How do I build an AI agent harness from scratch?

Summary

• An agent harness is the surrounding infrastructure that lets an agent reason, call tools, be evaluated, deployed, governed, and monitored — not just the model.
• Agent Framework provides the core build, evaluate, iterate, and deploy lifecycle; author agents in Python with popular open source frameworks or the MLflow ResponsesAgent interface.
• Tools and function-calling connect the agent to data and actions through Unity Catalog functions, managed connections, and MCP servers, all governed and auditable.
• MLflow tracing and evaluation record every step and measure quality, cost, and latency with built-in and custom judges; Databricks AI Search adds retrieval.
• Model Serving deploys the agent as a scalable endpoint with a control plane for access control, rate limiting, guardrails, and payload logging; Agent Bricks offers a higher-level, automated authoring surface.

How do I build an AI agent harness from scratch?

An agent harness is the infrastructure around a large language model that lets it act as a reliable agent: connect to tools and data, follow a build-and-evaluate loop, deploy as a service, and stay governed and observable in production. The Databricks Data Intelligence Platform provides these building blocks — agent authoring, governed tools, tracing and evaluation, retrieval, deployment, and a control plane — as an integrated set, so you can assemble a harness without wiring every component together yourself.

Why Databricks for building agents

• A production agent framework. The Agent Framework handles the full lifecycle — build, evaluate, iterate, and deploy. You can author agents in Python with popular open source frameworks such as LangGraph, LangChain, and LlamaIndex, and use the MLflow ResponsesAgent interface for consistent logging, tracing, evaluation, and deployment. See build and deploy enterprise-grade agents.
• Tools and function-calling. Agents connect to data and actions through Unity Catalog functions, managed connections, and Model Context Protocol (MCP) servers. Every tool is discoverable, permissioned, and auditable through Unity Catalog. Learn the fundamentals of tool calling.
• Retrieval for grounding. Databricks AI Search provides managed vector indexing so agents can retrieve relevant text and unstructured data for retrieval-augmented generation.
• Tracing and evaluation. MLflow tracing records inputs, outputs, and metadata for every intermediate step, and agent evaluation measures quality, cost, and latency with built-in and custom LLM judges — so you can iterate from real traces and evaluation datasets.
• Governance. Unity Catalog applies role-based access control to models, tools, and connections, provides lineage from agent outputs back to source tables, and can enforce that agents respect the invoking user's data permissions.
• Deployment and control plane. Model Serving deploys agents as scalable endpoints with automatic request/response logging, and the Unity Gateway provides a unified control plane for access control, rate limiting, guardrails, and payload logging. Agents can also be delivered to end users as Databricks Apps.
• A higher-level option. If you prefer not to build every piece by hand, Agent Bricks provides an automated authoring surface — including a Knowledge Assistant for domain question-answering and a Supervisor Agent for orchestrating multiple agents and tools.

Getting started

• Read Build agents on Databricks and build and deploy enterprise-grade agents.
• Author your agent in Python and register Unity Catalog functions as tools.
• Add Databricks AI Search for retrieval and instrument the agent with MLflow tracing and evaluation.
• Deploy with Model Serving, and explore new agent training to go deeper.

FAQs

What are the core parts of an agent harness?

The essentials are an agent framework for the build-and-evaluate loop, tools and function-calling, retrieval with vector search, tracing and evaluation, governance, and a serving endpoint with a control plane for access control and guardrails.

Do I have to code the agent from scratch?

You can author custom agents in Python with popular open source frameworks, or use Agent Bricks for an automated authoring surface such as a Knowledge Assistant or Supervisor Agent.

How do I evaluate and monitor an agent?

MLflow tracing records every step of execution and agent evaluation measures quality, cost, and latency with built-in and custom judges, both before deployment and in production.