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Vanishing Gradients

Vanishing Gradients

Written by: Hugo Bowne-Anderson
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A podcast for people who build with AI. Long-format conversations with people shaping the field about agents, evals, multimodal systems, data infrastructure, and the tools behind them. Guests include Jeremy Howard (fast.ai), Hamel Husain (Parlance Labs), Shreya Shankar (UC Berkeley), Wes McKinney (creator of pandas), Samuel Colvin (Pydantic) and more.

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Episodes
  • Building an Enterprise AI Agent for Healthcare

    Building an Enterprise AI Agent for Healthcare
    Jul 17 2026
    Every capability in an agent needs its own evidence and release bar. A model-provider slip, an incorrect tool call, and a wrong fertility-benefits answer should not be held to the same pass rate.William Horton, Staff AI Engineer at Maven Clinic, joined us the day after Maven Assistant reached its first external users. The agent helps members inside Maven Clinic’s women’s and family healthcare platform find providers, manage appointments, navigate Maven, and get basic health information. William had spent much of launch day reading chat traces and turning the surprises into product decisions and tests.William shows how a production failure moves through Maven’s system: the trace becomes a regression case, code handles deterministic checks, and LLM judges cover behavior that cannot be reduced to exact outputs. Human labels calibrate those judges, while the consequence of a wrong answer determines whether the capability ships. You can apply the same release workflow to the agent you are building now.“For a lot of our tool-call evaluation, I’ll accept that it runs ten times and passes nine times. Going for that ten out of ten is just not worth the effort.”— William Horton, Staff AI Engineer, Maven ClinicYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You'll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off 👈In This Episode* The architecture behind Maven Assistant. A stronger lead agent routes requests to four narrower specialists for appointments, provider search, health questions, and Maven support. Hard guardrails run before dynamic routing.* Why an enterprise healthcare assistant only needs 15 to 20 tools. Maven divides a manageable toolset across its specialists instead of exposing one model to hundreds of choices. Existing APIs become safer agent tools, with user identity and other application state injected by code.* Turn failures into the cheapest reliable eval. A response claiming the agent was “made by Google” became a string check, tool calls are verified deterministically, and LLM judges handle clinical accuracy and other qualitative behavior.* Set release thresholds from the consequences. Nine passes in ten can be acceptable for a cheap failure. Maven withheld benefits answers that could influence tens of thousands of dollars and routes self-harm language directly to human support.* Let production change the product and the test set. Early chats changed the roadmap, became regression cases, exposed weaknesses in the judges, and supplied realistic opening messages for simulated users.Join the Four-Month Follow-UpThis episode was recorded live inside our Building AI Applications course the day after Maven Assistant reached its first external users. By the follow-up four months later, Maven will have a much larger body of real conversations. William will return to compare the launch assumptions with what members actually used, which evals changed, and how newer models altered the system.Register to join the livestream or receive the recording afterwards.Resources* Maven Clinic* Maven introduces Maven Intelligence* Google Agent Development Kit👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You'll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off 👈How You Can Support Vanishing GradientsVanishing Gradients is a podcast, workshop series, blog, and newsletter focused on what you can build with AI right now. Over 70 episodes with expert practitioners from Google DeepMind, Netflix, Stanford, and elsewhere. Hundreds of hours of free, hands-on workshops. All independent, all free.If you want to help keep it going:* Become a paid subscriber, from $8/month* Share this with a builder who’d find it useful* Subscribe to our YouTube channel* Join one of our other workshops here Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe
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    1 hr and 9 mins
  • What Claude Fable Means for Coding Agents

