AI Agents for PMs in 69 Minutes — Masterclass with IBM VP
Product Growth Podcast
Navigating the Challenges of Retrieval-Augmented Generation Systems
This chapter examines the hurdles and misconceptions in implementing retrieval-augmented generation (RAG) systems, particularly in customer service. It highlights the necessity of high accuracy and robust engineering practices to effectively utilize internal data for operational success.
Today's Episode
What makes AI agents different from chatbots?
That’s the question we break down from every angle with today’s guest.
Armand Ruiz, VP of AI Platform at IBM, who has been in AI for 16 years and has become one of the most-followed AI voices on LinkedIn.
Armand leads AI platforms at IBM, building the building blocks for enterprises to build AI agents securely. He spends his time meeting with CIOs from the biggest brands who all have AI as their number one priority - and agents as one of their core components.
In our conversation, he breaks down:
* How AI agents differ from the chatbots we know
* The four-step framework every agent needs
* Why RAG systems power 90% of enterprise AI
* How product management changes when agents do the work
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Check out the conversation on Apple, Spotify and YouTube.
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Timestamps
00:00 Intro
02:39 What Makes AI Agents Special
04:40 The Four Steps of AI Agents
07:14 AI Agent Development Frameworks
12:59 RAG Explained
16:55 ADS
18:46 Common RAG Mistakes
26:48 Managing Multiple AI Agents
31:39 ADS
33:57 How AI Changes Product Management
37:43 Problem Investigation vs Feature Factory
41:22 Roadmap to Build AI Agents
43:30 Can Open Source AI Win?
51:39 IBM's AI Strategy
59:32 Career Journey: Intern to VP
1:02:36 Building 200K LinkedIn Followers
1:08:18 Outro----
Key Takeaways
1. AI Agents vs Chatbots: Chatbots respond to queries while agents execute complete workflows. The difference between getting suggestions and getting finished work.
2. Four-Step Agent Framework: Every agent needs Thinking (reasoning), Planning (task breakdown), Action (system execution), and Reflection (learning from outcomes).
3. RAG Dominates Enterprise: 90% of enterprise AI uses RAG to connect LLMs to proprietary data. Success requires 95%+ accuracy through sophisticated evaluation.
4. Vision RAG Unlocks Value: Most business data lives in charts and tables that traditional text-only RAG completely misses.
5. Framework Selection Matters: Use coding frameworks (LangGraph, CrewAI) for complex systems. Use no-code tools (Lindy, n8n) for rapid prototyping.
6. PM Ratios Transform: Traditional 1:6-10 PM-to-developer ratios become 1:2-30 when agents handle research and documentation.
7. Prototypes Beat PRDs: Show working systems instead of 20-page documents teams misinterpret. AI enables functional demos.
8. Open Source Wins: Despite closed-source capabilities, enterprises choose open source for licensing control and infrastructure flexibility.
9. Technical Literacy Essential: Understanding agents, RAG, and frameworks becomes baseline knowledge for everyone, not just developers.
10. Implementation Reality: Enterprise RAG needs heavy data engineering. Teams underestimate accuracy requirements and engineering complexity.
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Newsletters:
AI Agents: The Ultimate Guide for PMs
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