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Vanishing Gradients: Daily Summaries Delivered
by Hugo Bowne-Anderson
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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- 1 weeks ago1h 4m
Four Months Inside a Production AI Agent: What Real Users Changed
When ML/AI Engineer William Horton last joined me, Maven Assistant had reached its first external users the day before. The healthcare AI agent was available to 20 percent of Maven Clinic’s users, and the team had deliberately withheld answers about benefits. A wrong response could shape a decision involving $15,000 of fertility coverage, and the evals had not earned the right to ship it.Four months later, Maven Assistant is available to 100 percent of users, benefits answering is live, and weekly conversation volume has grown by roughly ten times. Real usage also overturned part of the roadmap. The team had invested heavily in provider search and appointment tools, but 50 to 60 percent of early conversations were basic health questions such as whether someone could eat tuna while pregnant.Production changed the engineering system too. An emergency guardrail told someone already in the ER to go to the ER. Zendesk content told people already using the Maven app to open the app. A newer model failed an upcoming-appointments eval because it correctly noticed that the mocked appointments were in the past.William explains how Maven turns those failures into deterministic tests, LLM judges, synthetic negatives, and manual review. He also walks through the move from Gemini Flash models toward newer OpenAI models, what GPT-5.6 and Fable mean for a production agent, why model upgrades can make old prompt instructions obsolete, and why open-weight models still have to justify their GPU, infrastructure, and engineering costs.“If anybody tells you that they’ve got their evaluations so good that they can just swap a model and know, with no manual review, that it’s going to be better, that person is probably lying, or they work at one of three places in the world.”— William Horton, Staff Machine Learning 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 code production10 for 10% off. 👈In This Episode* What changed between 20 percent and 100 percent rollout. Benefits answering cleared its release bar, Maven Assistant reached the remaining users, and weekly conversation volume grew by roughly ten times.* Why real usage beat the original roadmap. The complex provider and appointment agents received less traffic than expected, while 50 to 60 percent of early conversations were basic health questions.* How production failures enter the evaluation system. William estimates that the deterministic tool-use layer now involves more than 1,000 test scenarios, while clinical quality, empathy, and completeness still require judges, human calibration, and manual work.* When the model is right and the eval is broken. GPT-5.6 Terra rejected an “upcoming” appointment that had already happened, exposing a bad test fixture that the previous model had accepted.* What happens when GPT-5.6 or Fable arrives. A model swap can remove old prompt instructions, add new behavioral failures, change latency and cost, or reveal that yesterday’s harness is constraining a more capable model.* Why Maven moved beyond Gemini Flash 2.5. William discusses adopting newer OpenAI models, keeping real-time chat on smaller models and low reasoning settings, and changing the model without simultaneously rewriting the prompt.* The economics of open-weight models. A smaller self-hosted model still needs an always-on GPU, infrastructure, and engineering attention that could otherwise go into the product.* What William would rebuild today. Provider search and appointment booking probably belong in one agent, and model experiments should begin before the original choice hardens into the architecture.Start With the First EpisodeWilliam first joined Vanishing Gradients the day after Maven Assistant reached external users. In Building an Enterprise AI Agent for Healthcare, he explains the original architecture, how failures become regression cas
- 2 weeks ago1h 8m
Building an Enterprise AI Agent for Healthcare
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
- 3 weeks ago1h 2m
What Claude Fable Means for Coding Agents
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
- May 25, 20261h 4m
The Future of Agentic Data Science
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</
- May 20, 20261h 19m
Agent-Harness.ipynb*
One thing that I don’t like about Claude is that you get into this weird mental state: oh, I think I trust the model. Let’s do the slot machine. Hit click, which puts you in an inactive mode of thinking. Maybe it’s better to use a worse model….Vincent Warmerdam, senior data professional and prolific open-source maintainer (some packages with over a million downloads), now Engineer at marimo, joins Hugo to talk about how the Python notebook is evolving from a static scratchpad into a working agent harness, and what it takes to stay in the loop as a developer when agents are writing most of the code. This episode was originally a livestream Q&A with the Vanishing Gradients audience.We Discuss:* Shared Notebook Canvas: Notebooks act as a shared memory space where agents and humans co-exist, enabling real-time visual feedback by direct manipulation of global state and UI elements;* Speed-of-Thought Models: Faster, open-weight models like Kimi K2 enhance exploratory flow by keeping humans more alert to the code, unlike