configuring-ai-agents

Configuring AI Agents

An AI agent is an LLM-powered import step that processes records through an AI model instead of writing them to an external system. Records flow in, the model processes them according to instructions, and structured output flows back into the pipeline.

AI agents handle four concerns:

• Prompt design -- the system instruction that defines the model's behavior, goals, and constraints (up to 1,000,000 characters on the built-in providers). The prompt receives each record as context and must produce output that downstream steps can consume
• Structured output -- json_schema output format forces the model to return data conforming to a JSON Schema, enabling reliable field extraction for mapping. text returns free-form responses. blob returns binary data (image generation)
• Tool use -- the model can call web search, MCP server tools, Celigo Tool resources, or image generation during processing. Tools extend the model's capabilities beyond its training data
• Response mapping -- extract fields from the model's response back into the record for downstream steps. Configured on the flow's pageProcessors[] entry as {"fields": [{"extract": "_json.category", "generate": "category"}]}, but planned when building the agent. A json_schema response is available via _json

AI agents do not require a _connectionId unless using BYOK (bring your own key). Without one, built-in providers use platform-managed credentials; catalog providers are BYOK-only and require one.

Used across flows, APIs, and tools. An agent has no trigger or schedule of its own: it runs when one of those hands it a record, or when a chat request sends it a message. A request like "every morning" or "whenever an order arrives" needs the parent flow built too, with the agent as one of its steps.

AI Agent Imports