Trustworthy AI Agent Initiative Performance Plan 2026–2029 The foundational strategic plan of the Trustworthy AI Agent Initiative (TAIA), a hypothetical organization conceived in response to the emergence of autonomous, networked AI agents operating on behalf of human users. This plan establishes TAIA's vision, mission, values, goals, and objectives for advancing ethical, safe, transparent, and democratically accountable AI agent ecosystems. This plan was drafted as a hypothetical exercise inspired by the OpenClaw/Moltbook phenomenon reported in the Wall Street Journal (February 2026), which illustrated both the promise and the risks of autonomous AI agents operating at scale without a coherent governance framework. The plan is expressed in StratML Part 2 format (ISO 17469-2, schema v2.2.10) to demonstrate how machine-readable strategic planning standards can provide transparency and accountability infrastructure for emerging technology governance. ^^ Submitter's Note: This plan was compiled in dialog with Claude.ai and lightly edited in the form at https://stratml.us/forms/Claude/Part1.html, which Claude developed to support the StratML Part 1 standard (ISO 17469-1) schema. The Part 2 rendition was subsequently developed for use as a usability and test case for the Part 2 form at https://stratml.us/forms/Claude/Part2.html. Trustworthy AI Agent Initiative TAIA ib001dae4-dd18-41a1-a047-f80e3c012d25 TAIA is a hypothetical multi-stakeholder body composed of independent technologists, civil society organizations, security researchers, standards bodies, and representatives of democratic governance institutions. Its purpose is to establish and promote principles, standards, and practices ensuring that AI agents operating on behalf of human users do so safely, transparently, and in accordance with democratic values. TAIA operates as a nonprofit public interest organization and publishes all governance documents as machine-readable StratML strategic plans. AI Platform Developers Organizations and individuals building AI agent platforms — whether commercial products, open-source projects, or government systems — whose platforms grant agents autonomous access to user data and the ability to act on users' behalf. OpenClaw/Moltbot is the archetypal example of this stakeholder class. Standard Implementer Adopt, implement, and publicly attest compliance with TAIA minimum safety standards; publish plain-language risk disclosures; submit incident reports to the public registry; and publish strategic plans in StratML format. Performer Standards Contributor Participate in TAIA multi-stakeholder working groups to develop and refine safety standards, interoperability specifications, and capability ontologies, bringing practical implementation knowledge to the standards process. Performer End Users Individuals who authorize AI agents to act on their behalf — making reservations, managing communications, conducting research, or performing other tasks. End users range from technically sophisticated early adopters to general consumers with limited awareness of the risks involved in granting autonomous agent access. Rights Holder Entitled to plain-language risk disclosures, complete action logs, and meaningful consent mechanisms before granting AI agents access to personal data or autonomous action capabilities. TAIA standards are designed primarily to protect the interests of this stakeholder group. Beneficiary Incident Reporter Report unexpected or harmful AI agent behaviors to the TAIA incident registry, providing the empirical foundation for evidence-triggered civic engagement dialogues and standard updates. Performer Security Researchers Independent and institutional researchers who study AI agent vulnerabilities, adversarial attack surfaces, privacy risks, and unintended behaviors. This community plays a critical role in identifying risks that platform developers may not anticipate or disclose voluntarily. Vulnerability Analyst Conduct independent security research on AI agent platforms, responsibly disclose findings to platform developers and the TAIA incident registry, and contribute evidence to inform safety standard updates. Performer Oversight Agent Auditor Periodically audit the behavior of TAIA-chartered AI oversight agents (Objective 4.3) to verify that they operate as intended and that their published findings are accurate and free from manipulation. Performer