A single AI agent can handle simple tasks well. But business processes rarely involve just one step. Client onboarding requires document collection, data entry, compliance checks, welcome emails, and calendar scheduling. No single agent handles all of that at expert level.<\/p>\n
Multi-agent workflows solve this by assigning each step to a specialist:<\/p>\n
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The result:<\/strong>\u00a0A complete business process runs end-to-end with minimal human intervention. Each agent does one thing well. Together, they do what used to require a full team.<\/p>\n Start with one process you repeat weekly, then map it into agent-sized steps.<\/strong>\u00a0The biggest mistake is trying to automate everything at once. Pick your most time-consuming repeatable workflow and break it down.<\/p>\n Here\u2019s the step-by-step approach:<\/p>\n Step 1: Map your process.<\/strong>\u00a0Write out every step in your current workflow. For client onboarding, that might be: receive inquiry, collect documents, verify information, create client profile, send welcome package, schedule kickoff.<\/p>\n Step 2: Group steps into agent roles.<\/strong>\u00a0Each agent should own 1-3 related steps. Don\u2019t create one agent per step – that\u2019s over-engineering. Group by function: communication, data processing, scheduling.<\/p>\n Step 3: Define the handoff rules.<\/strong>\u00a0How does Agent A know to pass work to Agent B? Usually it\u2019s trigger-based: \u201cWhen intake form is complete, notify document processing agent.\u201d On LaunchLemonade, you set these as simple if-then rules.<\/p>\n Step 4: Set guardrails for each agent.<\/strong>\u00a0What can each agent do? What requires human approval? A communication agent might draft emails freely but need approval before sending anything to clients. Define these boundaries upfront.<\/p>\nHow Do You Build a Multi-Agent Workflow Without Code?<\/h2>\n