Agent

Classic Agent
New Agent

The classic Agent node gives your LLM autonomous control over tools, enabling it to iteratively decide which tools to use and when to use them. Instead of pre-planning every step, the Agent reasons through problems dynamically, calling tools as needed to complete complex tasks.

Agent Strategies

Agent strategies define how your Agent thinks and acts. Choose the approach that best matches your model’s capabilities and task requirements.

Function Calling
ReAct (Reason + Act)

Uses the LLM’s native function calling capabilities to directly pass tool definitions through the tools parameter. The LLM decides when and how to call tools using its built-in mechanism.Best for models like GPT-4, Claude 3.5, and other models with robust function calling support.

Install additional strategies from Marketplace → Agent Strategies or contribute custom strategies to the community repository.

Configuration

Model Selection

Choose an LLM that supports your selected agent strategy. More capable models handle complex reasoning better but cost more per iteration. Ensure your model supports function calling if using that strategy.

Tool Configuration

Configure the tools your Agent can access. Each tool requires:Authorization - API keys and credentials for external services configured in your workspaceDescription - Clear explanation of what the tool does and when to use it (this guides the Agent’s decision-making)Parameters - Required and optional inputs the tool accepts with proper validation

Instructions and Context

Define the Agent’s role, goals, and context using natural language instructions. Use Jinja2 syntax to reference variables from upstream workflow nodes.Query specifies the user input or task the Agent should work on. This can be dynamic content from previous workflow nodes.

Execution Controls

Max Iterations sets a safety limit to prevent infinite loops. Configure based on task complexity - simple tasks need 3-5 iterations, while complex research might require 10-15.Memory controls how many previous messages the Agent remembers using TokenBufferMemory. Larger memory windows provide more context but increase token costs. This enables conversational continuity where users can reference previous actions.

Tool Parameter Auto-Generation

Tools can have parameters configured as auto-generated or manual input. Auto-generated parameters (auto: false) are automatically populated by the Agent, while manual input parameters require explicit values that become part of the tool’s permanent configuration.

Output Variables

Agent nodes provide comprehensive output including:Final Answer - The Agent’s ultimate response to the queryTool Outputs - Results from each tool invocation during executionReasoning Trace - Step-by-step decision process (especially detailed with ReAct strategy) available in the JSON outputIteration Count - Number of reasoning cycles usedSuccess Status - Whether the Agent completed the task successfullyAgent Logs - Structured log events with metadata for debugging and monitoring tool invocations

Use Cases

Research and Analysis - Agents can autonomously search multiple sources, synthesize information, and provide comprehensive answers.Troubleshooting - Diagnostic tasks where the Agent needs to gather information, test hypotheses, and adapt its approach based on findings.Multi-step Data Processing - Complex workflows where the next action depends on intermediate results.Dynamic API Integration - Scenarios where the sequence of API calls depends on responses and conditions that can’t be predetermined.

Best Practices

Clear Tool Descriptions help the Agent understand when and how to use each tool effectively.Appropriate Iteration Limits prevent runaway costs while allowing sufficient flexibility for complex tasks.Detailed Instructions provide context about the Agent’s role, goals, and any constraints or preferences.Memory Management balance context retention with token efficiency based on your use case requirements.

The new Agent node is in beta and available in Workflow apps only.

The new Agent node runs an agent as one step of a workflow. Unlike the classic node’s model-with-tools setup, the agent arrives as a complete worker with its own capabilities and sandbox.You pick the agent and tell it what to do; it works through the task on its own and hands the results back to the rest of the flow.

Choose an Agent

When you add the node, pick who does the work:

Invite an Agent
Start from Scratch

You can invite any published agent: it arrives with its saved capabilities, and you set only its task here.An invited agent is like a full-time employee: its capabilities are managed centrally in Agents. When you publish an update there, every workflow that uses the agent gets it.

To edit its capabilities, click Edit in Agents.
If you want changes for this step only, click Make a copy: the node switches to a one-time copy that no longer follows the original.

You can always swap the agent later.

Two nodes that invite the same agent still work separately: each starts from the agent’s saved setup in its own sandbox, and nothing from one node’s run (files it wrote, tools it installed) carries into another.To pass results between nodes, declare them as outputs and reference them downstream.

Give It a Task

In Agent task, describe what you need at this step, the way you’d brief a colleague on one job. This is separate from the agent’s own prompt and capabilities. Type / to pull in outputs from earlier nodes.Say you’ve invited a support agent that already knows your product docs and writes in your support tone, and the workflow hands it inbound customer emails. The task covers only this step:

Read the customer email in {{customer_email}} and draft a reply that answers every question in it. Keep it under 150 words.

Variables you pull in reach the agent as text and are truncated at 2,000 characters.To hand over longer content in full, pass it as a file: the agent receives the whole file and reads it in its sandbox.

Declare Its Outputs

By default, an Agent node returns a single output, text: the agent’s reply to its task, as one block of text. When a later step needs something more specific, like a single value or a particular file, declare it as its own output.Type / in the Agent task, choose New output, then name it and pick a type in place. Each declared output can be referenced by downstream nodes on its own.A declared output sits right in the task text, so you can tell the agent exactly what to put in it. For example, in this task {{vendor_name}} and {{quote_file}} are declared outputs:

Compare the three vendor quotes in {{vendor_quotes}} and write a recommendation. Put the winning vendor's name in {{vendor_name}} and its quote PDF in {{quote_file}}.

Each file the agent returns is capped at 50 MB.

Orchestrate

Publish

Monitor

Knowledge

Integrations

Workspace

Agent Strategies