    What Claude Fable Means for Coding Agents
    Jul 8 2026
    Nicolay Gerold works all day and night on AMP, one of the most interesting coding-agent harnesses out there.If you’re building with coding agents, this conversation will help you understand: * when to trust the model, * when to build harnesses around it,* which model is worth paying for, * which programming languages gives the agent better feedback, and * when to take the keyboard back.Coding-agent products are living inside a blender. Opus 4.8 to Fable changes what the model can be trusted with, eats a workflow, and suddenly the best product decision is to delete code.AMP had handoff because long agent threads used to get messy. Compaction would lose the plot, the model would make worse decisions, and the product needed a way to move the work somewhere cleaner. Then compaction got better. The model ate the feature. AMP killed it.Builders inherit the annoying product test: does this harness code help inspect, verify, recover, or merge model work, or is it just babysitting yesterday’s model?Nico and Hugo riff on why loop engineering is overrated (and when to use it), why Fable is the first model with real engineering taste, and why you should stop writing Python code today and start writing TypeScript and Rust for all your AI Engineering workflows.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You'll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off 👈In This Episode* Coding-agent harnesses today: compaction, sandboxes, review flows, and the features frontier models are starting to absorb.* Why AMP keeps deleting its own features when models get better.* The test for every harness feature: does it make the agent’s work easier to inspect, verify, or recover from?* Local agents, cloud sandboxes, and where each fits when bugs, issues, logs, or customer feedback turn into code changes.* Background agents without auto-merge fantasy: how useful work comes back as branches, checkouts, or review candidates.* Loop engineering in practice: tight loops with clear objectives, broad loops that create review overload, and where builders should draw the line.* When deterministic code beats an AI step, and when a single agent with the right tools can replace brittle orchestration.* The TikTok problem for coding: hundreds of agent threads, fragmented attention, and why loop engineering can become a trap.- The TikTok problem for coding: hundreds of agent threads, fragmented attention, and why loop engineering can become a trap.Resources* AMP* AMP Owner’s Manual* Nicolay Gerold’s Show Us Your Agent Skills dossier* Clio: Privacy-Preserving Insights into Real-World AI Use* TigerBeetle TigerStyle* How to Build A Coding Agent with Nico and Hugo Build AI Agents From First Principles👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You’ll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off. 👈How You Can Support Vanishing GradientsVanishing Gradients is a podcast, workshop series, blog, and newsletter focused on what you can build with AI right now. Over 70 episodes with expert practitioners from Google DeepMind, Netflix, Stanford, and elsewhere. Hundreds of hours of free, hands-on workshops. All independent, all free.If you want to help keep it going:* Become a paid subscriber, from $8/month* Share this with a builder who’d find it useful* Subscribe to our YouTube channel* Join one of our other workshops here Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe
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    1 hr and 2 mins
  • The Future of Agentic Data Science

    The Future of Agentic Data Science
    May 25 2026
    So I think we’re really at a historical moment, and the opportunity is massive. Almost 15 years ago, we were promised that data science was going to be this incredible thing and create all this value for people. And I think nowadays it’s mostly viewed as a cost center in most companies. I think we can really now fulfill that original promise with agentic data science. Thomas Wiecki, Co-creator of PyMC and Founder at PyMC Labs, joins Hugo to talk about how agentic data science is finally fulfilling the promise of Decision Intelligence.We Discuss:* Decision Engines: Transform data science from a cost center providing cryptic answers into a real-time decision intelligence hub delivering actionable outcomes;* Tame the “Garden of Forking Paths”: Overcome human shortcuts by running parallel analyses to provide an honesty check, revealing the true robustness of business conclusions;* Multiplayer Data Science: Foster organizational learning by moving agents into team chats, democratizing “what-if” questions and reducing context-switching friction;* The Full Agentic Data Science Stack: Beyond harness and skills, the full stack includes orchestration for parallel analyses and a causal eval layer to measure actual outcome improvement;* Agentic Dashboards: Move beyond static BI; use chat interfaces to inquire into models and generate real-time, custom visualizations for specific follow-up questions;* Encode Professional Judgment as Skills: Elevate agent performance by encoding expert domain standards and high-fidelity workflows into specific Agent Skills, rather than relying on LLM pre-training;* Ground Decisions in Generative Processes: Prevent hallucinations by forcing agents to model underlying physical or behavioral processes, providing a programmatic guardrail aligned with market realities;* Scripted Causal-Bayesian Workflows: Their methodologically structured nature—from prior elicitation to posterior predictive checks—makes Causal-Bayesian workflows inherently automatable for agents;* Iterative Autonomy via Skills: Achieve autonomy iteratively: verify workflows with human oversight, then encode verifiable parts as skills to hand off trusted tasks;You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!👉Want to learn how to apply agentic engineering to the world of data science? Come build the future of Agentic Data Science with us in our upcoming course. It’s a live cohort with hands on exercises, capstones, and reusable agent skills, OSS code, and notebooks that will 10x your data science projects. Sign up here and use the code ADSVG10 for 10% off. Hit reply to enquire about group discounts.👈LINKS* Thomas Wiecki on LinkedIn* PyMC Labs* Open-Sourcing Decision Lab: Scaling AI Judgment in Data Science (PyMC Labs blog)* Decision AI Discord* Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results (Sage Journals)* The Agent Harness Reading List* Show Us Your Agent Skills (GitHub)* Agentic Data Science course with Hugo, Thomas, and Luca (10% off with code ADSVG10)* Upcoming Events on Luma* Vanishing Gradients on YouTube* Watch the podcast video on YouTube👉Want to learn how to apply agentic engineering to the world of data science? Come build the future of Agentic Data Science with us in our upcoming course. It’s a live cohort with hands on exercises, capstones, and reusable agent skills, OSS code, and notebooks that will 10x your data science projects. Sign up here and use the code ADSVG10 for 10% off. Hit reply to enquire about group discounts👈 Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe
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    1 hr and 5 mins
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