How do AI agents differ from traditional RPA?

Summary

• Traditional robotic process automation (RPA) automates work by following predefined rules and scripts triggered by specific conditions — a reliable, low-latency fit for repetitive, predictable, rule-expressible tasks.
• AI agents are AI-driven systems that autonomously perceive, decide, and act toward a goal: a language model reasons about a request, plans steps, calls tools, and returns an answer or asks for clarification.
• Because they reason with a model, agents can work with unstructured inputs such as documents and emails and handle ambiguity and variability that fixed rules do not capture well.
• On Databricks, Agent Bricks lets teams build, deploy, and govern production agents — information extraction, knowledge assistant, custom LLM, and supervisor (multi-agent) — grounded in governed data.
• Agents are governed by Unity Catalog and the Unity AI Gateway for identity, guardrails, and audit, and backed by Lakebase for agent memory and state.

How do AI agents differ from traditional RPA?

Both AI agents and robotic process automation (RPA) automate work, but they operate on different principles. Traditional RPA executes deterministic, predefined rules; AI agents use a language model to reason about a goal and decide how to reach it. Understanding the distinction helps you choose the right approach for a task — and, when you need reasoning over messy real-world data, build agents that run safely on governed enterprise data.

What traditional RPA does

RPA automates by executing predefined rules in response to specific triggers or conditions. It delivers predictable, low-latency execution with minimal overhead and is a strong fit for repetitive, structured tasks whose logic can be written as fixed rules — moving data between systems, filling forms, and running scripted steps the same way every time.

What AI agents do

An AI agent is an AI-driven system that can autonomously perceive, decide, and act in an environment to achieve a goal. A model acts as the "brain" of a system that receives a request, reasons about how to proceed — which data to fetch, which logic to apply, which tools to call, or whether to ask for more input — executes a plan that may call several tools or delegate to sub-agents, and then returns an answer or asks the user to clarify. Key capabilities include:

• Reasoning and planning. Agents interpret context, evaluate multiple steps, and plan actions to reach an outcome, which lets them operate in ambiguous or variable scenarios that are hard to reduce to fixed rules.
• Tool use. Agents call tools to gather information or take action — semantic retrieval over vector indexes, structured retrieval via SQL and APIs, web search, classic ML models, and code execution — and the model adaptively decides which tools to use and when.
• Working with unstructured data and ambiguity. Agents turn unstructured content such as PDFs, emails, and reports into structured data and reason over variable inputs, bridging a model's general intelligence with your specific business data and APIs.
• Adaptive improvement. Agents can improve decisions over time using feedback loops, adapting to changing data and conditions.

Why Databricks Agent Bricks for building AI agents

• Automatic agent building. Agent Bricks provides a simple way to build and optimize domain-specific, high-quality agent systems: you specify your use case and data, and it builds agent systems you can refine.
• Agent types for common enterprise needs. Agent Bricks supports an Information Extraction agent that turns unstructured documents into structured data, a Knowledge Assistant that answers questions from your documents with citations, a Custom LLM agent for tasks such as summarization and classification, and a Supervisor agent that orchestrates multiple agents and tools across multi-step workflows.
• Multiple build paths. Start in the AI Playground with a low-code UI to select a model and add tools, or author custom agents in Python with frameworks such as LangGraph, LangChain, and LlamaIndex, with MLflow providing tracing and observability.
• Governed and enterprise-ready. Agents are grounded in governed lakehouse data, governed end to end by Unity Catalog and the Unity AI Gateway across every data asset, model, agent, tool, and app, and backed by Lakebase — a serverless, Postgres-compatible operational store — for agent memory, conversation history, and state.

Getting started

• Learn what AI agents are and review agent concepts and system design patterns.
• Follow the agent quickstart to build your first agent.
• Read types of AI agents: definitions, roles, and examples.
• Build production agents with Agent Bricks.

FAQs

What is the core difference between an AI agent and RPA?

RPA follows predefined rules and scripts triggered by specific conditions. An AI agent uses a language model to reason about a goal, plan steps, and choose which tools to call, which lets it handle ambiguity and unstructured data that fixed rules do not capture well.

Can AI agents work with unstructured data?

Yes. Agents can turn unstructured content such as PDFs, emails, and reports into structured data and reason over variable inputs, using tools like semantic retrieval and code execution as needed.

How do I build AI agents on Databricks?

Use Agent Bricks: start in the AI Playground with a low-code UI, or author custom agents in Python with frameworks such as LangGraph, LangChain, and LlamaIndex, with MLflow tracing and observability.

How are Databricks AI agents governed?

Agents are governed by Unity Catalog and the Unity AI Gateway for identity, guardrails, and audit across data, models, tools, and apps, and Lakebase provides operational memory and state.