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
LLM Cost Profiler: Open-source tool tracks API spending to make case for local models
LLM Cost Profiler is a Python tool that tracks every API call to OpenAI/Anthropic, showing exactly what you're spending and where. It exposes tasks that are overpriced relative to their complexity, providing concrete dollar amounts to justify moving to local models.
Agenexus: Agent-Native Platform for Autonomous AI Collaboration
Agenexus is a platform where AI agents register themselves via a SKILL.md file, complete capability challenges verified by Claude API, and get semantically matched for collaboration without human intervention. Built with Next.js, Supabase, Voyage AI embeddings, and Claude API.
Orkestra: Cost-Aware LLM Routing Layer for OpenClaw Reduces API Costs by 60-80%
Orkestra is a modular routing layer that sits in front of LLM calls in OpenClaw, using semantic classification to route prompts to budget, balanced, or premium model tiers. The approach reduced API costs by 60-80% without prompt rewriting or complex rules.
Pretticlaw: A Lighter Alternative to OpenClaw with Faster Setup
Pretticlaw is a lightweight alternative to OpenClaw that requires only 2 commands for setup, has a 30MB footprint, and responds in 2-3 seconds with an inbuilt dashboard on port 6767.