Custom prompt

The freeform task: write the instruction yourself. Everything the agent step can do — structured output, tools, memory, knowledge — hangs off this task.

Config

Prompt, optional system prompt, and the output schema.

• Prompt (required) — the instruction. It's template-rendered before sending: the model sees only the rendered text, so every value it needs must be interpolated — {{ payload.text }}, {{ payload.customer.email }}. Describing a field in prose delivers no data.
• System prompt — optional persona/rules sent as the system message. Also template-rendered.
• Output schema — optional JSON Schema. This picks the mode:

Two modes

Structured (schema set) — the model returns JSON matching your schema; each top-level property lands at payload.data.<field> for downstream steps:

{ "ok": true,
  "data": { "intent": "refund", "urgency": "high" },
  "raw": "…", "usage": { "input_tokens": 812, "output_tokens": 41 },
  "error": null }

Text (no schema) — the model's prose lands at payload.text.

Set a schema whenever a later step references specific fields — {{ payload.data.intent }} only exists in structured mode. Text mode is for terminal steps: the last summary before a send.

Tools

Optionally hand the agent tools; it decides when to use them:

• calculator — real arithmetic instead of model math.
• web — the web: live search for pages it has no URL for (results injected as context, platform model only, billed per result) plus fetching pages mid-run — "is google.com up?" finds the status page and reads it. With a schema set it's search only, so the answer stays structured.
• code — sandboxed JavaScript for on-the-fly computation.
• your connected apps — any operation of a connected app as a tool (look up a Slack user, read a thread, add a reaction…). The AI adds these when it builds; they show as chips here.
• your workflows — any workflow with a sub-workflow trigger becomes a callable tool; the agent runs it and reads its result.

With looping tools (calculator / web fetches / code / workflows) the agent runs a tool-calling loop and answers in text mode — payload.text, plus a payload.tool_calls trace; the schema is ignored.

Memory & knowledge

• Memory — pick an AI memory and the agent remembers prior turns across runs. The session key (e.g. {{ payload.chat_id }}) keeps different conversations separate; blank shares one session. With memory the agent answers in text mode.
• Knowledge — pick a knowledge base and the agent retrieves the most relevant chunks (default 5, tune with Retrieved chunks) and answers grounded in them.

Gotchas

• The #1 failure: prose that mentions data instead of interpolating it. summarize the ticket sends nothing; summarize: {{ payload.ticket.body }} sends the ticket.
• Tools + schema don't mix — a tool loop always answers as text. Need both? Chain a second agent step that structures the text.