Your 60-Day Pro Builder Plan — From First Chain to Full Multi-Agent Stack

The exact weekly plan for going from beginner automation to professional AI agent architect — with specific daily actions, measurable milestones and the right workflow to build each week.

The jump from building basic workflows to building reliable, production-grade AI agent systems is not about learning new concepts — you have learned them in this guide. It is about applying them systematically, in the right order, with enough practice on each to make it intuitive before moving to the next.

Days 1-10: Prompt Chaining Mastery

Days 1-3: Take one workflow you already have running and rebuild it as a 3-step chain. Add JSON output to the first prompt. Measure the quality difference against the single-prompt version — it should be visible.

Days 4-6: Add a branching chain using Make.com's Router. Your email triage agent from Vol. 1 becomes your first branching chain: classify → route → each branch runs its own follow-up prompt.

Days 7-10: Build Workflow 1 (Dynamic Content Pipeline). This is your first fully chained production workflow. Four prompts, one DALL-E call, one WordPress API call. When it runs without errors for 5 consecutive content briefs, it is production-ready.

Days 11-20: Conditional Logic and Webhooks

Days 11-13: Replace your first scheduled trigger with a webhook. Connect Typeform or Stripe as the source. Verify the response time drops from up to 15 minutes to under 2 seconds.

Days 14-16: Add a 3-route conditional decision to one existing workflow. Build the catch-all route and test it with deliberately unexpected input.

Days 17-20: Build Workflow 4 (Smart CRM Updater). Uses Calendly webhook + Clearbit API + conditional routing + HubSpot update. Your first workflow that touches an external CRM via API.

Days 21-35: Memory and APIs

Days 21-26: Build the customer memory pattern. Set up a Google Sheet as your customer database. Build a Make.com workflow that fetches the row, injects into the system prompt, processes the interaction, and updates the row. Test with 10 simulated interactions.

Days 27-31: Make your first HTTP module API call to Serper or Hunter.io. Build it into an existing research workflow. Verify the results include current information your training-data-limited model could not know.

Days 32-35: Build Workflow 2 (Customer Intelligence Agent). Uses HubSpot webhook, Clearbit API, Serper API, 2-step prompt chain, HubSpot update and Slack notification. This is the workflow that consistently impresses people who see it for the first time.

Days 36-50: RAG and Multi-Agent Systems

Days 36-42: Build your first Flowise RAG pipeline using your own documents. Test with 20 questions you know the answers to. Aim for 90%+ accuracy before proceeding.

Days 43-47: Build Workflow 6 (Document QA Engine). Connect Flowise to Slack. Test with your whole team. Their questions will reveal gaps you did not know existed.

Days 48-50: Design a 2-agent system: research agent and writing agent with explicit handoff via Make.com webhook. Test the handoff with 3 different topics.

Days 51-60: n8n, Error Handling and Production

Days 51-55: Install n8n cloud. Recreate your most-used Make.com workflow in n8n. Add a JavaScript Code node that validates and cleans the AI output. Experience the difference full code access makes.

Days 56-58: Audit every existing workflow. Add error handlers, retry logic and monitoring log entries to each. This is the step that transforms prototypes into production systems.

Days 59-60: Build Workflow 10 (Executive Briefing System) in n8n. Parallel data fetching, 4-step prompt chain, HTML email formatting. This is your first expert-level workflow — and your most personally useful one.