Chain of Thought | AI Agents, Infrastructure & Engineering

Andy Triedman is a partner at Theory Ventures, and he is building his own software factory, while assessing how startups are leveraging theirs. His read after running agent fleets: the models are frankly good enough for most of the work. The vast majority of things you'd ask an agent to do are not intelligence limited. They are context limited. We have infrastructure work to do - but we can be more ambitious! So the hard part moves upstream. Deciding what the agents should build, what they need to know, and when they should stop and ask a person is now most of the job. We cover: Moving from solving one problem at a time to building systems that solve whole classes of problems, like generating and testing integrations Which tasks still need human approval, and which can be fully handed off Why a short feature spec leaves too much unsaid, and how better questions up front cut review interruptions Keeping shared context and company decisions current, because an agent takes everything it reads as true Inference ROI, and when well-scoped tasks can go to cheaper models Sandboxes and simulated customer environments for agents First steps for teams: shared skills, cloud agents and approval gates Protecting customer data, and organizing agent memory from session traces to company priorities New episodes, the ideas behind them and Conor's essays land in the Chain of Thought newsletter first. Subscribe: https://newsletter.chainofthought.show/ Chapters: (0:00) Agents are context limited, not intelligence limited (0:34) From coding agents to software factories (5:53) What still needs human approval (10:16) Agent loops that improve the product on their own (14:52) Where agent automation breaks down (17:59) The bitter lesson and engineering as experimentation (20:08) From writing code to directing agent fleets (24:18) Shared context and stale decisions (27:25) Inference ROI and cheaper models (30:23) Moving human time from code to customer intent (34:42) Sandboxes and simulated customer environments (37:26) First steps: shared skills and cloud agents (40:14) Protecting customer data and isolating agents (42:32) Context limits and hierarchical agent memory (47:05) What Theory Ventures looks for in founders Connect with Andy Triedman: LinkedIn: https://www.linkedin.com/in/andy-triedman/ Andy at Theory Ventures: https://theoryvc.com/team-members/andy-triedman Theory Ventures: https://theoryvc.com/ Connect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/ Twitter/X: https://x.com/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ YouTube: https://www.youtube.com/@ConorBronsdon More episodes: https://chainofthought.show Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000. Thanks to Walrus, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot Thanks to Inngest, presenting sponsor of season four of Chain of Thought. Agents in production run long - they call models and wait on APIs and people. But the longer agents run, the more they break. Inngest handles that with durable execution. You build your agent as steps in TypeScript, Python, or Go. When a step fails, Inngest retries it with exponential backoff, and completed steps are saved and skipped. Try it out: https://inngest.link/cot-pod Thanks to G2i for sponsoring this episode - for over a decade, they vetted and placed engineers at other companies, from startups to FAANG. Two years ago, they turned that same judgment inward, building their own bench to review RL environments, evals, and training data that models are trained on. Get access: https://fandf.co/3SFxVm6

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 delivers webhooks for companies like Clerk and Resend, and it created Standard Webhooks, the spec OpenAI, Anthropic and Google Gemini have adopted. In this episode of Chain of Thought, Tom joins Conor Bronsdon to talk through designing APIs for agents that adapt, whether agents still need SDKs, and why he wants less code even when agents write it. He also introduces Poke, Svix's new spec for making event-driven APIs work for agents. This episode is sponsored by Svix. We cover: Why events are harder for agents than API calls: the sender controls them, and they arrive outside the agent's session How polling endpoints and Svix's plugins for Hermes and OpenClaw let an agent pick up where it left off Why Tom calls JSON an abomination and webhooks duct tape, and why worse is better anyway Why Svix created Standard Webhooks when everyone implemented webhooks differently, and how the spec let Kong and ngrok add webhook verification Whether agents still need SDKs, and why Svix's skills work like a solutions engineer while its docs stay written for humans Why Tom wants less code even when agents write it, and what piling patches on patches costs Poke, Svix's early spec for letting agents subscribe to and trigger events across services, released so feedback can shape it while it's still malleable Chapters: (0:00) Duct tape, retry storms and the case for simplicity (1:19) Why integrations got easier and harder at once (2:59) How EteSync's webhook requests led to Svix (5:22) Worse is better: why webhooks persist (7:15) What changes when an agent receives the event (9:07) What happens when the laptop closes (11:08) Hermes, OpenClaw and agents that poll (13:03) Letting agents choose their own subscriptions (14:42) The long tail of the web without APIs (18:38) Why Svix created Standard Webhooks (24:17) How Standard Webhooks is governed (26:16) Following real usage, including MCP (28:17) Designing APIs for agents that adapt (30:38) Do agents still need SDKs? (31:25) Skills as documentation for agents (34:52) Less code, even when agents write it (36:19) Agentic development and the offshoring analogy (37:34) Software factories and the plumbing underneath (40:57) Keeping up while the standards change (44:18) What Svix is launching with Poke (46:39) Why Svix released the Poke spec early (47:58) Building for your customers' customers (49:38) Clerk, Resend and webhooks in the background (52:22) Where to follow Tom Links from the episode: Svix: https://link.svix.com/cot Poke: https://www.svix.com/poke/ Standard Webhooks: https://www.standardwebhooks.com Standard Webhooks on GitHub: https://github.com/standard-webhooks/standard-webhooks EteSync: https://www.etesync.com ssot-check: https://github.com/conorbronsdon/ssot-check Most of the Web Will Never Get APIs for AI Agents | Dhruv Batra: https://chainofthought.show/podcast/63-most-of-the-web-will-never-get-apis-for-ai-agents-dhruv-batra/ Connect with Tom Hacohen: LinkedIn: https://www.linkedin.com/in/tomhacohen/ X: https://x.com/TomHacohen Svix: https://link.svix.com/cot Connect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/ Twitter/X: https://x.com/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ YouTube: https://www.youtube.com/@ConorBronsdon More episodes: https://chainofthought.show Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups will get $12,000 in credits, $50,000 for YC companies.

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 mod

Andrea Malagodi, CTO at Sonar, found one engineer with more than 500 PRs at a low cost per PR and another with high spend and few PRs. The two were doing very different work, and Malagodi explains why cost-per-PR dashboards miss that context.

AI agents need your company’s definitions, rules and business context to make sense of its data. WisdomAI co-founder and CPO Kapil Chhabra explains how teams maintain that context as the business changes, who owns it, and why agents also need a harness th