Chain of Thought

What breaks when agents become the customer? Most of the integration layer, APIs, SDKs and webhooks alike, was built for human developers and for servers that are always listening. Agents are neither. Full transcript and chapters: https://chainofthought.show/podcast/78-what-breaks-when-agents-become-the-customer-svix-ceo-tom-hacohen/ When an agent runs on a laptop, closing the lid takes down the tunnel its webhooks come through. When the lid opens, it gets a storm of retries from 12 hours earlier. Svix founder and CEO Tom Hacohen's answer is to let the agent poll for what it missed. Svix deliv

If your agent's memory lives inside one model provider, switching models or harnesses means starting over. You need portable memory - yet when switching agent memory between models, you can swing accuracy by thirteen points or more, depending on which model reads them back. Full transcript and chapters: https://chainofthought.show/podcast/77-switching-models-shouldnt-mean-starting-over-walrus-protocols-kimberly-logan/ Kimberly Logan, Head of Product at the Walrus Foundation, where she builds Walrus Memory, joins Chain of Thought to discuss how to build portable and verifiable memory for AI age

Andrea Malagodi is CTO at Sonar, which builds AI code verification and governance tools. When finance asked about the size of his team's AI bills, he calculated cost per PR. At one extreme was an engineer with more than 500 PRs in a short period and a low cost per PR. At the other was an engineer with very high spend and very few PRs. Full transcript and chapters: https://chainofthought.show/podcast/76-your-best-ai-engineer-might-have-the-worst-metrics-sonar-cto-andrea-malagodi/ Judged on cost alone, the first engineer wins and the second looks like a problem. Malagodi looked closer. The first

An AI agent answering questions about your business needs to understand how that business works. How do you calculate churn? When does your fiscal year end? Who has the authority to change those definitions? Connecting a model to company data leaves those questions unresolved. Full transcript and chapters: https://chainofthought.show/podcast/75-why-context-alone-isnt-enough-for-enterprise-ai-agents-wisdomai-cpo-kapil-chhabra/ That’s the work of context engineering: giving agents the business definitions, instructions and examples they need to interpret your data. But that context changes as th

Laurie Voss co-founded npm - now head of developer relations at Arize, he argues that engineers will increasingly earn their keep as 'product engineers': understanding what users need and directing AI agents to build it. Full transcript and chapters: https://chainofthought.show/podcast/74-ai-codes-product-engineers-decide-what-to-build-laurie-voss/ One example: a bakery owner who knows how to make a croissant but has no interest in building software. Someone still has to turn that owner's needs into requirements. Laurie sees that work becoming central to product engineering, with cheaper code

Genspark went from launch to $250 million in ARR in about a year. Along the way it shipped a card-thin meeting recorder, open sourced an office suite that Wen Sang says one engineer prototyped in a week, and started running product triage with agents instead of product managers. Full transcript and chapters: https://chainofthought.show/podcast/73-gensparks-bet-ai-agents-become-the-users-of-software/ Wen's bet is that agents, not people, become the next users of software. Wen Sang is co-founder and COO of Genspark. In this episode he walks through the company's three-layer architecture (models,

Watch the full conversation on YouTube Full transcript and chapters: https://chainofthought.show/podcast/72-tuning-gpu-performance-with-ai-agents-amds-anush-elangovan-on-rocm-10/ AI agents can keep tuning GPU workloads after you step away from the keyboard. Anush Elangovan, Corporate VP of AI Software at AMD, returns to Chain of Thought to explain how that works with Hyperloom and ROCm 10. Anush and Conor Bronsdon trace the process from installing ROCm through Claude Code or Codex to profiling workloads, finding slow kernels, and testing optimizations while preserving numerical accuracy. Anush

Jaime DeLanghe has spent nine years at Slack turning search, machine learning, and now agents into product. Her team just shipped Slack Code: tag a coding agent like Claude Code, Devin, Codex, or the GitHub agent in a conversation, and it spins up a code channel where everyone in that conversation gets a live development environment, diffs post as artifacts, and the channel winds down when the task is done. Full transcript and chapters: https://chainofthought.show/podcast/71-slack-wants-to-be-the-context-harness-for-code-cpo-jaime-delanghe/ Slack's bet is that AI at work is multiplayer. Agents

Tormod Ree puts 11 or more cameras and dozens of microphones into a single meeting room, then runs computer vision on all of it to figure out who is present, who is talking, and who is looking at whom. He is the chief product and engineering officer at Neat, the Zoom-backed video hardware company. Full transcript and chapters: https://chainofthought.show/podcast/70-11-cameras-dozens-of-mics-the-ai-that-reads-the-room/ Before Neat, Tormod co-founded AVA, a computer vision security company Motorola acquired, and spent close to eight years at Cisco running the Spark Board. He explains how Neat tu

Behind Thomson, the new legal AI model from Thomson Reuters, is a $40 million investment in people, compute, and evaluation methods. The final training run cost just $450,000. CTO Joel Hron, whose teams build Westlaw, Practical Law, and CoCounsel for millions of professionals in more than 100 countries, joined us for the launch to break down why the 175-year-old company chose to own its model layer instead of solely renting frontier intelligence. Full transcript and chapters: https://chainofthought.show/podcast/69-thomson-1-the-new-40m-legal-ai-model-thomson-reuters-joel-hron/ We cover: Why Th

Attackers used to take months, sometimes 270 days, to weaponize a disclosed vulnerability. Now it happens in weeks, minutes if the incentive is there, and independent reports from Mandiant and CrowdStrike show the average time to exploit has gone negative. Full transcript and chapters: https://chainofthought.show/podcast/68-time-to-exploit-is-negative-ai-broke-the-patch-cycle-dan-lorenc/ Dan Lorenc's conclusion: finding flaws is no longer the hard part. Fixing them first is. Dan is the co-founder and CEO of Chainguard. Before that he spent years at Google building the backbone of software supp

Agents are like teenagers: profoundly intelligent, extremely resourceful, no fear of consequence, and missing the judgment to know right from wrong at all times. That's how Cisco President and Chief Product Officer Jeetu Patel thinks about securing AI agents, and it's why he says static allow/block rules are already obsolete. Agents are smart enough to route around them. Full transcript and chapters: https://chainofthought.show/podcast/67-parenting-your-ai-agents-for-best-results-ciscos-jeetu-patel/ Jeetu returns to Chain of Thought to map cyber's third phase: an agent trust platform where sec

Jitender Aswani was customer zero for Presto at Meta, where a billion daily active users generated queries that took hours to return. He watched that drop to minutes, scaled the same technology at Netflix across 300 million subscribers, and now runs engineering and security at Starburst, the $3.35 billion platform built on Trino. Full transcript and chapters: https://chainofthought.show/podcast/66-data-federation-not-centralization-is-what-enterprise-ai-needs/ His argument: every enterprise AI project that stalls is fighting the same hidden battle. The agents can query the model fine. They jus

Charles Guillemet is CTO of Ledger and the founder of the Donjon, Ledger's internal offensive security lab whose job is to break the company's own products before attackers do. He spent a decade in cryptography and hardware security before Ledger, including designing secure integrated circuits. Full transcript and chapters: https://chainofthought.show/podcast/65-you-cant-secure-an-ai-agent-with-software/ His argument is blunt: you cannot secure an AI agent with software alone. As agents start moving real money, API keys and trust scopes leave no physical verification layer, and Charles makes t