We open-sourced a 6-library governance stack for AI agents (Python)
Six Python libraries for AI agent governance, though Context Kubernetes feels like buzzword soup.

Orchestrates multi-AI governance, but demo is theater—no production backend, unclear scaling story.
Enterprise AI ops teams managing multi-model deployments
Anthropic Batch API · OpenAI Platform · Hugging Face Model Hub
Assign roles and execution sequences across 6 AI systems Set governance modes that constrain how systems respond Broadcast a single prompt to multiple systems in defined order Load context documents from a vault and inject them into sessions Deploy structured missions with WHO/WHAT/WHERE configuration Control response behavior with compression, speed, and length sliders SNAIL mode (step-by-step acknowledgment), LOCK IN (acute focus) Command postures: FALL IN LINE, FOLLOW, LEAD, TAKE CHARGE Full session export and audit logging
The demo uses simulated responses to show how governance state flows through. The actual product is delivered to licensed organizations who connect their own API keys and engine. Built as a single self-contained HTML file (~7K lines). I wanted something that could run anywhere, including air-gapped environments, with zero infrastructure dependencies. Happy to discuss the architecture, the governance model, or the reasoning behind any of it.
RiftWalking
Six Python libraries for AI agent governance, though Context Kubernetes feels like buzzword soup.
Two-agent system where The Architect learns from PR review comments automatically.
Wave-based parallel AI agent orchestration with a PM coordinator for Claude Code projects.
Local prompts execute remotely over SSH with zero server setup and keys never leave your machine.
Multi-agent Claude orchestration with DAG workflows, but Cursor already owns this UI.
Multi-agent orchestration for two CLIs, but early-stage and AI-generated codebase raises maintenance concerns.