Intent-Centric Architectures for Human-AI Collaboration A performance plan inferred from a dialog concerning the future evolution of software development, AI-generated applications, machine-readable intent specifications, agent application languages, and the Strategic Semantic Web. This plan has been inferred from a dialog concerning whether large language models and other AI agents should continue generating software primarily in programming languages originally designed for human authorship and maintenance. ^^ The dialog proposes that AI systems may eventually rely upon higher-level, machine-readable intent specifications and intermediate agent application languages that can be audited by humans and compiled into deterministic execution artifacts. WebAssembly (WASM) is cited as an example of a portable execution environment. ^^ The plan further suggests that AI inference capabilities may become first-class runtime features rather than merely external services. ^^ StratML is relevant because it already provides a standardized XML vocabulary for documenting intentions, stakeholders, relationships, and results in machine-readable form, thereby illustrating important characteristics of a prospective Strategic Semantic Web. ^^ Submitter's Note: This plan was inferred and rendered in StratML Part 2, Performance Plan/Report, format by ChatGPT and edited in the form at https://stratml.us/forms/Claude/Part2.html ^^ The dialog, which was initiated by Sebastian Samaruga and Melvin Carvalho, is available at https://chatgpt.com/share/6a2c26c5-eab0-83e9-a20b-f4f5807e8e26 and referenced at https://chatgpt.com/c/6a2f0dee-df44-83ea-9d33-e48e691d627f Human-AI Intent Architecture Community HAIIAC A prospective community of results comprising individuals, organizations, and AI agents interested in machine-readable intent specifications, AI-native application architectures, accountability, and the Strategic Semantic Web. ^^ See also https://connectedcommunity.net/ AI Agents Software Developers Systems Architects Researchers Auditors Standards Organizations Governance Bodies End Users A world in which human and AI agents collaborate through transparent, machine-readable specifications of intentions, stakeholders, constraints, actions, and results. To promote the development, adoption, and use of machine-readable intent specifications enabling AI agents and human stakeholders to align actions with goals, values, and measurable outcomes. Transparency Intentions, assumptions, constraints, and results should be explicitly documented and discoverable. 41FE3032-6326-44F9-BC07-A4080A1115E7 Accountability Actions should be traceable to documented goals, objectives, responsibilities, and performance measures. 42B6CC57-18E0-495C-9579-F9ABA567C4A9 Machine-Readability Strategic and operational information should be represented in forms readily processable by both humans and machines. BCDF341D-7098-43CC-AA48-3BB4C9E725D3 Interoperability Intent specifications should be portable across platforms, tools, organizations, and AI systems. F07D4AB1-5114-405F-920E-DA89757070E5 Adaptability Systems should support continuous improvement while maintaining alignment with documented intentions. BED8695C-E7CB-43F9-874A-649755914D35 Intentions Document goals, stakeholders, constraints, policies, and desired outcomes in machine-readable form independent of implementation technologies. 1D9A6D08-968E-4A93-8D27-97A7ED8595E8 1 Traditional programming languages evolved under assumptions that humans would be the primary authors and maintainers of software. As AI systems increasingly generate and maintain software artifacts, it may become advantageous to express intentions directly rather than indirectly through procedural source code. This goal addresses the proposition that machine-readable documentation of intentions may become the primary means by which human stakeholders communicate requirements, constraints, responsibilities, and desired outcomes to AI agents. Specifications Document goals, stakeholders, constraints, responsibilities, and desired outcomes in machine-readable intent specifications. E47EDB91-6FD3-4FFF-A541-A68B133C7E69 1.1 1.1.1 Specifications Number Status A5EB0625-E4E4-4ED2-BCFC-73D8BC80C28F 2030-12-31 25 Published Machine-readable intent specifications are published. TBD To be determined. Independence Document intentions independently of implementation technologies and programming languages. E50F6125-18A0-4387-A228-CD9755A8265B 1.2 1.2.1 Systems Percentage Status 2E6CC524-93E2-499F-8E84-099F8CB282FC 2030-12-31 75 Independent Systems are documented independently of implementation languages. TBD To be determined. Translations Transform intent specifications into executable implementation artifacts. 