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Rajiv Shah – AI Problem Framing for Agentic AI

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Rajiv Shah - AI Problem Framing for Agentic AI

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Category: A.I Tags: AI Problem Framing for Agentic AI, Rajiv Shah, Rajiv Shah - AI Problem Framing for Agentic AI
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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

AI Problem Framing for Agentic AI

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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