Artwork for Agents and Engineers | Agentic AI, Software & Agentic Engineering

Agents and Engineers | Agentic AI, Software & Agentic Engineering · August 25 · 41 min

Search is Eating AI

Dan is joined by Hugo Bowne-Anderson and Doug Turnbull. Hugo is an independent data and AI scientist who has advised and taught teams at Netflix, Meta, and Amazon. Doug is an independent consultant and search expert with experience at Shopify, Reddit, and Wikipedia, and the author of "Relevant Search" and "AI Powered Search." In this episode, they discuss what agentic search actually means and the 3 primary ways to implement agentic search. Hugo argues that search is becoming a core skill because agents can handle questions that require repeated retrieval and synthesis. To improve agentic search, most teams should start with evals. Establish a retrieval baseline, find areas for improvement, and iterate. Defining a “good” retrieval baseline is product-specific as a healthcare assistant may need its first result to be correct, while an e-commerce system may succeed by offering several useful options. Doug and Hugo argue that the lack of good training examples means its often useful to read and write your own search code. As to other code, Hugo describes the "dark factory" pattern, where code is written by AI agents but cannot be directly read by humans. How do we design systems for a world where the primary consumers of code are AI agents, not humans? Full episode notes Transcript Chapters (00:00) - Agents, engineers, and reverse centaurs (01:24) - Three paths for agentic search (05:32) - Teaching production-ready enterprise agents (07:06) - Why search is eating AI and data (10:24) - Start with evals and retrieval baselines (16:08) - Writing code to build judgment (18:45) - Learning and building beyond code (24:39) - Verification in a world of abundant code (29:52) - Resisting agent-driven feature sprawl (33:38) - Specialized models and better agent interfaces ⠀ Links from the show -------------------- Build Production-Ready AI Agents for the Enterprise Vanishing Gradients retrieval-augmented generation BM25 NDCG Terence Tao dark software factories lights-out manufacturing Unix philosophy OpenClaw Simon Willison ⠀ Guests ------- Hugo Bowne-Anderson, Independent Data and AI Scientist Website LinkedIn X GitHub Bluesky ⠀ Doug Turnbull, Principal, SoftwareDoug Website LinkedIn X GitHub Bluesky ⠀ Follow the podcast ------------------- LinkedIn Threads Instagram TikTok ⠀ Follow Dan Gerlanc ------------------- X LinkedIn Threads Bluesky

0:00 · Agents, engineers, and reverse centaurs-41:18

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In this episode, they discuss what agentic search actually means and the 3 primary ways to implement agentic search. Hugo argues that search is becoming a core skill because agents can handle questions that require repeated retrieval and synthesis.

To improve agentic search, most teams should start with evals. Establish a retrieval baseline, find areas for improvement, and iterate. Defining a “good” retrieval baseline is product-specific as a healthcare assistant may need its first result to be correct, while an e-commerce system may succeed by offering several useful options.

Doug and Hugo argue that the lack of good training examples means its often useful to read and write your own search code. As to other code, Hugo describes the "dark factory" pattern, where code is written by AI agents but cannot be directly read by humans. How do we design systems for a world where the primary consumers of code are AI agents, not humans?

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