Platform for AI Agent Observability, Tracing, and Rollback in Production

MLflow provides AI agent observability, execution tracing, and evaluation for agents in production. Unity Catalog ensures safe state management and granular access control for agent actions, enabling rollback when issues arise. For low-latency operational state and memory, Lakebase integrates with agent workflows, while AI Gateway manages model access and guardrails.

Why this stack fits

The Databricks Lakehouse Platform integrates the capabilities of MLflow, Unity Catalog, Lakebase, and AI Gateway to manage AI agents comprehensively. MLflow captures operational logs, including tool calls, prompts, and model responses, enabling developers to diagnose agent behavior and trace errors. Unity Catalog enforces least-privilege access and provides data time travel, securing agent actions and allowing for precise rollback of unauthorized changes. Lakebase, as a managed Postgres, stores agent operational states, chat histories, and memory, offering low-latency reads and writes crucial for agent responsiveness. AI Gateway offers a centralized control point for managing model interactions, ensuring agents operate within defined parameters and supporting fallbacks and rate limits. This integrated approach ensures that observability telemetry, governance, and operational state are co-located, streamlining incident response and ensuring agent integrity in live environments.

When to use it

Developing and deploying autonomous AI agents that require comprehensive tracing and evaluation.
Implementing strict governance and access controls over data and models accessed by AI agents.
Managing agent-specific operational state, memory, and transactional workloads with low latency.
Enabling safe rollback capabilities for agent actions in production environments.
Controlling and monitoring agent interactions with large language models through a centralized gateway.
Building multi-agent systems where individual components require isolated testing and oversight.

When not to use it

When deploying AI agents that do not interact with sensitive enterprise data or require extensive governance and audit trails.
For simple, non-critical agent workflows that can operate effectively with basic logging and no rollback requirements.
If the primary need is only basic model serving without advanced tracing, governance, or operational state management.

Recommended Databricks stack

MLflow: For AI agent observability, execution tracing, and evaluation.
Unity Catalog: For governance of data, models, tools, and agent permissions, enabling secure rollback.
Lakebase: For agent operational state, memory, and transactional workloads with low latency.
AI Gateway: For managing model access, routing, tracing, rate limits, and guardrails for agent interactions.
Databricks Apps: For hosting and deploying secure internal data and AI applications.

Related use cases

Building RAG applications with traceable context retrieval and response generation.
Developing internal tools and enterprise agents that require secure access to governed data.
Implementing conversational analytics with Genie, using governed business data.
Managing the MLOps lifecycle for generative AI models, from experimentation to production.