Product Growth Podcast

AI Agents for PMs in 69 Minutes — Masterclass with IBM VP

37 snips
Sep 5, 2025
Guest
Armand Ruiz
In this engaging discussion, Armand Ruiz, VP of AI Platform at IBM and a leading voice in AI, explores the fine line between AI agents and traditional chatbots. He introduces a four-step framework essential for developing effective AI agents and explains how Retrieval-Augmented Generation (RAG) systems are reshaping enterprise AI. The conversation dives into how these innovations impact product management roles, emphasizing the need for collaboration and continuous improvement in AI processes. Armand also shares insights into the evolving challenges of implementing RAG in business contexts.
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INSIGHT

Four-Step Agent Loop

  • Agents deliver automation by chaining reasoning, planning, action, and reflection.
  • This four-step loop enables agents to convert prompts into executed, improving workflows over time.
ADVICE

Prototype First, Scale With Code

  • Use low-code tools to prototype agents quickly, then move to Python frameworks for complex needs.
  • Try LangGraph, LlamaIndex, Autogen or no-code builders like Lindy and n8n based on scale.
INSIGHT

Why RAG Dominates Enterprise

  • Retrieval-augmented generation (RAG) injects up-to-date company data into LLMs without retraining.
  • RAG fits 90% of early enterprise use cases because it connects both structured and unstructured sources.
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