You Ask, I Answer: Building Audit Trails For Enterprise AI Agents?

In today's episode, Vince asks, how do we build supervisor agents and verifiable audit trails regulated industries? We're seeing observability and governance gaps in our AI agents in the enterprise. Enterprise AI adoption is being blocked for us because we can't verify what the agent did and why. Okay. Agents are nothing more than software, right?

An agent is nothing more than a piece of software. If you go back to the almost timely issue I did for May 31st, where I talk about the analogy of map, server, and app, right? So an AI model is like a map inside of a mapping application. The server is the internet server the app connects to, and the app is the tool the end user experience, like Google Apps, uh Google Maps, you know, Apple Maps, OpenStreetMap, etc. AI is the same way.

There's the AI model, there's a server that serves the AI model, and then there's an app. There's a harness typically. If you don't have observability in your AI agent in your agentic infrastructure, it's because you didn't build logging in. At a the most fundamental level, you did not build logging into it. And the fix for that is relatively straightforward.

You go back to your coding agent or you go back to your age agentic infrastructure and you say, well, where should we put logging in? Where do we need to put logging in, and how are we going to process those logs? There's a good chance that you have or should have something like some kind of data warehouse, a data lake of some kind in the enterprise that allows you to say, put logs here. There's a number of companies out there that do exactly that. They can process server logs at scale and handle them Splunk.

For example, is one company that does that. I don't know if they're even still in business or not, but at the end of the day, you can have a log data warehouse, and then have deterministic and agentic uh AI processors of those logs. What I would generally recommend is you have some sort of deterministic capability like Python code that summarizes all the logs, because you're gonna have a lot of them that summarizes all the logs for a day, and then maybe have an agentic AI system that reads the log summaries and says, hey, here's what I'm noticing from day to day. There's more of this happening, more of that happening, and so on and so forth. That infrastructure is not difficult to build, it's expensive because you a logging server costs money, a logging server costs time to generate that code, and most importantly, it requires someone with subject matter expertise to look at the log reports, not the raw logs themselves, but what is summarized, and agree or disagree with the machine that, like, yeah, there is no cause for concern, or well, actually, there's a bunch of these anomalous events here that shouldn't be happening at all.

Why is this happening? We need to investigate. Where people are going wrong is they're treating agentic AI and you know, agent harnesses and stuff as if there's something brand new that we've never seen before. These are just apps, they're just pieces of software. There's nothing new here.

Yes, their functionality is new, yes, language models and good harnesses given capabilities that seem autonomous and powerful, but they're just apps. What do you have in place in your company for monitoring existing apps? Now, if the answer is nothing, you have much bigger problems than AI, right? You have some serious, serious internal problems that you need to address before you do another thing with AI because you are on a course charter for disaster. But reuse the architecture and the infrastructure you already have.

I promise you, in a well-run enterprise, you have this infrastructure already, you can go ahead and use that and save yourself a whole bunch of headache. Uh a whole bunch of just nightmares about it. So build logging and observability. And if you don't know how, prompt your AI. Go into Claude Code and say, we need to build, you know, uh observability into our agentic AI.

We have to use struct log for structured logs. We have to push it towards a server. Help me design a system that will do this. Um give it your coding standards, give it your data storage standards, give it the your system diagram and your architecture and say, help me build something that works with our architecture. And as long as you've provided enough information, the machine can do that.

You always have the ask me questions until you have enough information to complete the task because that's mandatory. But once you've done that, you should be all set. You can have the tools build you the tooling. That is the number one best use case for AI is to help you build tools so that you don't have to keep using AI. Anyway, that's the answer to the question.

It's a good question. Build logging in. You know there's a gap, build it. Thanks for the question. Talk to you on the next one.

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