The next level beyond workflow automation is AI agents — systems that can understand a goal, break it into steps, execute those steps, observe the results, and adapt. Here’s how we build them.
What Makes Something an “AI Agent” vs. an Automation?
A regular automation follows a fixed path: if X, then Y, then Z. An AI agent can reason about what to do next based on intermediate results.
Automation example: New form submission → send SMS → wait 24 hours → send email
AI Agent example: New form submission → Claude reads it → decides which follow-up strategy makes most sense → executes that strategy → reads the response → decides next step accordingly
The Architecture
Our multi-step agents typically look like this in n8n:
- Input node: Receives the trigger (webhook, schedule, API call)
- Context node: Pulls relevant data from CRM, email history, previous interactions
- Claude reasoning node: Feeds all context to Claude with instructions and a list of available tools
- Tool execution nodes: Send SMS, update CRM, create calendar event, etc.
- Observation loop: Claude reviews what happened and decides whether to continue or stop
Real Example: Dormant Lead Reactivation Agent
We built an agent that runs weekly, identifies leads that went cold 30-90 days ago, researches each one (checks if their website changed, if they have new Google reviews, if their business has grown), then crafts a highly personalized reactivation message for each one.
One client recovered $47,000 in deals in the first month from dormant leads. Ask us about building an agent for your business.