Agents After Dark, powered by Prefactor

Matt Doughty
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Agents After Dark is a podcast exploring what actually happens when AI agents move from demos into real-world production environments. Hosted by Matt Doughty, Co-Founder and CEO at Prefactor the show features conversations with founders, enterprise leaders, engineers, and operators building the next generation of agentic systems, AI infrastructure, and runtime platforms. We go beyond the hype to unpack the technical, operational, and organisational challenges behind deploying AI agents at scale — from governance and security to orchestration, observability, MCP, evaluation, and production readiness. If you're building, deploying, or managing AI agents inside modern organisations, this is where the real conversations happen.

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  1. 3일 전

    Trustworthy AI, Where a Mistake Is Life and Death | Aaron Vanston, Co-Founder and CTO of Buildpass

    What happens when a wrong answer isn't a bad response, it's a safety incident? Construction is one of the least forgiving places to deploy AI. The data is scattered across drawings, site diaries, forms, and credentials, and getting it wrong can mean a defect, a blown budget, or an injury on site. BuildPass is putting AI into that environment anyway, without taking the human out of the loop. In this episode of Agents After Dark, Matt Doughty sits down with Aaron Vanston, Co-Founder & CTO of BuildPass to unpack how you earn trust in AI agents when the downside is life and death. Together they discuss: Why Aaron went from strategic founder back "on the tools," and how AI lets context-rich, time-poor leaders ship ideas againThe case that "SaaS is dead", and why the real opportunity is building the system of record and tools (MCP included) for customers to build on, not another dashboardWhy human-in-the-loop is non-negotiable in construction: no hidden multi-step chains, every generative output shown to the userTrust as one-way vs. two-way doors: confidence scores, sources, and impact analysis before an action graduates from "always ask" to autonomousWhy delegating control to users beats deciding for them, and how to avoid the "accept all" fatigue that made everyone skip permissionsWhy evals are a moat: the tests and captured intent-vs-outcome data that tell you whether a new model is actually better for your usersHow to catch agentic drift when models change underneath you, and why an eval can be as informal as a vibe check A practical guide to running AI agents in a regulated, safety-critical industry, covering human oversight, trust, evals, and quality loops, for any enterprise deploying agents where mistakes actually cost something. About Aaron Aaron Vanston is Co-Founder & CTO of BuildPass, an AI-native construction platform used by hundreds of builders and over 150,000 workers across Australia, New Zealand, and the US. He previously led engineering teams at REA Group and SEEK. About Prefactor Prefactor helps enterprises trust AI in production. Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong. Learn more at prefactor.ai

    40분

  2. 6월 22일

    Writing 90% of Your Code With AI: Vibe Coding at 60 Million Users| Matthew Blode at Linktree

    What happens when AI writes 90% of your code, and 60 million people rely on it? That's the reality for engineers shipping with tools like Claude Code, OpenAI Codex, and Cursor every day. The output is faster and often better, but it raises a harder question: How do you move at that speed without shipping the bug that breaks production? In this episode of Agents After Dark, Matt Doughty sits down with Matthew Blode, who builds Link Apps at Linktree and is an OpenAI Codex ambassador, to get specific about writing, reviewing, and shipping AI-generated code at scale. Together they discuss: Why Matthew now writes over 90% of his code with AI yet writes less code than ever, and outputs moreHow vibe coding actually works in production: plan mode, phased to-dos, and layered review across multiple coding agents plus human code reviewThe invoicing bug that reached production when an AI hallucinated a dependency upgrade, and the QA that would have caught itHow risk tolerance shifts from a startup MVP to a platform with 60 million users, using feature flags, internal dogfooding, and AI code reviewers in GitHubWhy AI evals still need humans in the loop ("who watches the watchman"), and the observability tooling Linktree leans onDelivering AI features users actually want, including the right to opt out of AI entirely, and the data sovereignty question that raisesThe Pixar storyboard method for moving from proof of concept to production without burning money on dead-end demos Whether you are a founder shipping an AI-native MVP or an engineer delivering features to millions, this is a practical look at agentic coding in production: how to keep velocity high and quality intact when the machine writes most of the code. About Matthew Matthew Blode builds Link Apps at Linktree and is an OpenAI Codex ambassador, deep in the AI coding and Codex community. He has built and sold two startups, including Fingertip, an AI-powered website builder, and brings a hands-on perspective on agentic coding, code quality, and shipping AI features safely at scale. About Prefactor Prefactor helps enterprises trust AI in production. As organisations deploy more AI agents, maintaining visibility into performance, risk, and operational quality becomes increasingly difficult. Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong. Learn more at prefactor.ai

