Episode 63: Why Gemini 3 Will Change How You Build AI Agents with Ravin Kumar (Google DeepMind)

Vanishing Gradients

Gemini 3 is a few days old and the massive leap in performance and model reasoning has big implications for builders: as models begin to self-heal, builders are literally tearing out the functionality they built just months ago... ripping out the defensive coding and reshipping their agent harnesses entirely.

Ravin Kumar (Google DeepMind) joins Hugo to breaks down exactly why the rapid evolution of models like Gemini 3 is changing how we build software. They detail the shift from simple tool calling to building reliable "Agent Harnesses", explore the architectural tradeoffs between deterministic workflows and high-agency systems, the nuance of preventing context rot in massive windows, and why proper evaluation infrastructure is the only way to manage the chaos of autonomous loops.

They talk through:

• The implications of models that can "self-heal" and fix their own code
• The two cultures of agents: LLM workflows with a few tools versus when you should unleash high-agency, autonomous systems.
• Inside NotebookLM: moving from prototypes to viral production features like Audio Overviews
• Why Needle in a Haystack benchmarks often fail to predict real-world performance
• How to build agent harnesses that turn model capabilities into product velocity
• The shift from measuring latency to managing time-to-compute for reasoning tasks

LINKS

• From Context Engineering to AI Agent Harnesses: The New Software Discipline, a podcast Hugo did with Lance Martin, LangChain
• Context Rot: How Increasing Input Tokens Impacts LLM Performance
• Effective context engineering for AI agents by Anthropic
• Upcoming Events on Luma
• Watch the podcast video on YouTube

Join the final cohort of our Building AI Applications course starting Jan 12, 2026: https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav