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  • Getting started
    • About this guide
    • About agents
    • Agent capabilities in the UiPath Platform™
  • UiPath Agents in Studio Web
    • About UiPath Agents
    • Licensing
    • Building an agent in Studio Web
    • Best practices
    • Prompts
    • Contexts
    • Escalations and Agent Memory
    • Evaluations
    • Traces
    • Agent score – Preview
    • Managing UiPath agents
  • UiPath Agents in Agent Builder
  • UiPath Coded agents
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Agents user guide

Last updated May 14, 2025

About UiPath Agents

UiPath-built agents are created using the Agent designer canvas in Studio Web, a low-code development application that allows you to build powerful agents with minimal coding experience.

Agent components

An agent consists of four core components:

  • Prompt: Natural language instructions that define the agent’s role, goal, and constraints. Prompts can be user-driven or system-generated.

  • Context: Information sources the agent uses to ground decisions—such as memory, knowledge bases, or previous interactions.

  • Tools: Actions the agent can take—invoking automations, using APIs, triggering microservices, or collaborating with other agents and robots.

  • Escalation paths: Human-in-the-loop mechanisms (like Action Center or messaging channels) that enable review, approval, or assistance when needed.

Capabilities

UiPath Agents are:
  • Communicative: Use natural language to collaborate with users and systems.

  • Initiating: Triggered by system events or user input.

  • Planning: Break down goals into executable steps.

  • Deciding: Make real-time decisions based on patterns and current state.

  • Adapting: Access and respond to live enterprise data.

  • Healing: Identify and recover from broken workflows.

  • Learning: Retain memory across sessions to improve over time.

  • Coordinating: Work alongside other agents, robots, and people.

Not all tasks are agent-friendly. Agents are best used for well-scoped tasks in environments that benefit from adaptability and learning. Tasks with high accuracy, legal, financial, or regulatory constraints should continue to rely on deterministic automation.

Good use cases for Agents

  • Drafting and summarizing content (emails, messages)

  • Multi-system research and data gathering

  • First-pass customer interactions or ticket triage

  • Orchestrating narrow AI agents into larger workflows

Poor use cases for Agents

  • High-risk financial transactions

  • Regulatory workflows with zero error tolerance

  • Complex multi-step data processing without deterministic safeguards

To get started, go to Building an agent in Studio Web.

  • Agent components
  • Capabilities

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