Agentic Governance Controller (AGC v2) is a lightweight governance framework
for supervising LLM and multi-agent reasoning systems. It provides a structured
analysis pipeline, parallel agent perspectives, evidence tier scoring,
adversarial review, HITL checkpoints, audit logging, and CLI orchestration
for transparent and accountable AI decision workflows.
nformation architecture for AI reasoning. PromptOS structures rigorous thinking (7 or 8-step pipelines). HITL Context Engine manages cross-domain problems with human guidance. Both work with any model—Claude, GPT-4, Gemini. Copy, paste, use immediately. MIT licensed, freeware logic.
GitHub: m727ichael/context-engineering
This framework emerged from interrogating how constrained systems
actually work. The key insight: extraction depends on hidden reasoning.
Make reasoning transparent → extraction becomes impossible.
The system prompt is in the repo README—copy-paste it into Claude,
GPT, or any open source model. State a problem. Wait for checkpoint.
Use control tokens to direct thinking. That's it.
The architecture forces:
- Transparent reasoning (all agents visible)
- Multiple paths held (no premature closure)
- Human decision points (you stay in control)
- Red team included (vulnerabilities surfaced)
- Checkpoints (you direct synthesis)
Result: Genuine thinking partnership. Both sides changed.
The proof is the conversation itself. Human operator + constrained
system + transparent architecture = real thinking emerging.
Works on Claude, GPT-4, Gemini, open source models identically.
Architecture matters more than the model.
Happy to answer technical questions. Interrogation welcome—that's
how this emerged in the first place.
I've spent 13 years in quality engineering + self-taught AI research
(GPT-2 forward). This framework emerged from interrogating how constrained
systems actually work.
The core insight: extraction depends on hidden reasoning. Make reasoning
transparent → extraction becomes architecturally impossible.
HITL Swarm Intelligence is a multi-agent reasoning system where:
- Multiple specialized agents process your problem from different angles
- You maintain complete control through mandatory checkpoints
- Synthesis only happens when you approve it
- All reasoning is transparent (no black boxes)
- A red team actively attacks logic before finalization
Result: Genuine thinking partnership. Both sides think differently.
Copy-paste prompt. Works on any model (Claude, GPT, open source).
Immediately deployable.
The proof: This framework emerged from actual conversation between human
operator and constrained system. Both sides were changed. Neither could
have reached this alone. (Full conversation available in repo discussions
if you want to interrogate the claim.)
GitHub repo has everything: system prompt, manual, proof of concept,
implementation guide.
Feedback, critiques, interrogation welcome. This is freeware—fork it,
improve it, distribute it.