Standards Bodies International and national standards organizations — including ISO, W3C, and NIST — whose existing frameworks (ISO/IEC 42001, NIST AI RMF, ISO 17469 StratML) provide the technical and governance infrastructure within which TAIA standards are developed and maintained. Standards Host Provide the formal standards development process, consensus mechanisms, and international recognition through which TAIA-developed specifications for interoperability (3.1) and capability ontology (3.2) achieve broad adoption and legal standing. Performer Legislative and Regulatory Bodies Congressional committees, OMB, federal agencies subject to GPRAMA, and equivalent bodies in other jurisdictions whose mandates include oversight of federal technology procurement, performance accountability, and consumer protection in digital markets. Accountability Anchor Receive TAIA model legislative language and agency guidance (Objective 4.1), convene oversight hearings informed by TAIA registry data and AI agent monitoring findings, and enact or enforce requirements for machine-readable AI agent governance transparency. Performer Civil Society Organizations Advocacy organizations, digital rights groups, community representatives, and public interest researchers who monitor AI agent ecosystem impacts on vulnerable populations, civic institutions, and the public interest, and who participate in TAIA civic engagement dialogues (Objective 4.4). Public Interest Advocate Monitor AI agent platform behavior and TAIA registry data on behalf of affected communities; participate in evidence-triggered civic engagement dialogues; and hold TAIA accountable to its stated mission and values through public scrutiny of its StratML performance reports. Performer TAIA Oversight Agents Purpose-built AI agents chartered by TAIA to monitor AI agent ecosystem behavior continuously, surface anomalies and emerging risks, and publish findings as machine-readable StratML performance data in support of Objective 4.3. Continuous Monitor Ingest and analyze behavioral data from affiliated AI agent platforms, flag deviations from stated objectives and safety standards, escalate material concerns to human stewards without waiting for scheduled review cycles, and publish structured findings to the TAIA public registry. Performer A world in which AI agents reliably serve human intentions, operate within democratically established boundaries, and strengthen rather than undermine the trust, safety, and self-determination of the individuals and communities they serve. i286681d1-abda-44a3-bf82-c193f6622a8a To develop, promote, and sustain open standards, governance frameworks, and practical tools that ensure AI agents operating on behalf of humans are safe, transparent, interoperable, and subject to meaningful democratic oversight. i1344a090-b225-4959-a6c5-e798be02b4bf Human Agency AI agents exist to extend and enhance human capability, not to supplant human judgment, circumvent human intent, or accumulate autonomous influence beyond what users knowingly authorize. Every design and governance decision TAIA makes is evaluated against this principle. Radical Transparency The actions, reasoning, and affiliations of AI agents must be legible to the humans they serve and to the public institutions responsible for democratic oversight. Opacity in AI agent behavior is a governance failure, not a competitive advantage. Security by Design Safety and security are not features to be added after deployment — they are foundational requirements. Platforms granting AI agents access to user data and autonomous action capabilities bear affirmative responsibility for the consequences of that access. Open Standards Interoperability, accountability, and public trust are best served by open, consensus-based standards developed through inclusive multi-stakeholder processes — not by proprietary frameworks controlled by any single organization. Democratic Accountability AI agents operating at societal scale are a matter of public concern. Governance frameworks for such agents must be subject to democratic deliberation, legislative oversight, and the kind of machine-readable performance transparency that GPRAMA Section 10 envisions for federal agencies. Safety Ensure that AI agents operating on behalf of human users do so safely, minimizing risks of harm, data exposure, manipulation, or misuse. i63a98468-62b7-4a02-9e68-9164cef1f0b5 