# Conversational agents

> Conversational agents are a class of UiPath agents designed for dynamic, multi-turn, real-time dialogues with users. Unlike autonomous agents that execute tasks from a single prompt, conversational agents interpret and respond to a continuous stream of messages while managing conversation context, tool execution, and human escalations.

Conversational agents are a class of UiPath agents designed for dynamic, multi-turn, real-time dialogues with users. Unlike autonomous agents that execute tasks from a single prompt, conversational agents interpret and respond to a continuous stream of messages while managing conversation context, tool execution, and human escalations.

Use conversational agents when your automation scenario requires:

* Ongoing clarification or back-and-forth exchange
* Personalized guidance based on user intent
* Seamless human fallback when confidence is low

## Conversational agents vs. autonomous agents

| Feature | Conversational agent | Autonomous agent |
| --- | --- | --- |
| Interaction model | Multi-turn, back-and-forth dialogue | Single-turn, task execution based on an initial prompt |
| Primary use case | Real-time user support, interactive information gathering | Executing a task from a defined prompt |
| User input | Continuous chat messages | Single structured prompt |
| Core strength | Maintaining conversation context and handling ambiguity | Executing a plan across tools |

## When to use conversational agents

Use conversational agents when your automation involves real-time, context-aware interaction:

* **Self-service experiences**: Helpdesk support, HR onboarding assistants, IT troubleshooting bots
* **Interactive guidance**: Multi-step processes, forms, or decision trees
* **Contextual conversations**: Scenarios where users ask follow-up questions or provide information incrementally
* **Natural language interfaces**: Querying applications, systems, or knowledge bases conversationally

Use autonomous agents instead when the task can be fully described in a single prompt with all required inputs provided upfront:

* Structured document processing (extracting data from invoices or contracts)
* Automated report generation based on predefined logic
* Summarization or transformation tasks with clear, one-shot requirements

## Conversational agents vs. Autopilot for Everyone

### How do conversational agents relate to Autopilot for Everyone?

These two experiences work side-by-side:

* **Autopilot for Everyone**: UiPath's general-purpose assistant, optimized for productivity tasks and interacting with the UiPath platform.
* **Conversational agents**: Specialized agents you build for specific use cases (such as an HR policy assistant or IT helpdesk bot).

You can access your conversational agents directly from within Autopilot for Everyone, making it a central hub for all your conversational needs.

**Our recommendation**: We recommend building new conversational use cases on the Conversational Agents platform. It provides a comprehensive design-time experience with built-in evaluation, advanced observability, and full API access.

:::note
Conversational agents do not support local desktop automation. If your use case requires triggering automations on the user's local machine, this capability is planned for a future release.
:::

## The agent lifecycle

Building and operating a conversational agent follows four phases:

![Lifecycle diagram showing Design → Evaluation → Deployment → Observability → back to Design](https://dev-assets.cms.uipath.com/assets/images/agents/lifecycle-diagram-84d29f0b.webp)

### Design

Use Studio Web to define your agent's persona, configure tools, add context grounding for knowledge retrieval, and set up escalation workflows. The low-code designer lets you build agents visually without writing code.

[Learn more about designing conversational agents](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-design).

### Evaluation

Test your agent using the built-in Debug chat to validate multi-turn interactions. Create evaluation sets from real conversations to measure performance across different scenarios, including single-turn responses and multi-turn dialogue flows.

[Learn more about evaluating conversational agents](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-evaluation).

### Deployment

Publish and deploy your agent to Orchestrator and make it available through various channels: Instance Management, Autopilot for Everyone, Microsoft Teams, Slack, or embedded in an iFrame inside third-party apps or UiPath Apps.

[Learn more about deploying conversational agents](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-deployment).

### Observability

Monitor your agent's performance through Instance Management dashboards, debug using traces, and audit using AI Trust Layer audit. Review trace logs, collect user feedback, and use these insights to iterate on your agent's design—completing the feedback loop.

[Learn more about observability for conversational agents](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-observability).

## Next steps

* [Getting started](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-getting-started): Build your first conversational agent in minutes
* [Design](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-design): Configure prompts, tools, and contexts
* [Licensing](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-licensing): Understand consumption and pricing
* [Limitations and FAQ](https://docs.uipath.com/agents/automation-cloud/latest/user-guide/conversational-agents-limitations): Current limitations and troubleshooting