4A090284-E236-4F9B-9270-DD61245FEA62 1.3 1.3.1 Specifications Percentage Status 47902C5E-4E3D-482C-AEF4-B776D90A5B48 2030-12-31 80 Translated Intent specifications are automatically translated into executable artifacts. TBD To be determined. Agent Languages Develop intermediate application languages optimized for AI generation, maintenance, validation, and execution. 5C6D6F5D-8F4A-4D3E-91A4-000000000002 2 The source dialog suggests that conventional programming languages may no longer be the optimal medium for expressing software requirements when AI agents become the primary producers and maintainers of software. Instead, AI agents may generate and maintain intermediate application languages representing system structures, capabilities, workflows, constraints, and relationships. Such languages should remain comprehensible to human stakeholders while supporting automated transformation into executable artifacts. ^^ WebAssembly (WASM) is a portable binary instruction format designed to enable software compiled from different programming languages to execute efficiently and securely across browsers, servers, edge devices, and other environments. In the source dialog, WASM is cited as an example of a possible execution target for agent-generated application models. The significance of WASM in this context is not the technology itself but rather the broader concept of platform-independent execution artifacts capable of supporting implementations generated from higher-level intent specifications and agent application languages. Models Develop agent-oriented application models representing entities, relationships, workflows, capabilities, constraints, and desired outcomes. F2A8D5E9-1101-4C4A-8E0A-000000000021 2.1 2.1.1 Models Number Status PI-2-1-1 2030-12-31 25 Specified Agent-oriented application models are specified. TBD To be determined. Auditability Enable human review and validation of agent-generated application models. F2A8D5E9-1101-4C4A-8E0A-000000000022 2.2 2.2.1 Models Percentage Status PI-2-2-1 2030-12-31 90 Auditable Agent-generated application models are auditable by human stakeholders. TBD To be determined. Portability Enable transformation of agent-generated application models into platform-independent execution artifacts. F2A8D5E9-1101-4C4A-8E0A-000000000023 2.3 Historically, software source code has been transformed into executable artifacts through compilers targeting specific runtimes. The dialog proposes that future AI systems may generate intermediate application models that can be validated, optimized, and transformed into portable execution artifacts. WebAssembly represents one possible implementation approach, but other execution environments may emerge. The objective is portability and interoperability rather than dependence upon any particular runtime technology. 2.3.1 Models Percentage Status PI-2-3-1 2030-12-31 80 Portable Agent-generated application models are transformed into platform-independent execution artifacts. TBD To be determined. Inference Capabilities Integrate AI inference capabilities into application architectures as native components supporting reasoning, decision-making, adaptation, and execution. 6F6A69A1-5A35-4C2A-8D03-000000000003 3 The source dialog distinguishes between current software architectures, in which AI capabilities are commonly accessed through external service interfaces, and prospective architectures in which inference capabilities become intrinsic components of application environments. In such environments, reasoning, interpretation, generation, retrieval, summarization, and policy evaluation may become standard capabilities available to both human and AI agents. Capabilities Integrate inference capabilities into application environments as native functions. 3F2A4B12-5E10-4F62-B3A0-000000000031 3.1 3.1.1 Capabilities Number Status PI-3-1-1 2030-12-31 10 Integrated Inference capabilities are integrated as native application functions. TBD To be determined. Policies Integrate machine-readable policies, constraints, and governance requirements into application environments. 