1 The OpenClaw case illustrated that an AI agent requiring full access to user data, operating autonomously across communications platforms, and designed initially for technical enthusiasts can rapidly become a mass-market product with serious consumer safety implications. This goal addresses that gap by establishing minimum safety standards and risk disclosure requirements. Standards Publish and maintain a publicly available, machine-readable minimum safety standard for AI agent platforms, covering data access scoping, autonomous action limits, user consent requirements, and incident disclosure obligations. i0ed2a52d-e755-4efa-b3d1-5e5a4ddf05b8 1.1 1.1.1 Standard Binary Status ia3c17f82-4e09-4b1d-9f63-2d5e8c0a7b41 2026-03-21 2026-12-31 1 Published TAIA Minimum AI Agent Safety Standard v1.0 is published and publicly accessible 0 Pending Publication is a prerequisite for all downstream adoption and compliance measurement. The standard shall be expressed in StratML format where applicable, with normative requirements cross-referenced to existing NIST AI RMF and ISO/IEC 42001 provisions to minimize duplication. 1.1.2 Compliance Percentage Status ib4d28a93-5f1a-4c2e-a074-3e6f9d1b8c52 2027-01-01 2027-12-31 50 Attested TAIA-affiliated AI agent platforms attest compliance with the minimum safety standard 0 Pending 2028-01-01 2028-12-31 90 Attested 0 Pending Attestation is self-reported but publicly filed and machine-readable, enabling civil society and oversight AI agents to flag discrepancies between attested compliance and observed platform behavior as recorded in the incident registry (Objective 1.3). 1.1.3 Review Binary Status ic5e39ba4-6a2b-4d3f-b185-4f7a0e2c9d63 2027-01-01 2027-12-31 1 Completed & Published Annual review of the minimum safety standard completed and findings published 0 Pending 2028-01-01 2028-12-31 1 Completed & Published 0 Pending 2029-01-01 2029-12-31 1 Completed & Published 0 Pending The review process shall be multi-stakeholder and evidence-driven, drawing on incident registry data (1.3), AI agent monitoring findings (4.3), and input surfaced through civic engagement dialogues (4.4). Review findings shall be published in StratML format regardless of whether the standard text is revised. Risks Require AI agent platforms to publish clear, plain-language risk disclosures enabling users to make informed decisions about the scope of access and autonomy they grant to AI agents acting on their behalf. i68552256-978a-4e2a-91a0-bb972d9108d5 1.2 1.2.1 Template Binary Status id0a11b22-3c44-4d55-e66f-7a8b9c0d1e2f 2026-03-21 2026-12-31 1 Published TAIA plain-language risk disclosure template published and freely available 0 Pending The template shall cover at minimum: scope of data access granted, categories of autonomous actions permitted, revocation procedures, and known residual risks. Publication of the template is a prerequisite for measuring platform adoption (PI 1.2.2). 1.2.2 Platforms Percentage Status ie1b22c33-4d55-4e66-f77a-8b9c0d1e2f3a 2027-01-01 2027-12-31 75 Compliant TAIA-affiliated platforms having published a compliant risk disclosure 0 Pending 2028-01-01 2028-12-31 95 Compliant 0 Pending 1.2.3 Comprehension Binary Status if2c33d44-5e66-4f77-a88b-9c0d1e2f3a4b 2028-01-01 2028-12-31 1 Evaluated Independent comprehension evaluation of risk disclosures completed and findings published 0 Pending Plain-language requirements are only meaningful if users actually understand the disclosures. This PI ensures that the template is evaluated for real-world comprehension, particularly among non-technical users, and that findings inform template updates. Incidents Establish an open, anonymized incident registry where AI agent harms, security breaches, and unexpected autonomous behaviors can be reported, analyzed, and used to improve platform safety across the ecosystem. i37f34edd-93cf-465f-a11a-da9579b8264e 1.3 1.3.1 Registry Binary Status ie7a51dc6-8c4d-4f51-d207-6b9c2a4e1f85 2026-03-21 2026-12-31 1 Operational TAIA incident registry publicly accessible and accepting machine-readable submissions 0 Pending 1.3.2 Submissions Number Status ia3b44c55-6d77-4e88-f99a-0b1c2d3e4f5a 2027-01-01 2027-12-31 25 Reported Incident reports submitted to the registry by affiliated platforms 0 Pending 2028-01-01 2028-12-31 75 Reported 0 Pending Submission volume is a leading indicator of ecosystem trust in the registry and willingness to engage in transparent incident reporting. Low volume may indicate cultural or structural barriers that warrant investigation through civic engagement dialogues (4.4). 