What are decision traces in a context graph? How they reveal agentic reasoning

Your AI agent approved a refund outside the standard time frame. Now a compliance officer wants to know why, and whether the support agent granted a refund like this before. The chat log shows the conversation between the customer and the agent. The log doesn’t show what the agent checked before granting the refund or how it handled similar situations previously. Without a decision trace, you can try to piece together why an agent made a decision, but its reasoning is really a black box. With a decision trace, you get a structured record of how an AI agent reached a decision. It captures not only the outcome but also the reasons and tools the agent used. This is stored in a context graph, where those steps stay connected to the conversations, facts, and other information the agent relied on, so you can follow them to the outcome. Here’s why that matters, and what it takes to give your agents that kind of persistent, queryable memory. What does a decision trace record? Decision traces vs. LLM traces vs. logs AI agents make many decisions as…