Chain of Thought | AI Agents, Infrastructure & Engineering

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.
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 the business evolves, and someone has to keep it accurate.
In this episode of Chain of Thought, WisdomAI co-founder and CPO Kapil Chhabra joins Conor Bronsdon to explain how his team approaches that challenge. We explore how companies maintain shared context, why data teams are taking on the role of AI context engineers, and how a specialized harness plans queries, checks results and repairs errors before returning an answer.
Recorded at WisdomAI’s San Mateo office, this episode is sponsored by WisdomAI.
We cover:
Why the context layer is broader than a semantic layer or catalog, and how it differs from memory
How context drifts, and why a subject-matter expert has to approve changes to shared definitions
Why Kapil sees an AI context engineer role emerging, and why data teams are moving from providing insights to providing context
How WisdomAI's harness decomposes a question, federates queries across data sources and repairs errors, including a customer that replaced a $5M-a-year analytics pipeline
The four ingredients of a trustworthy AI answer: accuracy, consistency, governance and explainability
Live Apps: governed analytics apps built from a single prompt, and what keeps them live
Why your context is your IP and should stay portable
Chapters:
(0:00) Do your agents have the right context?
(1:56) The four ingredients of trust
(5:13) The criticality and impact 2x2
(8:12) Data, context, harness: the hospital analogy
(11:12) What the context layer actually means
(11:48) Specialized harnesses: legal, support, analytics
(13:17) Why only 7% of data leaders have scaled AI
(15:46) What models can't guess: ARR, churn, fiscal years
(16:45) The data stack collapses into the context layer
(20:55) Memory vs. context
(25:26) Are agents the new users of software?
(27:19) Where humans should spend their time
(28:14) Commissioning an AI agent, and who verifies it
(31:28) Context drift and the learning loop
(34:04) Context is a multiplayer game
(35:12) Decompose, query, verify, repair
(38:47) Replacing a $5M analytics pipeline with federation
(42:04) The context development life cycle
(43:29) The AI context engineer
(46:02) Jobs are changing, not disappearing
(46:59) Product, people and process
(51:20) Who decides? Why FDEs can't own your context
(52:22) Data context vs. business context
(54:11) The benchmark: specialized harness vs. general agent
(56:08) Meeting users in ChatGPT, Claude and Slack
(58:48) Static vs. runtime context
(1:00:08) Harness engineering as models change
(1:01:44) Right-sizing AI and Live Apps
(1:04:42) The boring parts: governance, security, caching
(1:06:18) 1,000 dashboards, 50 human-years
(1:08:08) What "live" means
(1:09:19) Are dashboards going away?
(1:12:08) A pipeline app built on a weekend walk
(1:15:53) Who owns the apps?
(1:17:27) Data teams now provide context, not insights
(1:18:56) Building with the WisdomAI MCP
(1:19:43) Your context is your IP
(1:20:48) Closing thoughts: none of that work goes to waste
Links from the episode:
Meet the Modern Data Team (WisdomAI CDO report)
AI Context Engineer (ACE) certification
Live Apps
WisdomAI in ChatGPT Work
Avoid AI Writing
ssot-check
I Paid an AI Agent $8 to Write About its 'Life'
Slack Wants to Be the Context Harness for Code | CPO Jaime DeLanghe
The AI Framework Era Is Over: Why Context Is the Moat | Jerry Liu
Connect with Kapil Chhabra:
LinkedIn
WisdomAI
WisdomAI on X
Connect with Chain of Thought host Conor Bronsdon:
Newsletter
Twitter/X
LinkedIn
YouTube
More episodes: https://chainofthought.show
Thanks to WisdomAI for sponsoring this episode. WisdomAI is the agentic analytics platform for trusted enterprise intelligence: governed context, an analytics harness that makes every answer consistent and verifiable, and Live Apps built from a single prompt. Try Live Apps: https://wisdom.ai/liveapps

