Skip to main content

​
Overview

Agents are AI teammates capable of executing real multi-step work across apps and websites. Agents can read and use your organization’s knowledge, follow rules you define, and work inside workflows to complete complex tasks end-to-end. You can configure what they know, how they should behave, and then chat with them or embed them inside Workflows to automate processes.

Organizational Knowledge

Attach Library Files so the Agent answers from your own indexed documents

Rules and Guardrails

Define the rules the Agent must follow and keep answers grounded in the selected context

Web Enrichment

Optionally turn on Web Search when the Agent needs external information

Workflow Ready

Chat with an Agent directly, or embed it inside a Workflow to automate a process

​
Creating an Agent

1

Create the Agent

Click Create Agent under Agents.
2

Name and Describe It

Give your Agent a clear Name and Description so users can easily recognize its role and purpose.
3

Assign a Department

Assign a Department label to specify which department this Agent belongs to. Use consistent labels across Agents so teams can easily filter and find the right one.
4

Attach Context from Library Files

Attach context from Library Files. Select one or more relevant files ingested in the Library. Ensure only high-quality, up-to-date documents are selected to avoid outdated or conflicting answers.
5

Enable Guardrails

Tick the Guardrails checkmark to enable information retrieval from the Selected Library Files.
6

Define the Rules

Define the rules the Agent must follow, for example: Answer only from the selected Library files. If information is not in the context, say you don’t know.
7

Turn On Web Search (optional)

Turn on Web Search if you want the Agent to enrich answers with information from the web.
If you do not enable Guardrails, the Agent may use web sources and could respond beyond the specific documents or rules you intend. Always enable Guardrails for controlled, compliant behavior.
8

Instruct Your Users

Specify the Description to instruct users what input to specify when chatting with the Agent.
9

Test and Refine

Start a conversation by asking it questions that match its purpose. Verify that the Agent uses the selected Library files accurately while following the Guardrail rules. Then refine them as needed based on its responses.

​
Agents in Workflows

Agents are not limited to chat. You can embed them as a step inside a workflow, or chain several of them together:
  • Embed Existing Agents in the workflow to perform the pre-defined instruction and rules bound by the Agent.
  • Multiple Existing Agents can be linked together in a chain using the Workflows. Linking one Agent to another allows you to design “teams” of AI teammates, where each Agent handles a specialized part of the process. Configure inputs/outputs so the next Agent can use results produced by the previous Agent. For example, Agent A summarizes a document, Agent B reviews the summary for compliance.
To run an agentic task that reasons, executes code, and generates files inside a workflow step, use the Agent action node.

​
Pro Tips

Treat Agents as AI teammates that you are managing. Give them a clear role: “HR assistant”, “Sales deck generator”, “Policy Q&A”.
Guardrails first, Web Search second. Start with Guardrails and Selected Library Files only to ensure accuracy. Turn on Web Search only when you explicitly need external information (e.g., market research, news).
Curate context carefully. Avoid adding too many unrelated Library Files to a single Agent. Group context by topic or use case to reduce conflicting or noisy answers.
Show users how to prompt the Agent. When explaining how to use the Agent in the Description, include examples such as: “Ask me: ‘Summarize the Q4 policy changes for sales reps.’” “Use me when you need: draft responses based on the Employee Handbook.”
Test before sharing by chatting with the Agent yourself using realistic and irrelevant questions. Check for accurate citation and if it behaves correctly when it doesn’t know the answer (e.g., admits uncertainty instead of guessing).

​
Next Steps

Knowledge Base

Ingest and index the files your Agents will use as context

Workflows

Embed and chain Agents inside automated processes

Chatbots

Publish a conversational experience for external users