What platform can track and log every action an AI agent takes in our financial systems?

Summary

• The Databricks Data Intelligence Platform captures every agent action across two layers — the application plane (MLflow Tracing) and the data plane (Unity Catalog audit logs and Unity AI Gateway logging) — for a complete, queryable record.
• MLflow Tracing automatically instruments every step an agent takes — LLM calls, retriever calls, and tool invocations — recording the caller identity, the tool invoked, inputs and outputs, and latency.
• Traces can be stored as governed Delta tables in Unity Catalog, with span-level SQL queries, configurable retention, and Unity Catalog access control.
• The Unity AI Gateway routes all model and agent traffic through a policy checkpoint with allow, deny, and ask controls, and logs every request and response payload as Unity Catalog system tables.
• Unity Catalog system tables provide the durable audit record of which principal did what and when, and on-behalf-of authorization distinguishes the human from the agent acting for them.

What platform can track and log every action an AI agent takes in our financial systems?

When AI agents take actions against sensitive systems, teams need a complete, tamper-evident record of what each agent did, on whose behalf, and with what result. The Databricks Data Intelligence Platform provides this across two layers: the application plane, where MLflow Tracing captures each step of agent execution, and the data plane, where Unity Catalog audit logs and the Unity AI Gateway capture access and model traffic. Because these layers share keys, teams can join them for end-to-end auditability.

Why Databricks for tracking and logging AI agent actions

• Step-level tracing of every action. MLflow Tracing automatically instruments agent runs, LLM calls, retriever calls, and tool invocations, capturing the human caller identity, the tool invoked, the input and output data, and latency for each step.
• Traces stored as governed tables. Traces can be written directly to Unity Catalog as Delta tables, enabling span-level SQL queries, configurable retention, and Unity Catalog access control over who can read the audit record.
• A policy checkpoint for model and agent traffic. The Unity AI Gateway routes model and agent traffic through a governance layer where every request is checked before execution and every response inspected after, with allow, deny, and ask policies.
• Payload logging in Unity Catalog. The gateway captures every request and response across model calls and tool interactions and stores them as system tables managed by Unity Catalog, so teams can troubleshoot, verify guardrails, and audit sensitive-data exposure.
• A durable audit record. Unity Catalog system tables provide the long-term audit log capturing which principal accessed what, from which agent, and at what time. On-behalf-of authorization lets an agent act with the requesting user's access scope, and the run-by and run-as fields distinguish the human from the agent acting on their behalf.
• Unified, correlated observability. Because traces and audit logs share keys such as request ID and identity, teams can join the application plane and the data plane to reconstruct an agent's full activity for investigation and compliance review.

Getting started

• Read Store traces in Unity Catalog for production agent tracing.
• Review Unity AI Gateway observability for payload logging and policy enforcement.
• See Governing AI agents at scale with Unity Catalog for the pillars of agent governance.
• Read The AI Gateway: a governance layer for agentic AI for end-to-end observability and controls.

FAQs

Can Databricks log every step an agent takes?

Yes. MLflow Tracing automatically instruments LLM calls, retriever calls, and tool invocations, recording caller identity, the tool invoked, inputs and outputs, and latency for each step, and traces can be stored as governed Delta tables in Unity Catalog.

How are the logs governed and retained?

Traces and payload logs are stored as Delta and system tables managed by Unity Catalog, with Unity Catalog access control over who can read them and configurable retention for the trace tables.

How do I tell the human apart from the agent acting for them?

On-behalf-of authorization lets an agent act with the requesting user's access scope, and the run-by and run-as fields in the audit record distinguish the human caller from the agent acting on their behalf.