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.
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 making software for narrower industries more viable.
We discuss where he still sees a need for human code review and operational knowledge, what he would look for in a computer science course if he were starting out today (and what he wouldn't do), and why he compares AI today to the web in 1997. He is optimistic about the technology and skeptical of the valuations, while leaving one question unresolved: how do junior engineers learn the judgment this work demands?
We cover:
Why the "aha moment" of solving a problem survives even when agents type the code
Where Laurie still sees a need for human code review, operations, and tacit knowledge
Whether a $15,000 coding bootcamp or a theory-heavy CS degree is still worth it
Why AI in 2026 looks like the web in 1997, and what that says about the bubble
How AI-generated pull requests burden open source maintainers, and why Laurie expects cheaper code to pressure closed-source business models
Why the systems analyst returns as the product engineer, and why that means niche software for bakeries and auto parts
Why Laurie predicts open-model competition and diminishing returns could compress frontier-model margins
How apprenticeships could help junior engineers develop product judgment
Chapters:
(0:00) The shift in software jobs
(0:44) Why Laurie is optimistic about the code generation explosion
(3:06) The aha moment moves from typing code to thinking
(5:49) Where agents still need human review and operational knowledge
(8:51) Is college still worth it?
(9:54) Bootcamps versus theory-heavy CS courses
(13:04) We are all product engineers now
(15:55) AI is the web in 1997
(18:19) Exponential growth, the labs' pause, and npm's ten-year curve
(20:07) Barring AGI, AI is a normal technology
(21:48) Block's layoffs and companies staying smaller
(23:14) What the labor data shows: fewer people, more capital
(26:29) Open source as the canary: drowning in AI pull requests
(28:11) AI reimplementations and the pressure on software moats
(30:26) Personal software and the kill-my-SaaS hackathon
(32:32) The bakery and the return of the systems analyst
(34:09) Niche software for specific industries
(35:36) Bootstrapping and the DevTools opportunity
(37:13) What this means for the model companies
(38:20) Frontier-model margins and open-model competition
(39:53) How the bubble pops: scaling laws and diminishing returns
(43:24) Staying private and the trough of disappointment
(45:51) Get good at a domain, not the technology
(50:44) The missing junior ladder is the question of our time
(53:32) Closing thoughts: it's 1997, you can retrain
Connect with Laurie Voss:
Blog: https://seldo.com/
LinkedIn: https://www.linkedin.com/in/seldo/
Twitter/X: https://x.com/seldo
Bluesky: https://bsky.app/profile/seldo.com
Arize: https://arize.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 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
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

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.
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, tools and premium data as the execution layer, a memory layer he calls the second brain, and a collaboration layer called Gen Team), why a meeting note should be the start of work rather than the end of it, the engineering behind the SecondBrain Note, and where he thinks knowledge work goes once agents absorb the busy work.
Disclosure: Genspark provided the SecondBrain Note recorder discussed in this episode at no cost. Genspark is not a sponsor of this episode.
We cover:
Why Genspark builds the self-driving car around the frontier labs' engines, and what that means for people who cannot code
How Genspark's mixture-of-agents architecture routes work across 70+ models, 150+ in-house tools and paid data sets
Evals that grade whether the sales proposal answered the RFP, not whether the model can solve a differential equation
What a meeting turns into a week later when an agent needs it: proposals, pricing models, research, follow-ups
The SecondBrain Note's microphone array, battery decisions, and consent in a two-party state
Why GenOffice went open source, and the one-week prototype story behind it
How Genspark runs product feedback triage with agents and no dedicated PMs
Chapters:
(0:00) Cold open
 (0:32) Geniuses with goldfish memories
 (3:47) Engines and vehicles: Genspark builds the self-driving car
 (6:25) Mixture of agents: models, in-house tools and premium data
 (10:03) Grade the work output, not the intelligence
 (11:22) A meeting note is where the work starts
 (13:25) The second brain: Genspark's memory layer
 (14:45) A thousand recorders, one question for the revenue team
 (16:33) Execution, memory and collaboration layers
 (19:19) Ten days in Bora Bora without a laptop
 (20:07) Gen Team, Slack, and meeting customers where they are
 (21:57) Agents become the users of software
 (24:17) Keeping memories current when the deal changes
 (26:41) Engineering the SecondBrain Note
 (30:18) The note as an API for the room
 (31:19) What deserves hardware and what stays software
 (33:33) Learning hardware supply chains at a two-year-old company
 (35:05) Why GenOffice went open source
 (38:01) What knowledge workers do once the busy work is gone
 (40:07) Building on Genspark with the CLI
 (42:01) Consent, two-party states and the surveillance line
 (43:45) Genspark Claw
 (47:09) Cheaper hardware, deeper integration, and model welfare
 (51:13) Eighty people and a lot of agents
 (53:03) What 2027 looks like
 (59:26) Where to find Wen, and product triage without PMs
Connect with Wen Sang:
LinkedIn: https://www.linkedin.com/in/wen-sang/
Twitter/X: https://x.com/sang_wen
Genspark: https://www.genspark.ai
GenOffice on GitHub: https://github.com/genspark-ai/genoffice
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 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 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 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