frontier models that can induce passive thinking;* Pi as a Harness: Vincent favors an agent harness where agents extend themselves rather than reach for MCP, and where hooks can rigidly constrain which files an agent is allowed to read or touch;* Why PRDs Don’t Fit Notebooks: Notebook work is fundamentally exploratory, so the discipline that works for shipping web apps does not transfer cleanly; the one exception is reproducing a paper;* Interactive Code Review: Interactive UIs (e.g., dragging integers) transform code into a physical object, incentivizing developers to actively review and understand agent logic;* Modular “Lego” Components: Provide agents with high-level, well-tested components (”Lego” code) instead of raw boilerplate, creating systems that are easier to debug and modulate;* Algorithm-Driven Visualization: Let the algorithm dictate the visualization needed, rather than choosing visualizations first, revealing the most interesting structures within the data;* Don’t Outsource the Thinking: Pen and paper architectural planning, walks away from the keyboard, and protecting calm remain the most effective ways to keep producing good ideas in the age of AI-generated software.* Agent Auto-Healing: A marimo-specific linter solved 60% of agent errors overnight by letting agents diagnose and fix their own “slop” without complex prompt engineering;* Incremental Generation: Avoid monolithic LLM outputs; generate code one to two cells at a time to prevent laziness and ensure human oversight and learning;Vincent closes on the idea that calm, not the latest frontier model, is the most underrated tool for building well, and that we should study LLM output the way chess players studied the engines that beat them.Vincent gives several live demos toward the end of the episode. He describes them well enough to follow on audio, but the visuals are worth seeing, so check out the YouTube version here.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.👈Also join us for Ep. 3 of <st
- May 12, 20261h 32m
Agentic Engineering and the Lost Art of Verification
> I almost don’t read code now. My approach with Roborev is it’s like my code reader. The mantra is: Roborev reads every line of code that is generated. It gets read multiple times. And so, whenever I push up a pull request, the branch gets re-reviewed. And so by the time I’m merging a pull request into a repository, the code has all been read by agents four or five times minimum. I look at the code in terms of structural detail: does it look right?— Wes McKinney (creator of pandas, POSIT)Wes, Jeremiah Lowin (Prefect), and Randy Olson (Good Eye Labs) join Hugo and his cohost Thomas Wiecki (PyMC Labs) for the premiere of Show Us Your Agent Skills, a live session where guests walk us through the exact skills, workflows, and setups they use to work with agents every day.We Discuss:* Wes McKinney on why he barely writes, or even reads, code anymore, his “software factory” of parallel agents, and RoboRev, the background reviewer that reads every line four or five times before he merges;* The shift from “vibe coding” to agentic engineering, and why verification, not reading, is the part that actually matters;* Jeremiah Lowin on years of context engineering: trickling voice memos, recorded meetings, and morning briefs into his agent’s memory substrate as a true “second brain”;* Why Jeremiah picked OpenCode specifically for how deeply he can customize its memory, and what he’s building with FastMCP, Prefab, and Cardboard;* Randy Olson on encoding human judgment, like Tufte’s rules for data visualization, directly into agent skills, so the agents themselves perform the verification;* The “digital twin” Randy loads into his agents as a thought partner that pushes back instead of agreeing;* Skills as thin drivers, progressive disclosure, and managing context rot across extended sessions;* The rise of ephemeral, “just for me” software that agents finally make viable.Skills and workflows discussed and shown in the episode:* Wes’s RoboRev background code reviewer, his “software factory” dashboard, and his agentic engineering setup built on the Superpowers skills framework;* Jeremiah’s “explain” skill (which anchors every other skill he has), his voice memo memory pipeline, his FastMCP and Prefab projects, and Cardboard, his ephemeral presentation tool;* Randy’s data visualization verifier skills, his digital twin thought partner prompt, his cron job reports for colleagues, and his reflect and improve skill design pattern.Check out the GitHub repo where we’re starting to drop some of these skills and workflows for you to grab and try yourself.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!Up next on Show Us Your Agent Skills: Hilary Mason (CEO, HiddenDoor), Bryan Bischof (Theory Ventures), Eric Ma (Research DS lead, Moderna Therapeutics), and Tomasz Tunguz (Theory Ventures). Register on lu.ma to join live, or catch the recording afterwards.👉 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. <s
- Apr 23, 202653 min
Next Level AI Evals for 2026
