Ourbit Master AI — 15-Day Programme

Ourbit Master AI — Day 9: Automation and Multi-Step AI Workflows

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Day 9 — Automation and Multi-Step AI Workflows

Purpose: Design workflows with explicit inputs, AI steps, actions, validation and human gates.

Source/build status: New gap extending Day 8.

Learning outcomes

• Map a multi-step workflow from trigger to recorded outcome.
• Separate AI judgment from deterministic rules and external actions.
• Place validation and human approval gates according to consequence.
• Design observable failure handling and a manual fallback path.

Core lesson — Automation and Multi-Step AI Workflows

Automation should make a repeatable process more dependable, not merely faster. Define the trigger, inputs, transformations or AI decisions, validation, allowed actions, human gates, failure handling and record of outcome. A workflow should make failures observable and preserve a manual path when automation cannot proceed safely.

Worked example

For customer-request triage: receive request → classify intent → extract required facts → draft response → validate against known information → require human approval for defined cases → send → log outcome. A missing customer identifier or uncertain classification should stop or route the workflow rather than silently continue.

Teaching / video direction

Animate the customer-request workflow and deliberately inject a missing field, low-confidence classification and failed action. Show where each condition stops, retries or escalates. Close by moving from workflow execution to systematic output evaluation—Day 10.

Hands-on practical exercise

1. Choose a repeatable low-risk process with at least four steps.
2. Map trigger, inputs, AI decisions, actions and final record.
3. Mark every point where missing or uncertain data can cause failure.
4. Add validation, retry, stop or human-approval behavior at those points.
5. Define what is logged so a failure can be investigated.
6. Run one failure scenario on paper and show how the workflow recovers or exits safely.

Knowledge check

1. What distinguishes a workflow trigger from an AI decision?
2. Why should a workflow make failures observable?
3. Where should human approval be placed?
4. What is the purpose of a manual fallback path?
5. Why can automating a broken process amplify problems?
6. How does Day 9 extend the bounded tool loop from Day 8?

Assessment and remediation contract

The scored daily quiz uses 10 questions; 7/10 (70%) is a pass. Missed concepts map to exact lesson anchors for targeted remediation, including on a passing attempt. Attempts 1–9 below threshold return to remediation and a fresh eligible question set; an unsuccessful tenth attempt restarts the day in a new learning cycle while preserving history. The 50-question validated bank is a learner-release gate and will be scaled only after the Day 1 runtime passes acceptance.

Teaching / video script — 6–8 minute target

Open: Start with the worked scenario, not a generic definition: Triage an incoming customer request.

Teach: Demonstrate the method visibly: Trigger → classify → draft response → validate required facts → human approval when needed → send/log outcome. Pause at the judgment point and explain why the system should not simply continue on autopilot: Automate repeatable low-risk work first; make failures observable and preserve a manual path.

Practice bridge: Ask the learner to reproduce the method on a different low-risk example, mark the verification/control step, and explain the reason for it.

Close: Day 10: evaluate outputs systematically rather than trusting fluency.

Production / QA notes

• Use the worked scenario as the primary visual sequence; avoid generic AI-brain imagery.
• Keep terminology vendor-neutral and define unavoidable technical terms in context.
• Do not equate fluent output with verified truth or AI assistance with autonomous authority.
• Before learner release, fact-check the final lesson and validate every scored item against explicit teaching.