models, and verifies updates while keeping the base LLM fixed. Cited in Ch. 10
  • Packer, Charles; Sarah Wooders; Kevin Lin; Vivian Fang; Shishir G. Patil; Ion Stoica; Joseph E. Gonzalez. “MemGPT: Towards LLMs as Operating Systems.” 2023. arXiv:2310.08560. Cited in Ch. 11
  • Rasmussen, Preston; Pavlo Paliychuk; Travis Beauvais; Jack Ryan; Daniel Chalef. “Zep: A Temporal Knowledge Graph Architecture for Agent Memory.” 2025. arXiv:2501.13956. Cited in Ch. 4, Ch. 11
  • Zhu, Jinchang; Jindong Li; Cheng Zhang; Jiahong Liu; Menglin Yang. “HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents.” In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, 13757–13769, 2026. DOI 10.18653/v1/2026.acl-long.625. Cited in Ch. 4, Ch. 10, Ch. 11, Ch. 12
  • Lan, Hongyi; Jiaqi Song; Zhengjia Zhong; Hui Li; Hong Liu; Xianming Lin; Rongrong Ji. “EMA: An Episodic Memory Agent for Efficient and Selective Memory.” In Findings of the Association for Computational Linguistics: ACL 2026, 5088–5102. DOI 10.18653/v1/2026.findings-acl.250. Cited in Ch. 4, Ch. 10, Ch. 11, Ch. 12
  • Sun, Haoran; Zekun Zhang; Shaoning Zeng. “Preference-Aware Memory Update for Long-Term LLM Agents.” In Findings of the Association for Computational Linguistics: ACL 2026, 783–793. DOI 10.18653/v1/2026.findings-acl.38. Cited in Ch. 4, Ch. 11
  • Zhang, Weizhi; Xinyang Zhang; Chenwei Zhang; et al. “PersonaAgent: Bridging Memory and Action for Personalized LLM Agents.” In Findings of the Association for Computational Linguistics: ACL 2026, 26421–26439. DOI 10.18653/v1/2026.findings-acl.1315. Cited in Ch. 4
  • Zhang, Jiaquan; Chaoning Zhang; Shuxu Chen; et al. “Lightweight LLM Agent Memory with Small Language Models.” In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, 12914–12929, 2026. DOI 10.18653/v1/2026.acl-long.588. Cited in Ch. 10

Recent semantic compilers and domain-specific IR systems

The following are public engineering artifacts and design proposals, not peer-reviewed papers. They are included because they materially narrow the manifesto's originality claims and provide direct contemporary precedents.

  • mronus. “Spec: A Language-Agnostic Intermediate Representation for Autonomous Software Development.” Design Proposal v0.2 and proof of concept, repository created 13 January 2026. Official GitHub repository. Defines language-agnostic software IR families, explicit Decisions and invariants, agent specialization, traceability, and a semantic-specification / language-implementation boundary. Cited in Ch. 11, Ch. 12
  • Cohesive Systems. “Cohesive Vision.” Normative accepted architecture document, last verified 12 August 2026. Official source. Defines persisted canonical software semantics, producer-independent authoring projections, deterministic lowering and acceptance, revisions, interpretation evidence, and human-governed acceptance. Cited in Ch. 11, Ch. 12
  • CogFoundry Labs. “LoomLoom: Compile AI Work into Software.” Public repository and TemplateSpec / IR documentation, 2026. Official repository and IR specification. Defines an AI work compiler and reusable workflow/execution IR with typed dataflow, dependency-aware execution, model/tool policy, memory, recovery, budgets, validation, and artifacts. Cited in Ch. 11, Ch. 12
  • BlocUnited LLC / Mozaiks. “ADR 0007: Generalized Semantic Compiler.” Accepted 26 August 2026. Official ADR. Defines an immutable canonical SemanticGraph, ImplementationBinding, derived CompilationPlan, deterministic render/validation/promotion, and a boundary between agent candidate content and semantic authority. Cited in Ch. 11, Ch. 12
  • saengha. “Software Control IR.” Experimental semantic intermediate representation for AI-controlled software, repository created 13 September 2026. Official repository. Defines structured state and actions, validation before mutation, effect revisions, host-divergence detection, synchronization, named recovery, and actual-host observation after operations. Cited in Ch. 11, Ch. 12