Watch the full conversation on YouTube
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 shares a Hyperloom run spanning 14,000 models and explains why clear goals and feedback matter when agents are doing the tuning.
They also explore what comes next for engineers: keeping skills and frameworks reliable, managing the security and accountability of autonomous agents, and applying AI to the last mile of useful software.
We cover:
How agents help install ROCm and serve models through natural language
How Hyperloom profiles workloads and uses LLMs to explore optimizations
The role of GEAK in tuning kernels while preserving numerical accuracy
Anush’s account of optimizing 14,000 models in one pass
How software improvements get more performance from existing GPUs
Keeping agent skills current and testing across AI frameworks
Security, accountability, and the next bottlenecks in agent-driven development
Chapters:
(0:25) A decade of ROCm, now agent native
(3:03) What agentic ROCm looks like in practice
(6:17) Installing ROCm then versus now
(9:14) An order of magnitude more CI across every framework
(10:44) Anush’s workflow: agents and deployment
(12:21) Speed is the moat
(15:00) Success is a stranger who cannot spell ROCm serving an LLM
(17:09) Keeping agent skills from going stale
(21:38) Co-designing kernels with the frontier labs
(24:06) Hyperloom, GEAK, and 14,000 models in one pass
(26:45) Managing autonomous agents: control and liability
(32:21) Security at the speed of agent swarms
(36:03) ROCm performance gains on the same hardware
(37:36) Where enterprises hit walls in production
(40:24) Why coding was the right reward function for AI
(44:42) Which industries get the next software scale unlock
(47:02) The last mile of AI
(50:31) Closing thoughts
Connect with Anush Elangovan:
LinkedIn: https://www.linkedin.com/in/anushelangovan/
Twitter/X: https://x.com/AnushElangovan
ROCm.AI: https://rocm.ai
AMD AI blog: https://www.amd.com/en/blogs/by-author/anush-elangovan.html
AMD AI Developer Program: https://www.amd.com/en/developer/ai-dev-program.html
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

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.
Slack's bet is that AI at work is multiplayer. Agents belong in the channels where teams already work, not in a private chat with one person. Jaime explains why Anthropic pushes so much of its code through Slack, how the channel permission model became the agent context model, and what has to change in engineering culture when the branch is public and the whole team is steering the same agent.
The bigger question is whether Slack becomes the context harness where enterprise agents actually run.
In this conversation:
What happens mechanically when an agent creates a code channel, from authentication to diffs as artifacts
Why engineering at Slack now looks like delegating discrete tasks to agents instead of copy-pasting from a chat
How Slack's channel permission model doubles as the context and access model for agents
Why Anthropic ships code through Slack: the conversation is where the issue emerges
How culture decides whether a multiplayer coding session converges or splits
Why solo-terminal coding with an "army of Claudes" reinforces bias, and what social spaces fix
Slack as an accidental knowledge management system that ranks recency and engagement over correctness
(0:00) Slack as an IDE and a GitHub for your team
(0:29) Who is Jaime DeLanghe
(1:21) The reaction to the Slack Code launch
(5:30) Why coding agents belong in a context-rich environment
(6:08) Engineers now manage agents, not copy-paste code
(7:24) The permission model: agents get the channel's context
(11:44) What happens when a code channel is created
(15:00) Why Anthropic pushes so much code through Slack
(19:14) Steering one agent with many people: culture decides
(24:54) Slackbot, skills, and MCPs: agents go where the work is
(30:53) The solo terminal vs. agents in social spaces
(33:53) Org charts and ownership when agents join the team
(39:33) Learning loops and shared agent memory
(42:39) Citations, recency, and accidental knowledge management
(46:50) Context bloat and multi-pass search for agents
(50:01) How Jaime uses Slackbot as CPO
(52:38) Slack Code is V1 of multiplayer AI
Connect with Jaime DeLanghe:
LinkedIn: https://www.linkedin.com/in/jaime-delanghe-aba59b1a/
Slack Code announcement: https://slack.com/blog/news/slack-code-channels-for-agents
Introducing Slack Code (Salesforce): https://www.salesforce.com/introducing-slack-code/
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 Walrus Memory, 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 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.