There are a lot of reasons why we should do AI evals. For many companies doing AI evals is the way to build the feedback loop into the product development lifecycle. So it is like your compass. We’re using AI evals as a compass to guide product development and also product iteration. And also, many times we need evals to function as the pass or fail gate in release decisions. Whether this product is good enough for release or whether it is good enough for experiment, evals are also used in that.Stella Wenxing Liu, Head of Applied Science at ASU, and Eddie Landesberg, Staff Data Scientist at Google, join Hugo to talk about why AI evaluation is evolving from “vibe checks” into a rigorous, multi-disciplinary science and how causal inference will take AI evals to the next level in 2026.Vanishing Gradients is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.They Discuss:* Team-Centric AI Evals, integrating product managers, data scientists, and SMEs under a “benevolent dictator” (or not!) to ensure comprehensive and effective evaluation;* Custom Evaluation Metrics, moving beyond generic vendor metrics to analyze raw data and identify specific failure modes, avoiding generic product outcomes;* AI as Policy Evaluation, framing AI evaluation as a causal inference problem to estimate counterfactual performance of new “policies” (prompts, models) and predict online AB test outcomes;* Clear Product Constraints, defining what an AI product should not do with strict guardrails to prevent misuse, control costs, and avoid brand dilution;* Calibrated LLM Judges, statistically aligning LLM-as-a-judge with human experts using causal inference to ensure valid proxies for human welfare and business objectives;* Essential Data Curiosity, fostering a culture of manual data inspection to build intuition before relying on automated error analysis or agents, ensuring effective system design;* Statistical AI Evaluation, shifting from unit-test thinking to non-deterministic distributions, using confidence intervals and power analysis to discern genuine improvements from statistical noise;* Proactive Regulatory Compliance, developing rigorous, defensible internal evaluation standards now to gain a competitive advantage as vague AI regulations move towards enforced compliance;* Human-Centric Benchmarking, grounding AI systems in human judgment and user values, moving beyond automated scores to build resilient and differentiated AI.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!👉 Stella has just started teaching a cohort of her AI Evals and Analytics Playbook course starting this week. She’s kindly giving listeners of Vanishing Gradients 30% off with this link.👈Our flagship course Building AI Applications just wrapped its final cohort but we’re cooking up something new. <a target="_blank" href="https://tally.so/r/EkL
- Apr 15, 20261h 6m
Privacy Theater Is Not Privacy Engineering: What It Actually Takes to Ship Safe AI
Katharine Jarmul, Privacy in ML/AI Expert & Author of Practical Data Privacy, joins Hugo to unpack why most AI privacy advice is theater: and what technical privacy actually looks like when you’re shipping LLMs, agents, and multimodal systems into the real world.In this episode, we dig into how to build defensible systems in an era of AI agents and multimodal models: why system prompts (and your entire agent harness!) should be considered public by default, and why “privacy observability” is as critical as data observability for anyone building with LLMs today. Multimodal is what changes the threat model: identifiers hide in images, audio, and metadata, not just text, and the old anonymization playbook doesn’t cover it.Vanishing Gradients is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.We Discuss:* No Convenience Tax, you don’t have to trade privacy for utility: high-utility AI products can be privacy-preserving through technical controls like privacy routing and input sanitization;* Public Prompts and Harnesses: assume any instruction or secret in a system prompt or agent harness will be exfiltrated; don’t put sensitive info there in the first place;* Privacy Observability, tag and track data flows so information is used only for its original intended purpose: catch design flaws before they become legal problems;* Technical Privacy, implement mathematical and statistical constraints directly into ML systems and data flows so privacy is measurable and enforceable, not aspirational;* Tiered Guardrails, a three-layer approach: deterministic filters for hard rules, algorithmic models for nuanced classification, and internal alignment training for behavioral baselines;* Federated Learning Is Not Privacy, model updates in FL leak sensitive data on their own: you must layer differential privacy or encrypted computation on top, or you’re reverse-engineerable;* Anonymization Spectrum, navigate the “grayscale” of privacy in multimodal AI, balancing data utility and individual risk as identifiers hide in non-obvious places;* Privacy Champions, embed privacy accountability directly into development by training and incentivizing engineers inside product teams;* Red Teaming as Ritual, your goal is to attack yourself: practice thinking like an attacker, and turn privacy testing into an organization-wide creative ritual rather than a siloed security task.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!👉 Katharine is teaching her next cohort of Practical AI Privacy starting April 20. She’s kindly giving readers of Vanishing Gradients 10% off. Use this link. I’ll be taking it so hope to see you there!👈Our flagship course Building AI Applications just wrapped its final cohort but we’re cooking up something new. If you want to be first to hear about it (and help shape what we build), drop your thoughts here.LINKS* Practical AI Privacy course on Maven (10% off with code build-with-privacy)* <a ta
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About Vanishing Gradients
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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