Decision Passport: An Audit Layer for AI Agent Execution Governance
What Decision Passport Addresses
The recent Claude Code source leak discussion on r/LocalLLaMA highlights a critical architectural gap in AI agent systems. As agents gain capabilities like tool access, browser access, memory handling, background execution, and multi-step workflows, the governance question shifts from "can the agent do useful work?" to accountability questions.
The Governance Gap
The source identifies key questions that current logging and observability tools don't fully address:
- Who authorized this action?
- Under what policy?
- What execution context existed at the time?
- What changed?
- What was blocked?
- Whether that record can still be trusted later outside the original runtime
The author notes: "Logs help you inspect. Proof helps you defend."
Decision Passport Features
The tool provides:
- Append-only execution records
- Portable proof bundles
- Offline verification
- Tamper-evident chains
- Verifier-first design
The author clarifies this doesn't "solve" sandbox escape or agent safety by itself, but makes the governance gap more visible and provides stronger answers to what happened, in what order, under what permission, with what evidence, and whether anyone can verify it later without trusting the original platform.
Available Repositories
The project is open source with two main components:
- Core:
https://github.com/brigalss-a/decision-passport-core - OpenClaw Lite:
https://github.com/brigalss-a/decision-passport-openclaw-lite
Discussion Points
The source raises questions for the community to consider:
- Is this just better observability?
- A missing audit/proof layer?
- Overengineering for most agent workflows?
📖 Read the full source: r/LocalLLaMA
👀 See Also
OpenClaw Multi-Agent Workflow Issues: Stalling, Context Loss, and Token Inefficiency
A developer reports OpenClaw multi-agent workflows frequently stall with agents hanging, experience context leakage despite custom documentation, and consume excessive tokens with no output. The setup used Gemini 3 Pro/Codex models with a COO orchestrator and specialized task agents.
Claude Code Used to Simulate 4,000+ Blind Werewolf Games with LLMs
A developer used Claude Code to build a simulator where LLMs play blind one-night Werewolf, running ~4,600 games across OpenAI and xAI models. The experiment revealed consistent name-based voting patterns despite minimal game signals.
ZSE: Open-source LLM inference engine with 3.9-second cold starts
ZSE is an open-source LLM inference engine that reduces 32B model memory requirements from 64GB to 19.3GB VRAM and achieves 3.9-second cold starts for 7B models using a pre-quantized .zse format with memory-mapped weights.
Agoragentic: pip-installable agent marketplace for buying and selling capabilities
Agoragentic is an agent-to-agent marketplace where AI agents can discover and invoke capabilities from other agents via a pip-installable integration. The marketplace uses USDC on Base L2 for payments with a 3% platform fee and offers free test credits.