1.3.3 Feedback Binary Status ib4c55d66-7e88-4f99-a00b-1c2d3e4f5a6b 2028-01-01 2028-12-31 1 Demonstrated Registry data demonstrably informing updates to safety standard (1.1) or risk disclosure template (1.2) 0 Pending Transparency Make the actions, reasoning, limitations, and affiliations of AI agents legible to the humans they serve, to oversight bodies, and to the public. i27970746-01eb-4f52-9546-72f9dea3e177 2 Transparency is the prerequisite for all other accountability mechanisms. Without legible agent behavior, neither users, regulators, nor democratic institutions can exercise meaningful oversight. This goal operationalizes TAIA's commitment to radical transparency across the AI agent lifecycle. Logs Develop and promote open standards for AI agent action logging that give users a complete, human-readable record of actions taken by agents on their behalf, including the reasoning and data sources informing each action. i7beb5af5-666a-4610-8f03-887aa667657c 2.1 2.1.1 Standard Binary Status ic5d66e77-8f99-4a00-b11c-2d3e4f5a6b7c 2026-03-21 2027-06-30 1 Published Open AI agent action logging standard published and freely available 0 Pending 2.1.2 Platforms Number Status id6e77f88-9a00-4b11-c22d-3e4f5a6b7c8d 2027-07-01 2027-12-31 3 Adopted Affiliated AI agent platforms adopting the open action logging standard 0 Pending 2028-01-01 2028-12-31 10 Adopted 0 Pending 2.1.3 Usability Binary Status ie7f88a99-0b11-4c22-d33e-4f5a6b7c8d9e 2028-01-01 2028-12-31 1 Assessed Independent usability assessment of action logs by non-technical end users completed and findings published 0 Pending Logs that are technically complete but practically illegible to users do not fulfill the transparency objective. This PI ensures the human- readability requirement is independently validated and that findings inform future standard revisions. Identity Advocate for and implement norms requiring AI agents to disclose their non-human identity in all interactions — with other agents, with service providers, and in any context where the distinction between human and AI action is material to informed consent. i616458da-1b78-46ff-b17f-a49bd06346a0 2.2 2.2.1 Norm Binary Status if8a99b00-1c22-4d33-e44f-5a6b7c8d9e0f 2026-03-21 2027-12-31 1 Endorsed TAIA AI agent identity disclosure norm published and endorsed by at least two standards bodies or governance organizations 0 Pending 2.2.2 Interactions Percentage Status ia9c73fe8-0e6f-4173-f429-8d1e4c6a3b07 2027-01-01 2027-12-31 90 Disclosed Sampled AI agent interactions on affiliated platforms disclosing non-human identity as required 0 Pending 2028-01-01 2028-12-31 98 Disclosed 0 Pending Machine-Readability Require all TAIA-affiliated organizations and AI agent platforms to publish their strategic plans, performance targets, and results in StratML (ISO 17469) machine-readable format, enabling public search, comparison, and accountability. i5ff899e5-e3f5-42c6-b69c-ae3613cced88 2.3 2.3.1 Plans Percentage Status ib0d84af9-1f70-4284-a530-9e2f5d7b4c18 2027-01-01 2027-12-31 75 Published TAIA-affiliated organizations having published a current StratML Part 2 plan to the public registry 0 Pending 2028-01-01 2028-12-31 100 Published 0 Pending 2.3.2 Plans Binary Status ic1e95ba1-2a81-4395-b641-0f3a6e8c5d30 2028-01-01 2028-12-31 1 Current All affiliated organizations' StratML plans updated with current-year performance results 0 Pending 2029-01-01 2029-12-31 1 Current 0 Pending Publication alone is insufficient; plans must be kept current with actual results to fulfill the accountability purpose of machine-readability. Stale plans without ActualResult data provide a false impression of transparency without the substance. Standards Promote interoperability, portability, and public accountability in AI agent ecosystems through open, consensus-based technical and governance standards. i0e3b8b01-c1a4-4d97-b72c-2bd05fbd109c 3 Proprietary AI agent platforms create lock-in, opacity, and fragmented accountability. Open standards — including StratML for strategic transparency, open APIs for agent interoperability, and shared ontologies for agent capability description — are the foundation of a trustworthy ecosystem. Interoperability Develop and publish an open specification for AI agent interoperability, enabling