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Rajiv Shah – AI Problem Framing for Agentic AI
Most AI teams think their biggest challenge is:
Model performance
Prompt engineering
Tool selection
But in reality…
The biggest failure happens before any code is written
They’re solving the wrong problem.
AI Problem Framing for Agentic AI by Rajiv Shah is built around a critical idea:
AI success is determined by how well you define the problem — not how well you build the solution
The Core Idea: Most AI Failures Are Framing Failures
Many AI projects:
Work in demos
Hit evaluation metrics
Still fail in production
Why?
Because they solve something that doesn’t actually matter
According to the course, teams often spend months building systems… only to realize they tackled
the wrong problem from the start

The Shift: From “How Do We Build This?” → “Should We Build This?”
Traditional AI thinking:
Choose model → build system → optimize
Inside this course, the order flips:
Define problem → test assumptions → choose approach → then build
This shift alone can save:
Months of development
Significant cost
Entire failed projects
The “5-Step Loop” Framework
At the center of the training is a structured thinking model:
The Loop:
Outcome – What are we actually trying to achieve?
Assumptions – What must be true for this to work?
Alternatives – Is AI even the best solution?
Trade-offs – What are we sacrificing?
Signals – How do we know it’s working?
This loop becomes:
A repeatable decision-making system for every AI project
Why This Matters More in the Agentic AI Era
With AI agents:
Systems are more complex
Costs are higher
Failures are harder to detect
Which makes:
Problem framing exponentially more important
The wrong framing doesn’t just create bugs – it creates entire systems that shouldn’t exist.
Where This Course Has the Most Impact
AI Problem Framing for Agentic AI is especially relevant for:
AI engineers
Product managers
Startup founders
Teams building AI agents
It’s less about coding… and more about:
Thinking correctly before building anything





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