From the course: Building AI That Remembers: Architecting Reliable, Context-Aware Enterprise Agents
Join today to access over 25,600 courses taught by industry experts.
Making AI agents auditable and traceable
From the course: Building AI That Remembers: Architecting Reliable, Context-Aware Enterprise Agents
Making AI agents auditable and traceable
Okay, let's say that an agent at Big Star issues a $500 refund to an ineligible customer. And then they claim, hmm, the AI did it. That fails to satisfy managers or regulators. See, traditional AI operates like magic, like a black box. You provide an input, an answer emerges. But the reasoning in between, it stays hidden. A better solution combines decision traces and memory logs. Decision traces record each step of the agent's reasoning loop, the outline it created, the tools it selected, and then the internal thought process it performed before acting. Memory logs, they capture timestamped records of exactly what information the agent retrieved from long-term memory and what it wrote back into the database. Our friend Marcus, he investigates a high-value refund that triggered a system alert. That agent that issued a full refund for a final sale limited edition vinyl record without traceability, Marcus sees a refund, but he can't determine why. He can't tell if the AI hallucinated…
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.