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When Should You Build an AI Agent?

Hosted by Priyanka Shetty

Tue, Sep 22, 2026

4:00 PM UTC (1 hour)

Virtual (Zoom)

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Build your Agentic AI PM Team
Priyanka Shetty
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What you'll learn

Know When You Need an AI Agent

Distinguish prompts, workflows, and agents—and choose the right approach for a real PM task.

Identify agent-ready opportunities

Recognize the tasks, workflows, and product problems that benefit from autonomy, tools, memory, and multi-step reasoning

Evaluate value, feasibility, and risk

Assess whether an agent is worth building based on business value, technical feasibility, complexity, and potential risk

Define the right level of autonomy

Decide what an agent can do independently, where human approval is required, and what should not be automated.

Define Your Agent Opportunity

Leave with one clearly scoped agent use case that you can confidently prototype next.

Why this topic matters

AI agents are everywhere but not every problem needs one. Before investing time in building an agent, Product Managers need to determine whether the use case truly requires autonomy, tools, memory, and multi-step reasoning, or whether a well-designed prompt, copilot, or automated workflow would work better. In this Lightning lesson you’ll learn how to evaluate AI opportunities and avoid building an overly complex solution for a simple problem.

You'll learn from

Priyanka Shetty

AI Product Leader (Oracle, S&P Global, Weill Cornell) | AI Strategist & Founder

I’m a product leader, AI strategist, and builder with 15+ years of experience turning complex problems into products people actually use.

I’ve led product strategy and AI-powered experiences across enterprise SaaS and financial services, and today I help product teams and PMs become more AI-native through practical frameworks, hands-on building, and real-world product thinking.

I believe the future of product management isn’t about becoming an AI engineer—it’s about developing the judgment, fluency, and building skills to know what to build, why to build it, and how to make it better.

I teach what I practice: product sense, product taste, AI product strategy, AI prototyping, and the emerging skills every AI Product Manager needs.

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