Relevance to the manifesto

These sources establish direct prior art for domain-specific canonical semantic IR, compiled AI work, deterministic semantic lowering, proposal/acceptance boundaries, immutable revision lineage, and effect reconciliation. They do not individually define the domain-independent agent architecture proposed here, but they must be cited wherever the manifesto distinguishes semantic Work IR from workflow, software, application-generation, and software-control IRs.

Semantic persistence and workflow/state representations

  • LoopX Project maintainers. “RFC: Capable Agent Manager and Semantic Work Handoff (v0).” Accepted RFC, created 13 September 2026, last normative revision 4 October 2026. Official GitHub source. Defines semantic continuation of purpose, context, Decisions, constraints, Evidence, Commitments, expected return, and authoritative work transitions across persistent agent collaboration. Cited in Ch. 5, Ch. 11, Ch. 12
  • Quinto, Emanuele; Carlo Andrea Rozzi; Francesco Zanitti. “Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows.” 2026. arXiv:2607.08740. Represents workflow definitions, instances, inference records, context snapshots, and dependencies as persistent knowledge objects and distinguishes deterministic derivation from mediated inference. Cited in Ch. 1, Ch. 11, Ch. 12
  • Wang, Zhaohui Geoffrey. “Persistent Semantic Entities in Tool-Augmented LLM Systems.” In Proceedings of the 43rd International Conference on Machine Learning, PMLR 306 (2026): 129420–129451. Official PMLR record. This is a security analysis of persistent hidden contamination, not a semantic-agent architecture; it is included to prevent terminology confusion with the manifesto's title. Cited in Ch. 14

Graph Engineering and agent operating systems

  • Feng, Yuyuan; Zhishang Xiang; Chaobin Yang; et al. “Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence.” 21 August 2026. arXiv:2608.21156. The closest contemporary framing of the transition from individual to system intelligence; cited as emerging direction, not as the complete target architecture. Cited in Ch. 1, Ch. 7, Ch. 11
  • Mei, Kai; Xi Zhu; Wujiang Xu; Wenyue Hua; Mingyu Jin; Zelong Li; Shuyuan Xu; Ruosong Ye; Yingqiang Ge; Yongfeng Zhang. “AIOS: LLM Agent Operating System.” 2024. arXiv:2403.16971. Cited in Ch. 11

Current agent systems and engineering evidence

Interoperability protocols and extension systems

Research-use notes

Strong direct foundations

The most load-bearing academic sources for the architecture are Bratman; HTN and least-commitment planning; NASA logical decomposition; Doyle and de Kleer; Soar; W3C PROV; Singh's social commitments; Contract Net; distributed cognition; metacognition; and capability attenuation.

Contemporary convergence evidence

Graph Engineering, Agents API, Managed Agents, Cursor Projects, LangGraph/Deep Agents, MCP, A2A, AIOS, and recent memory work establish the current movement toward durable, coordinated, structured, and long-horizon systems. They are not cited as having already produced the full synthesis.

Sources intentionally excluded

Generic SEO articles, vendor comparisons, secondary “agent trends” posts, and uncited architectural diagrams were excluded where primary sources were available.

Maintenance rule

Before public release, rerun link and metadata validation for sources published or materially updated after 1 January 2026. Record the new research cutoff in the manifesto version history. Do not silently rewrite historical comparison claims after publication; revise them with a dated version and change note.

Version 1.0 · Published 19 October 2026 · Copyright © 2026 Lukman Nuriakhmetov. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).