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AI Nodes

AI nodes bring intelligence to your workflows. The AI Agent node is the core AI building block — it handles conversations autonomously with support for tools, knowledge bases, multi-turn memory, and multi-agent handoffs.

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ai.agent

Let an AI agent handle the conversation with the contact. The AI Agent node is a full-featured conversational agent. It can call tools, search knowledge bases, hand off to specialized sub-agents, and maintain multi-turn conversation memory — all within a single workflow node.

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Configuration

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Linked Agents

Instead of configuring everything inline, you can link to a pre-configured agent from the Agents page. When agentId is set, the node inherits the agent’s:

• Instructions (system prompt)
• Model selection
• Tools (HTTP tools, integrations)
• Knowledge (documents, URLs)

Inline configuration overrides linked agent settings where both are specified.

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Tools

AI Agent nodes can use tools during conversation to fetch information and take actions: When tools are configured, the agent autonomously decides when to call them based on the conversation context. Tool results are incorporated into the agent’s response.

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Knowledge & RAG

Agents can access knowledge through two mechanisms:

• Knowledge Links: URLs and documents attached to the agent. Content is scraped and injected into the system prompt as context.
• Document Search (RAG): When documents are uploaded to an agent, the platform performs vector search against the inbound message to find relevant chunks. These are injected into the system prompt automatically.

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Multi-Agent Handoffs

Agents can hand off conversations to specialized sub-agents. When handoffAgentIds is configured:

1. The primary agent receives the message and begins processing
2. If the conversation requires a different specialization, the agent transfers to the appropriate handoff agent
3. The handoff agent takes over with its own instructions, tools, and knowledge
4. The handoff chain is logged in the run step output

Each handoff agent is a fully configured agent with its own system prompt, tools, and knowledge base.

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Exit Conditions

Define conditions that end the agent’s turn and route to the next node in the workflow: Exit conditions create output handles on the node, enabling branching logic based on the agent’s assessment of the conversation.

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Outputs

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Connections

• Inputs: Single input handle receiving conversation context
• Outputs: Default output handle, plus one handle per exit condition

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Multi-Turn Conversations

AI Agent nodes automatically maintain persistent multi-turn memory. Each WhatsApp contact gets a dedicated conversation thread, so the agent remembers previous messages without you managing context manually.

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Example Use Cases

Customer support agent: Configure with your product knowledge base, connect HTTP tools for order lookup, and set exit conditions for “needs_human” (routes to wa.handoff) and “issue_resolved” (routes to a satisfaction survey). Sales qualification agent: Link a sales-focused agent with web search enabled, set tone to “professional”, and configure handoff agents for “technical_questions” (routes to a technical specialist agent) and “pricing_inquiries” (routes to a pricing agent).