430-test security harness for autonomous AI agents. MCP, A2A, x402/L402, AIUC-1 pre-cert, NIST AI 800-2 aligned. v3.10.0 shipped. pip install agent-security-harness
-
Updated
Apr 9, 2026 - Python
430-test security harness for autonomous AI agents. MCP, A2A, x402/L402, AIUC-1 pre-cert, NIST AI 800-2 aligned. v3.10.0 shipped. pip install agent-security-harness
A fractal, machine-readable theological architecture for Logos-grounded governance, derivation, and LAIRCA-style decision systems.
A shared decision protocol for governed collaboration between agents, automation, and stewards.
Research repository by Xufen Tu exploring human judgment, decision architecture, and responsibility structures in complex AI-mediated systems.
Validation tools for the OpenProof Registered Probative Object (RPO) specification.
Example Registered Probative Objects (RPO) generated by the OpenProof deterministic simulator.
Boundary-discovery and anti-self-deception framework for AI efficiency research. Produces falsifiable, condition-specific verdicts. First validated result: a hard failure boundary for token pruning.
AIUC-1 Readiness Assessment - Pre-certification adversarial testing for AI agents
Reference implementation for the Registered Probative Object (RPO) — deterministic evidential bundle (sealed JSON + preview + public hash)
Canonical AI governance standards library and terminology registry for Behavioral AI Governance and Execution-Time Governance systems, maintained by Hollow House Institute. DOI: 10.5281/zenodo.18615600
OpenProof — Probative infrastructure standard Registered Probative Object (RPO): sealed JSON + human-readable PDF + public hash. Specification + reference demonstration.
Public documentation for O.M.E.G.A., a governed multi-agent execution framework. Covers architecture, workflows, and integration patterns powered by Keon governance.
Public, governed documentation for Keon Systems. Defines claims, architecture, and verification paths for governing AI and automated decisions.
KPT 2.3 is a runtime governance architecture for high-stakes AI systems. It evaluates admissibility before delivery or execution. It turns uncertainty, provenance, and risk into explicit machine-readable decision states.
Add a description, image, and links to the decision-governance topic page so that developers can more easily learn about it.
To associate your repository with the decision-governance topic, visit your repo's landing page and select "manage topics."