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“Automation” in Switchbord isn’t one feature — it’s two complementary systems that work on the same underlying data. Journeys give you deterministic, visual, multi-step flows. Margaret gives you an AI assistant that can draft journeys, templates, and replies from a plain-language description, with a human always approving before anything commits. This page ties the two together.

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Two ways to automate

Journeys — scripted flows

Built on a visual canvas of message, wait, condition, and goal nodes. Deterministic: the same trigger and contact state always produce the same path. Best for flows you want to reason about precisely — onboarding sequences, abandoned-cart reminders, drip campaigns.

Margaret — AI-assisted drafting

Describe a flow in plain language — “draft a lead-capture journey that triggers on the keyword ‘preventivo’ and collects name, phone, email” — and Margaret proposes a journey graph as a draft. You review and approve before anything is created.
These aren’t mutually exclusive: the common pattern is asking Margaret to scaffold a journey, then refining the graph by hand in the Graphical Journey Editor.

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Where Margaret fits into journeys

Margaret is page-aware. When you’re on the journey editor, she sees the current graph shape, your selected node, and its triggers/actions — so “add a step that waits 2 hours then sends a follow-up” acts on the journey you actually have open, not a generic template. See Margaret — page-aware grounding for how this context-passing works across the product.

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The approval boundary

Every automation surface in Switchbord — journeys, Margaret, and connector integrations — follows the same rule: nothing customer-visible or data-mutating happens without a human approving it first.
  • Margaret never sends a message or commits a journey change silently; she proposes a draft and waits for Approve.
  • The gb-agent connector — an external customer-agent integration that can propose replies — is restricted to a strict dry-run boundary: it can suggest a draft_reply or hand off with human_review, but it cannot send messages, write to the CRM, assign conversations, or create tasks. Every effect is a manual operator action.
This means you can let automation get quite sophisticated — AI-drafted flows, AI-suggested replies — without giving up the ability to review every consequential action before it happens.

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Choosing which to use

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See also

  • Journeys — the scripted-flow primitive.
  • Margaret — full assistant capabilities, slash commands, and workspace memory.
  • gb-agent Connector — the dry-run boundary for external AI-proposed replies.
  • Data Model — ai_agents, ai_threads, and ai_agent_executions.