Ledger AI

Ledger is the runtime policy and evidence layer for autonomous AI agent actions. Fortune 500 enterprises have an average of 30 to 50 active AI pilot projects, but fewer than 10 percent reach production. The bottleneck is almost never the model. It is governance, risk, and accountability. When an agent touches a cloud API, a corporate card, or a paid service, a prompt injection or logic loop can cause five-figure runaway spend in hours. Compliance and legal cannot answer what the agent did, or why, which blocks SOC 2, EU AI Act, and NIST AI RMF readiness. And risk officers and CFOs cannot author agent governance rules without an engineer in the loop. Ledger solves this with a runtime interception layer. A FastAPI proxy sits between AI agents and the world. Every action an agent attempts, provisioning infrastructure, calling paid APIs, spending money, flows through Ledger first. The policy engine evaluates each action against enterprise-defined rules in real time: budget caps, category restrictions, time windows, and Gemini-powered intent alignment. Edge cases route to human approvers in Slack with full context. Every decision is logged with a SHA-256 hash, producing immutable audit-grade evidence. The differentiator: non-technical leaders define policy in plain English. A CFO types "agents cannot spin up GPU clusters on weekends without VP approval" and the Gemini-powered Policy Copilot converts that into executable runtime policy. No engineering bottleneck between business intent and enforcement. Built on Gemini 2.0 Flash, Python, FastAPI, SQLite, and the Slack API. Production-grade architecture. Hackathon-ready demo. Ledger is what unblocks the conversation between your AI team, your CFO, and your auditor, so AI agents can finally move from pilot to production.