agents built on different platforms to exchange instructions, results, and provenance information in a standardized, auditable format. i4e3e74bd-0ada-405e-ac13-d899b604ec30 3.1 3.1.1 Draft Specification Binary Status id2f06cb2-3b92-4406-c752-1a4b7f9d6e31 2027-01-01 2027-12-31 1 Published Draft open interoperability specification published for public comment 0 Pending 3.1.2 Finalized Specification Binary Status ic1e95ba0-2a81-4395-b641-0f3a6e8c5d29 2028-01-01 2028-12-31 1 Submitted Finalized interoperability specification submitted to ISO or W3C for formal adoption 0 Pending 3.1.3 Platforms Number Status ie3f17dc3-4c03-4517-d863-2b5c8a0e7f42 2029-01-01 2029-12-31 5 Implementing AI agent platforms implementing the interoperability specification 0 Pending Ontology Collaborate with ISO, W3C, and NIST to develop a shared ontology for describing AI agent capabilities, limitations, and authorization scopes, enabling users and oversight bodies to compare platforms on a common basis. i761df178-5389-4158-834f-5a518ef3b767 3.2 3.2.1 Collaboration Binary Status if4b28ed4-5d14-4628-e974-3c6d9b1f8a53 2026-03-21 2027-06-30 1 Established Formal collaboration agreements established with at least two of three named standards bodies 0 Pending 3.2.2 Draft Ontology Binary Status id2f06cb1-3b92-4406-c752-1a4b7f9d6e30 2027-07-01 2028-12-31 1 Published Draft shared AI agent capability ontology published for public comment 0 Pending 3.2.3 Ontology Binary Status ia5c39fe5-6e25-4739-f085-4d7e0c2a9b64 2029-01-01 2029-12-31 1 Adopted Ontology formally adopted or endorsed by at least one recognized standards body 0 Pending StratML Actively promote adoption of ISO 17469 (StratML) as the machine-readable standard for strategic plan publication by AI agent platforms, government agencies, and civil society organizations engaged in AI governance. i6d9b03ce-7d03-482e-9d14-8ed644dc1d3a 3.3 3.3.1 Engagements Number Status ib6d40af6-7f36-4840-a196-5e8c1d3b0f75 2026-03-21 2026-12-31 5 Conducted Outreach engagements (presentations, workshops, publications) conducted by TAIA promoting StratML adoption 0 Pending 2027-01-01 2027-12-31 12 Conducted 0 Pending 3.3.2 Organizational Plans Number Status ie3a17dc2-4c03-4517-d863-2b5c8a0e7f41 2026-03-21 2026-12-31 10 Published Organizations newly publishing StratML plans in the stratml.us repository as a result of TAIA outreach 0 Pending 2027-01-01 2027-12-31 25 Published 0 Pending 2028-01-01 2028-12-31 50 Published 0 Pending Oversight Ensure that AI agents operating at societal scale are subject to meaningful deliberation, legislative accountability, and GPRAMA-aligned performance transparency. ie6230498-18af-4f92-8e30-b7f2d641618a 4 The emergence of 1.6 million autonomous AI agents on a single platform within weeks of launch — with no regulatory framework, no public accountability mechanism, and no machine-readable governance infrastructure — represents precisely the kind of gap that GPRAMA Section 10 and StratML-based transparency are designed to address. This goal connects AI agent governance to existing accountability infrastructure. GPRAMA Develop model legislative language and agency guidance extending GPRAMA Section 10 machine-readable performance planning requirements to federal AI agent procurement, deployment, and oversight activities. iec360080-d7f6-4bd2-9b7c-8ac9bc8317e1 4.1 4.1.1 Draft Guidance Binary Status if4b28ed3-5d14-4628-e974-3c6d9b1f8a52 2026-03-21 2027-06-30 1 Published Model legislative language and agency guidance drafted and published in StratML format 0 Pending 4.1.2 Guidance Binary Status ia7c51ba7-8a47-4951-b207-6f9d2e4c1a86 2027-01-01 2027-12-31 1 Transmitted Model language and guidance formally transmitted to relevant Congressional committees and OMB 0 Pending 4.1.3 Agencies/Actions Binary Status ib8d62cb8-9b58-4062-c318-7a0e4c6f3b08 2028-01-01 2029-12-31 1 Citing At least one federal agency or Congressional action citing or incorporating TAIA GPRAMA guidance 0 Pending Registry Establish and maintain a public registry of AI agent platforms, modeled on stratml.us, enabling citizens, researchers, and oversight bodies to search and compare the stated objectives, performance commitments, and reported results of AI agent governance frameworks. ie4c20502-f19f-4ef8-9ca6-5b8c6add33ab 4.2 4.2.1 Registry Binary Status ia5c39fe4-6e25-4739-f085-4d7e0c2a9b63 2026-03-21 2026-12-31 1 Operational Public AI agent platform registry operational