> ## Documentation Index
> Fetch the complete documentation index at: https://docs.xpander.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Agent Nodes
> Add AI reasoning to your workflows with Agent, Classifier, and Summarizer nodes, each purpose-built for a different kind of intelligence.
Agent nodes use an LLM to reason about data, make decisions, and generate output. Three types are available, each designed for a specific kind of reasoning.
**Agent nodes vs. action nodes:** Agent nodes use an LLM to reason about data. Action nodes run deterministic operations (API calls, code, email) without an LLM. Use agent nodes when the step requires judgment; use action nodes when the step has a fixed, predictable outcome.
| Node | Purpose | Best for |
| -------------- | ---------------------------------- | ---------------------------------------------------------------- |
| **Agent** | Full AI agent with tool access | Multi-step reasoning, querying systems, making decisions |
| **Classifier** | Route inputs to different branches | Intent detection, categorization, triage |
| **Summarizer** | Condense or extract from content | Distilling long inputs, extracting key fields, formatting output |
## Reason and act with the Agent node
The Agent node connects a full Xpander agent (with its tools, knowledge base, and memory) into your workflow as a single step. When the workflow reaches this node, it hands the input to the agent along with your instructions, and the agent reasons through the task using whatever connectors and knowledge it has access to.
### Configure an Agent node
Select an agent from the dropdown. This can be any agent you've built in Agent Studio, arriving with its system prompt, tools, knowledge base, and memory already configured. If you don't have one yet, click **+ New agent** to create one inline.
The **Instructions** field tailors the agent's behavior for this specific workflow step. These instructions are appended to the agent's existing system prompt, so you don't need to repeat its general configuration. Focus on what this step should accomplish:
```text theme={"dark"}
Enrich the incoming lead with company data. Look up the company
in Clearbit and cross-reference with our CRM. If the company has
more than 500 employees, flag it as enterprise tier.
```
Use `{{variable}}` placeholders to inject data from previous workflow steps. Click the **Workflow variable placeholders** helper to see what's available.
**Persist memory thread** is on by default, meaning the agent reuses the same memory thread across workflow runs and builds up context over time. Turn it off when each run should start fresh.
**Run asynchronously** fires the agent without waiting for its response, useful for side effects (like logging) that shouldn't block the workflow.
**Output type** controls the format returned: text by default, structured output (JSON to a schema), or voice for audio-enabled agents.
## Route inputs with the Classifier node
The Classifier node reads input, evaluates it against criteria you define, and sends it down the matching branch. Unlike keyword matching or regex rules, the Classifier uses an LLM to understand intent. "I can't log in and I've been charged twice" routes to billing, not authentication, because the Classifier understands that the core issue is the charge.
### Configure a Classifier node
The Classifier works through **groups**. Each group defines a category with evaluation criteria, and each group becomes a separate output branch on the canvas.
You start with two groups: **Group 1** (which you should rename) and **Other**. The Other group is a fixed catch-all for anything that doesn't match your defined groups. It cannot be deleted.
For each group, write natural language **Evaluation Criteria** describing what qualifies:
```text theme={"dark"}
Group: Billing
Evaluation Criteria: The input relates to charges, payments,
invoices, refunds, subscription changes, or pricing questions.
```
```text theme={"dark"}
Group: Technical Support
Evaluation Criteria: The input describes a bug, error, system
outage, integration failure, or requests help with configuration.
```
Click **+ Add Group** to create additional categories. Each new group adds another output branch on the canvas, letting you wire different downstream logic for each category.
The **Auto-extract relevant data** checkbox (on by default) tells the Classifier to pull out the data points relevant to the matched group and pass them downstream. The next node receives a focused extraction, not the raw input.
The Classifier has its own **LLM settings** in the advanced configuration, independent of any agent. You can choose a fast, inexpensive model for classification while reserving a more capable model for complex reasoning in Agent nodes.
The **Additional instructions** field lets you add examples or edge case guidance ("When the input mentions both billing and technical issues, prioritize billing").
## Condense and extract with the Summarizer node
The Summarizer node distills large inputs (full API responses, long documents, multi-message histories) into what the next step needs. Beyond simple condensation, you can instruct it to extract specific fields, reformat data, or highlight only the changes since the last run.
### Configure a Summarizer node
The Summarizer has no agent to select and no groups to define, just an **Instructions** field where you describe what to do with the input.
The default instruction is "Summarize the input content," but you'll almost always want to be more specific:
```text theme={"dark"}
Extract the customer name, account ID, issue category, and
severity level. Ignore the conversation metadata and internal
routing information.
```
```text theme={"dark"}
Compare this report against the previous run's output. List only
the metrics that changed by more than 10%.
```
Like the Classifier, the Summarizer has independent **LLM settings** where you can choose a lighter model, since summarization is less demanding than multi-step reasoning.
**Summarizer node vs. Output Summarizer:** The Summarizer node lives in the middle of your workflow as a processing step between other nodes. The Output Summarizer is part of the END block and formats the final result. Use the Summarizer node when an intermediate step produces too much data for the next step. Use the Output Summarizer when you want to control how the workflow's final result is presented.
## What's next
Deterministic operations: tool invocations, code, email, and OCR.
Conditions, guardrails, wait gates, and nested workflows.