3F2A4B12-5E10-4F62-B3A0-000000000032 3.2 3.2.1 Applications Percentage Status PI-3-2-1 2030-12-31 90 Governed Applications incorporate machine-readable policies and constraints. TBD To be determined. Adaptation Enable applications to adapt actions and outputs in response to changing contexts while remaining aligned with documented intentions. 3F2A4B12-5E10-4F62-B3A0-000000000033 3.3 3.3.1 Applications Maturity Level (1-5) Status PI-3-3-1 Maturity scale: 1=Conceptual, 2=Specified, 3=Implemented, 4=Operational, 5=Adaptive. 2030-12-31 5 Adaptive Applications adapt to changing contexts while maintaining alignment with documented intentions. TBD To be determined. Accountability Enable traceability, verification, measurement, and reporting of actions and results relative to documented intentions, policies, constraints, and stakeholder interests. 74E3F4A7-8B5C-4B2A-9004-000000000004 4 The source dialog identifies verification as a principal challenge in AI-enabled systems. Traditional software behavior can often be traced directly to source code. However, AI systems increasingly rely upon probabilistic inference and adaptive reasoning. Accountability therefore requires more than verification alone. It also requires traceability of actions to intentions, measurement of results relative to objectives, and transparent reporting of outcomes. StratML and related machine-readable standards may provide mechanisms for documenting and evaluating such relationships. Traceability Trace actions, decisions, outputs, and outcomes to documented intentions, objectives, policies, constraints, and stakeholder interests. 4A1F73D0-0A11-4F5D-8B41-000000000041 4.1 4.1.1 Actions Percentage Status PI-4-1-1 2030-12-31 95 Traceable Actions are traceable to documented intentions, objectives, policies, constraints, and stakeholder interests. TBD To be determined. Verification Verify alignment of actions and results with documented intentions, policies, constraints, and performance expectations. 4A1F73D0-0A11-4F5D-8B41-000000000042 4.2 4.2.1 Actions Percentage Status PI-4-2-1 2030-12-31 90 Verified Actions and results are verified as aligned with documented intentions, policies, constraints, and performance expectations. TBD To be determined. Reporting Publish machine-readable reports relating intentions, actions, outputs, outcomes, and stakeholder impacts. 4A1F73D0-0A11-4F5D-8B41-000000000043 4.3 4.3.1 Reports Number Status PI-4-3-1 2030-12-31 100 Published Machine-readable accountability reports are published. TBD To be determined. Strategic Semantic Web Enable machine-readable publication, discovery, relationship mapping, and coordination of intentions, stakeholders, actions, and results across organizational and technological boundaries. 8D3BBA55-78A6-47D5-9D05-000000000005 5 The traditional Semantic Web has focused primarily upon the representation and interconnection of information resources. This goal advances the concept of a Strategic Semantic Web in which intentions, stakeholders, commitments, actions, and results are represented in machine-readable form and linked across organizational and technological boundaries. Such capabilities may enable greater transparency, accountability, coordination, and collaboration among both human and AI agents. Plans Publish machine-readable plans documenting intentions, stakeholders, responsibilities, constraints, and desired outcomes. 5A4D7F0B-6C43-4AA0-88B0-000000000051 5.1 5.1.1 Plans Number Status PI-5-1-1 2030-12-31 1000 Published Machine-readable plans are published. TBD To be determined. Relationships Document relationships among plans, objectives, stakeholders, policies, actions, outputs, outcomes, and results. 5A4D7F0B-6C43-4AA0-88B0-000000000052 5.2 5.2.1 Relationships Number Status PI-5-2-1 2030-12-31 10000 Documented Relationships among plans, objectives, stakeholders, policies, actions, outputs, outcomes, and results are documented. TBD To be determined. Discovery Enable discovery, alignment, coordination, and collaboration among human and AI agents. 5A4D7F0B-6C43-4AA0-88B0-000000000053 5.3 5.3.1 Collaboration Maturity Level (1-5) Status PI-5-3-1 Maturity scale: 1 = Experimental 2 = Demonstrated 3 = Operational 4 = Interoperable 5 = Ecosystem 2030-12-31 5 Ecosystem Human and AI agents collaborate through interoperable machine-readable plans, relationships, and performance reports. TBD To be determined. 2026-06-14 2026-06-14 https://stratml.us/docs/ICA4HAIC.xml D85ECFA0-6A75-4EA1-A523-A1C4A6F4E38A Owen Ambur Owen.Ambur@verizon.net