# Glean's Waldo: The Agentic Search Model Making AI Faster and Cheaper

Page: https://stenobird.com/podcast/answer-engine-optimization-aeo-the-ai-search-podcast-7756998/glean-s-waldo-the-agentic-search-model-making-ai-faster-and-cheaper
Text version: https://stenobird.com/podcast/answer-engine-optimization-aeo-the-ai-search-podcast-7756998/glean-s-waldo-the-agentic-search-model-making-ai-faster-and-cheaper.md
Podcast: [Answer Engine Optimization (AEO): The AI Search Podcast](https://stenobird.com/podcast/answer-engine-optimization-aeo-the-ai-search-podcast-7756998)
Published: 2026-05-02T12:33:44+00:00
Episode link: https://share.transistor.fm/s/52bff747
Audio file: https://media.transistor.fm/52bff747/413dfcbb.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/answer-engine-optimization-aeo-the-ai-search-podcast-7756998/episodes/glean-s-waldo-the-agentic-search-model-making-ai-faster-and-cheaper
Duration seconds: 394

## Resource

Glean's Waldo introduces an agentic search model that uses reinforcement learning to optimize the retrieval process. By acting as a specialized intermediary, it significantly reduces latency and token consumption without sacrificing final answer quality.

## Highlights
- Main idea: Waldo functions as a specialized agent that performs query decomposition and tool selection before hitting frontier models
- Efficiency gain: The model achieves 50% lower latency and 25% fewer tokens consumed through optimized retrieval
- Technical mechanism: The system uses a planning loop of query decomposition, iterative search, and evaluation rather than a single lookup
- Strategic shift: The rise of agentic search necessitates a move from traditional SEO to Answer Engine Optimization (AEO) to ensure brand discoverability
- Failure mode: Relying on monolithic models for complex enterprise queries leads to unsustainable costs and slow response times

## Topics

Agentic Search, Glean Waldo, Reinforcement Learning, Enterprise AI, LLM Efficiency, Answer Engine Optimization, Query Decomposition, Retrieval Augmented Generation

## Chapters
- 0:00 — Introduction to Waldo: An introduction to Glean's new agentic search model and its impact on enterprise AI efficiency.
- 1:00 — The Problem with Massive Models: Discussing the latency and token costs associated with sending every query directly to frontier LLMs.
- 2:00 — Efficiency Without Quality Loss: How Waldo uses a curated set of evidence to maintain high-quality answers while reducing compute.
- 3:00 — The Agentic Reasoning Loop: A deep dive into query decomposition, tool selection, and the iterative search process.
- 4:00 — The Future of Enterprise AI: Analyzing the shift from monolithic models to orchestrated systems of specialized agents.
- 5:00 — Optimizing for Agentic Discovery: How brands must adapt their content structure to be found by intermediate search agents.
- 6:00 — Conclusion and Takeaways: Final thoughts on the reality of agentic search models and the changing landscape of AI information consumption.

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