    32분

  3. 6월 22일

    MCP in Prod: Becoming the Electricity Every AI Agent Needs | Ajay Jumar, Head of MCP at Infotrack

    What happens when your customer is an AI agent, not a person? For 25 years InfoTrack has sold authoritative legal and property data to people clicking through interfaces. Now it is making those same services callable by AI agents using the Model Context Protocol (MCP). That shift raises a sharper question: How do you actually run MCP in production inside a regulated enterprise? In this episode of Agents After Dark, Matt Doughty sits down with Ajay Kumar, Head of MCP Services at InfoTrack, to break down what it takes to move Model Context Protocol from experiment to production. Together they discuss: Why InfoTrack stood up a dedicated MCP business unit instead of treating AI as an experimentHow an enterprise MCP gateway works in practice: a central registry, a search-and-invoke pattern, and vectorised tools that cut token costs across hundreds of servicesWhy human-in-the-loop elicitation matters the moment an agent can spend money, and why most major providers still don't support it out of the boxWhy hallucination is an agent problem, not an MCP problem, since MCP itself is deterministicHow MCP security is evolving after incidents like the Aura health breachThe shift in customer mindset from "what is MCP?" to "make my agent more productive"Why roughly 62% of companies are experimenting with agents while only 28% are scaling, and what separates the two As agents move from assistants to actors that order, transact, and execute, the businesses that win may be the ones whose products are built to be consumed by software. This conversation is a practical guide to running Model Context Protocol in production, covering architecture, security, and go-to-market, for any enterprise starting its own MCP journey. About Ajay Ajay Kumar is Head of MCP Services at InfoTrack, where he leads the company's Model Context Protocol business unit and shapes how AI moves from experimentation into core production workflows. With over 17 years across banking, prop-tech, insurance, and government, Ajay brings an engineering-first perspective on identity, architecture, and enterprise-scale delivery, and on what it actually takes to make agents and MCP work in regulated environments. About Prefactor Prefactor helps enterprises trust AI in production. As organisations deploy more AI agents, maintaining visibility into performance, risk, and operational quality becomes increasingly difficult. Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong. Learn more at prefactor.ai

    40분

  4. 6월 9일

    What Does an AI Agent Buy? | Andrew Bird, Head of AI at Affinda

    What happens when software becomes the customer? For decades, products have been designed, marketed, and sold to humans. But as AI agents become increasingly capable of researching, evaluating, and making decisions on our behalf, a new question emerges: What does an AI agent actually buy? In this episode of Agents After Dark, Matt Doughty sits down with Andrew Bird, Head of AI at Affinda, to explore how AI is changing the way products are discovered, evaluated, and purchased. Together they discuss: What happens when agents become decision-makers rather than assistantsHow software discovery changes in an agent-driven worldWhy traditional go-to-market motions may need to evolveHow trust, reputation, and verification influence agent decisionsThe future of procurement in a world of autonomous systemsWhat founders and product teams should be building for as AI adoption accelerates As agents move from copilots to autonomous actors, the way products are bought, sold, and evaluated may fundamentally change. This conversation explores what that future could look like and what it means for businesses today. About AndrewAndrew Bird is Head of AI at Affinda, where he helps organisations apply AI to automate and extract value from complex, unstructured information. Having worked at the forefront of AI adoption through multiple technology waves, Andrew brings a practical perspective on how agents, automation, and AI-driven decision making are changing the way software is built, evaluated, and purchased. About PrefactorPrefactor helps enterprises trust AI in production. As organisations deploy more AI agents, maintaining visibility into performance, risk, and operational quality becomes increasingly difficult. Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong. Learn more at prefactor.ai

    32분
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Agents After Dark is a podcast exploring what actually happens when AI agents move from demos into real-world production environments. Hosted by Matt Doughty, Co-Founder and CEO at Prefactor the show features conversations with founders, enterprise leaders, engineers, and operators building the next generation of agentic systems, AI infrastructure, and runtime platforms. We go beyond the hype to unpack the technical, operational, and organisational challenges behind deploying AI agents at scale — from governance and security to orchestration, observability, MCP, evaluation, and production readiness. If you're building, deploying, or managing AI agents inside modern organisations, this is where the real conversations happen.

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