Every decision. Every data source. Every tool call. Traced, hashed, and auditable.
AI agents are increasingly being trusted with important decisions — reviewing filings, assessing risk, processing claims, generating reports. This is only going to accelerate.
But right now, most agent systems are black boxes. No record of what data was accessed, what reasoning was followed, what alternatives were considered, or whether the agent even queried the systems it was supposed to. When that output gets sent to investors, handed to compliance, or used to make a business decision — tracing how it got there is a real problem.
Scale makes it worse. When there are dozens of agents running across different workflows — compliance, risk, reporting — each producing its own logs in its own format, there's no single place to see what's happening.
I've been working on a system to tackle this — AgentTrace.
AgentTrace is an observability and audit layer for AI agent systems. It captures every phase, every agent decision, every tool call, and every data source access in a tamper-proof, hash-chained event log.
https://drive.google.com/file/d/1TQ51ucXl14rrTUuPX8qUEFvk2hMjxp7-/view?usp=sharing
At a glance: total runs, live agents, pending reviews, and flagged items. Every agent in one view.
Every agent run produces a complete execution tree — phases, agents, decisions, data queries — all linked in a parent-child hierarchy. You can see exactly what happened, in what order, and why.