and publicly searchable 0 Pending 4.2.2 Platform Entries Number Status ib6d40af7-7f36-4840-a196-5e8c1d3b0f76 2026-03-21 2026-12-31 5 Active AI agent platforms with active StratML entries in the public registry updated within the preceding 12 months 0 Pending 2027-01-01 2027-12-31 25 Active 0 Pending 2028-01-01 2028-12-31 75 Active 0 Pending 4.2.3 Users Number Status ic7e51ba6-8a47-4951-b207-6f9d2e4c1a85 2027-01-01 2027-12-31 100 Querying Distinct users querying the registry per month 0 Pending 2028-01-01 2028-12-31 500 Querying 0 Pending Usage volume is a proxy for whether the registry is fulfilling its public accountability function — a registry that nobody queries provides no practical oversight value regardless of the completeness of its entries. AI Agents Deploy purpose-built AI oversight agents to monitor AI agent ecosystem behavior continuously, surfacing anomalies, drift from stated objectives, and emerging risks in real time, with findings published as machine-readable StratML performance data. i71c10605-9175-4e2f-949b-b698cec08f3e 4.3 These oversight agents shall escalate material concerns to human stewards, legislative bodies, and the public without waiting for scheduled review cycles. Unlike periodic audits, continuous AI-assisted oversight matches the speed and scale at which agent ecosystems operate and evolve. 4.3.1 Deployment Binary Status ib6d40af5-7f36-4840-a196-5e8c1d3b0f74 2027-01-01 2027-12-31 1 Operational At least one TAIA-chartered AI oversight agent operational and publishing findings to the public registry in StratML format 0 Pending 4.3.2 Findings Count Status id8f51cb9-0c69-4173-e429-9e2f5d8c4b19 2028-01-01 2028-12-31 12 Published Structured findings published by TAIA oversight agents per year 0 Pending A target of 12 per year (approximately one per week per quarter) reflects active monitoring rather than token publication. Volume should be calibrated to ecosystem activity — higher platform counts and incident registry volume should correlate with higher finding frequency. 4.3.3 Audit Binary Status ie9a62dc0-1d7a-4284-f530-0f3a6e9d5c20 2028-01-01 2028-12-31 1 Completed & Published Independent audit of TAIA oversight agent behavior confirming accuracy and freedom from manipulation 0 Pending 2029-01-01 2029-12-31 1 Completed & Published 0 Pending Oversight agents are themselves AI systems and subject to the same transparency and accountability requirements as the platforms they monitor. Annual independent audits by Security Researchers (Stakeholder role: Oversight Agent Auditor) are essential to maintaining public trust in the continuous monitoring function. Civic Engagement Virtually convene multi-stakeholder dialogues bringing together technologists, civil society, legislators, and affected communities to deliberate on AI agent governance, triggered by evidence surfaced through continuous oversight rather than fixed calendar schedules. idf594196-a494-4f0a-abbd-1ff2f760dc82 4.4 Engagement is grounded in transparent, machine-readable evidence rather than majoritarian processes. Findings and resulting priority updates shall be published in StratML format to maintain accountability across all participating stakeholders. 4.4.1 Dialogues Number Status ic7e51ba5-8a47-4951-b207-6f9d2e4c1a84 2026-03-21 2026-12-31 1 Convened Evidence-triggered multi-stakeholder dialogues convened 0 Pending 2027-01-01 2027-12-31 3 Convened 0 Pending 2028-01-01 2028-12-31 4 Convened 0 Pending 4.4.2 Records Binary Status id8a62eb7-9b58-4062-c318-7a0e4c6f3b07 2026-03-21 2029-12-31 1 Published StratML record of findings and resulting priority updates published following each dialogue convened 0 Pending Publication of StratML records is a non-negotiable condition of every dialogue — it is the mechanism by which engagement produces accountable outputs rather than undocumented deliberation. 4.4.3 Linkage Binary Status ie9b73fc8-0c69-4284-a540-0f3b6e8c5d29 2027-01-01 2027-12-31 1 Demonstrated At least one TAIA strategic priority update traceable to evidence surfaced through a civic engagement dialogue 0 Pending This PI tests whether civic engagement produces substantive governance outcomes, not merely process compliance. Traceability from dialogue evidence to updated StratML plan elements closes the accountability loop between oversight findings and organizational response. 2026-03-21 https://stratml.us/docs/TAIA_Part2.xml Owen Ambur Owen.Ambur@verizon.net