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AgentLed

Agents as Step

Embed an autonomous agent inside a larger workflow as a single step. The agent reasons, calls tools, and returns a result — then the workflow continues with the next step.

What is an Agent Step?

A regular AI step takes an input, runs a prompt, and returns a fixed structured output. An agent step goes further: it can call tools in a loop, reason about the results, call more tools, and decide when it has enough information to produce a final answer. It behaves like a mini-agent embedded inside a workflow step.

This is useful when the exact sequence of actions isn't known upfront — the agent figures it out. A plain AI step is better when the output shape is predictable and you just need a prompt transformation.

AI Step vs. Agent Step

DimensionAI StepAgent Step
Tool useNoneCalls connected app actions dynamically
Reasoning loopSingle prompt → outputReason → act → observe → repeat until done
Output shapeFixed schema defined upfrontAgent's final conclusion (structured or free-form)
Best forSummarize, classify, extract, transformResearch, draft, decide, multi-step retrieval
LatencyLower — single LLM callHigher — multiple calls until done

Configuring an Agent Step

In the workflow editor, add a step and choose Agent as the step type. Key fields:

{
  type: "aiActionWithTools",
  id: "draft_outreach",

  agent: {
    role: "Outreach Specialist",
    goal: "Write a personalised cold email for each contact",
    backstory: "Expert B2B copywriter who avoids clichés",
    model: "claude-opus-4-5"
  },

  prompt: "Draft an outreach email for {{steps.enrich.contact}}. Use the ICP from business memory.",

  tools: [
    { appId: "gmail",    actionId: "send_email",   maxCalls: 1 },
    { appId: "agentled", actionId: "recall_memory", maxCalls: 5 }
  ]
}

agent — role, goal, and backstory shape how the model reasons. These are injected into the system prompt.

tools — which app actions the agent can call, and how many times each. Setting maxCalls prevents runaway tool loops.

prompt — the task for this specific step. References previous step outputs with {{steps.stepId.field}}.

Input & Output

An agent step receives the workflow's accumulated execution context — all previous step outputs are available as template variables. Its own output is stored under its step ID and available to all subsequent steps:

// Step "draft_outreach" output is accessible downstream as:
{{steps.draft_outreach.email_subject}}
{{steps.draft_outreach.email_body}}
{{steps.draft_outreach.reasoning}}

The agent's final answer is its conclusion after all tool calls resolve. Intermediate tool call results are logged in the execution timeline but not passed downstream directly.

Example: Research → Score → Draft

A three-step workflow combining plain AI steps and an agent step:

Step 1AI Step — Enrich each company domain: calls Hunter to find the CEO email. Fixed output: {name, email, domain}.
Step 2AI Step — Score each enriched lead against the ICP from Business Memory. Output: {score, rationale}.
Step 3Agent Step — For each lead with score > 75, research their LinkedIn, recall the user's tone preference from memory, and draft a personalised email. The agent decides how many LinkedIn lookups to do before drafting.

When to Use an Agent Step

  • •The task requires searching, retrieving, or acting before producing an answer — and the exact sequence depends on what the agent finds.
  • •You want the agent to call real tools (send email, write to CRM, recall memory) as part of completing the step.
  • •The output is complex enough to benefit from a persona (role, goal, backstory) that shapes how the model reasons.

Next Steps