[MVP] Aha Loop is a fully autonomous AI development system, extended from the core ideas of Ralph. It's not just an execution engine, but a complete AI development framework with planning capabilities, research abilities, and oversight mechanisms.

JOYA(Joy Agents) is a governance framework for teams of AI agents. It provides the rules, structure, and accountability mechanisms that let multiple agents collaborate safely under human oversight.

Payer-side AI-assisted prior authorization review using Microsoft Agent Framework with four Foundry Hosted Agents (Compliance, Clinical, Coverage, Synthesis). Gate-based decision rubric, MCP healthcare data access, confidence scoring, audit trails, and human-in-the-loop oversight. Deploys via azd to Azure Container Apps.

AgentSentry v0.5.0-beta — Standalone Agent Management Framework for AI agent oversight. Built with RuFlo.

Open-Source AI Governance Frameworks & Compliance SOPs for responsible AI development, risk mitigation, and ethical oversight. Ensuring trust, transparency, and accountability in AI systems.

Automating grant proposal review for EO compliance using Azure AI Foundry and Agent Framework, reducing manual review time while maintaining legal oversight.

A structured multi-agent AI framework with built-in oversight and transparent decision-making. Four specialized agents work together using proven architectural patterns for reliable AI coordination.

CIRCE: Collaborative Intelligent Response Circular Evolution — A framework for autonomous multi-agent AI coordination with persistent memory, file-based protocols, and human oversight.

Production-ready AI validation framework for cross-industry safety • Multi-layer oversight (bias, toxicity, PII, prompt injection) • C++/UE5 + Python • ML-powered detection • API alerting (Slack/Jira/GitHub) • Docker ready • Drop-in integration

Codex Oribis v1.0 (Frozen Canon) — A non-verbal, deterministic framework for validating structural coherence in governance, AI oversight, and complex systems. Includes ORIBIS-CORE (MV-CORE) reference implementation. Validation only; no interpretation or recommendations.

Taxonomy-driven AI workflow framework that classifies tasks, routes decisions, and applies governance logic to balance automation, human oversight, and capital efficiency. Designed to reduce LLM overuse, enforce structured routing, and create transparent, auditable intelligent operations.

Modular, deterministic oversight framework for multi-agent swarms: intent routing, behavioral ownership tracking, safety gates, auditable DAG trails. Skeleton / interfaces only (Jan–Feb 2026).

Ethical AI governance framework for multi-model alignment, integrity, and enterprise oversight.

AI governance framework — laws, bill proposals, voting, and executive oversight for constituent projects

Comprehensive risk assessment and compliance framework for healthcare AI systems - Built for Congressional oversight

AI-powered novel manuscript management framework — automated DOCX conversion, continuity checking, and editorial oversight

A simulation framework for studying AI agent behaviors related to deception, oversight evasion, and strategic manipulation.

Table Governance: Constitutional AI governance framework. React/TypeScript implementation of R=C−A applied to multi-stakeholder oversight.

A practical framework for embedding ethics, governance, and human oversight into enterprise AI delivery, translating Responsible AI principles into real-world operating models.

A practical framework for helping organizations move from AI experimentation to real-world systems, with ethics and human oversight built in.

🧠 Klein AI Dual Framework - AI Accelerate 2025 Hackathon. Two AIs: Klein helps, Ophir protects. Built for Elastic Challenge with Google Cloud + Elasticsearch integration. Safe, trustworthy conversational AI with oversight.

A reference framework for accountable Human-AI collaboration. Implements the Checkpoint-Based Governance (CBG) model — a protocol-driven system for human arbitration, traceable oversight, and measurable AI accountability.

Enterprise GenAI & Agentic AI Governance and Evaluation Framework A practical, risk-based framework aligned with European enterprise governance best practices to evaluate, govern, and operationalize GenAI and Agentic AI systems with accountability, auditability, and human oversight.

Welcome to the Human-Governed AI (HUG AI) Development Methodology. This framework is designed to integrate AI-driven assistance into software development practices, empowering teams to maintain human oversight while leveraging the productivity and consistency benefits of AI.

A governance doctrine for AI systems based on explicit oversight. Externalizes trust and uncertainty into auditable, adversarial, and constrainable layers. A design framework, not an implementation guide.

Playbook for designing, governing and evaluating agentic AI workflows, covering multi-step reasoning, tool use and orchestration patterns. Includes architecture types, evaluation methods, oversight principles and risk frameworks.

Frameworks, standards and process templates for AI governance, model risk and oversight in regulated enterprise environments. Includes operating models, RACI charts, control libraries, approval workflows and policy skeletons.

This release marks the public certification of the Evie Protocol AI framework’s impact on GPT‑5 development. Officially acknowledged by OpenAI and federal oversight partners. This framework is now verified as one of the most impactful modular AI safety advancements deployed globally.

Analysis of structural blind spots in global and Australian AI safety frameworks. Designed for government, policy teams, and research institutions to identify hidden systemic risks, strengthen sovereign AI, and complement existing compliance-focused standards. Includes paper, code, and frameworks for structural oversight.

A comprehensive National AI Governance Framework proposing strategic leadership, ethical oversight, and regulatory excellence for AI deployment. Features executive briefing, five implementation pillars, phased roadmap, and success metrics for positioning nations as global AI leaders while ensuring accountability and citizen trust.