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Chain of Thought | AI Agents, Infrastructure & Engineering

Chain of Thought | AI Agents, Infrastructure & Engineering

De : Conor Bronsdon
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 AI is reshaping infrastructure, strategy, and entire industries. Chain of Thought is the podcast where builders reason through what's changing. Host Conor Bronsdon sits down with the engineers and founders shipping AI in production to get past the hype into what's working and what isn't. Episodes cover model infrastructure, inference, agent frameworks, evaluation, and developer tools. Guests have come from NVIDIA, Google DeepMind, AMD, Databricks, Vercel, and more. Every episode carries a full transcript and show notes at chainofthought.show. New episodes weekly. Conor Bronsdon is an independent consultant and angel investor in AI infrastructure and developer tools. He led technical ecosystem at Modular, acquired by Qualcomm in 2026; led developer awareness at Galileo, acquired by Cisco; and ran developer marketing at LinearB, where he was GM of the Dev Interrupted podcast and community. Views expressed by the host and guests are their own.Conor Bronsdon Economie
Épisodes
  • Asking Your Team to Automate Their Jobs With AI | Cloudera's Shayde Christian

    Asking Your Team to Automate Their Jobs With AI | Cloudera's Shayde Christian
    Nov 8 2026
    Shayde Christian's team at Cloudera has built AI agents that can open a sales opportunity in the CRM and send a customer a 30% discount on training. He still isn't authorized to switch them on in production. Cloudera runs as conservatively as the regulated customers it serves, and the business owners want more proof of reliability and accuracy first. The AI tool his team does lean on every day is, in his words, barely AI.Shayde Christian is Chief Data and Analytics Officer at Cloudera. He walks Conor through how support-case data became a signal for which customers need training, how Cloudera uses a public model to vectorize questions and private agents to execute or generate SQL, and why he developed new career paths before asking his data team to automate their own jobs.We cover:- How three non-break-fix support cases a quarter on a product like NiFi flag a customer who needs training, and Shayde's Bellagio fountain idea of dashboards that disappear once a decision is automated- Getting customer success managers up to speed on a new account in an hour or two instead of a week, and covering eight or nine accounts instead of two to four- How the query tool uses a public model to vectorize the question and agents to run existing SQL and Python; when no match is found, the agent can generate SQL, with a disclosure and feedback check- 200 power users for $2,000 a month, and why Shayde's advice is to ask whether you need AI, not just where to run it- Why data governance is still hard, and the table renaming, synonym columns and metadata work it took for AI to understand business meaning- Cloudera's new Mistral partnership: why Shayde says "Mistral reigns" in Europe and the Middle East, the sovereign AI stack with NVIDIA and Dell, and a proof of concept for agents that act across a company's SaaS systems- Three career paths for a data team whose work is being automated: AI engineer, "parachutist" and "insight assassin"- The human skills that matter more as AI takes routine work, and his advice to new graduates: automate your last jobConnect with Shayde Christian:LinkedIn: https://www.linkedin.com/in/shaydechristianCloudera: https://www.cloudera.comLinks from the episode:Cloudera and Mistral partnership announcement: https://mistral.ai/news/mistral-x-cloudera/MotionBench: https://motionbench.shConnect with Chain of Thought host Conor Bronsdon:Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.showChapters:(0:00) Why automating data work makes the team more valuable(1:05) Actionable signals buried in support cases(5:03) Data privacy culture as AI blocker(6:04) A shared customer view instead of more dashboards(13:08) Three levels of AI business value(15:47) Keeping data private with frontier models(23:55) AI coding tools and data masking(29:05) Evaluating accuracy before agentic actions(32:46) Why Mistral leads in Europe(37:10) Asking the team to automate their jobs(44:24) Change management starts with mindset(48:26) Career advice for new graduatesThanks 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/cotThanks to CoralBricks for sponsoring this episode. CoralBricks serves open models through one API, fast and at low cost: DeepSeek V4.1 Flash streams at over 300 tokens a second. Get $5 in credits on signup, plus another $5 on your first purchase: https://www.coralbricks.ai/r/Z6V2REThanks 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-podThanks to WisdomAI for sponsoring this episode. In WisdomAI's latest benchmark, Claude Code alone topped out at 73% accuracy; WisdomAI closed the gap, delivering accurate answers 3x faster at about a third of the cost. Get "The Blueprint for Trusted AI Analytics": https://wisdom.ai/benchmark
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    52 min
  • Agents Are Overtaking Humans in Search: Why Search Must Be Rebuilt

    Agents Are Overtaking Humans in Search: Why Search Must Be Rebuilt
    Nov 1 2026
    Saahil Jain joined You.com as a founding engineer in 2020, when it was a consumer search engine competing with Google. Now its CTO, he builds search for AI agents, and he argues that however smart models get, they will still need tools to find out what is happening in the world. His analogy: we built the self-driving cars before the roads. He walks through which work he expects to move inside the model, why better models made harnesses matter less, and why cheaper open models and routing are giving harnesses a new job. He says he changed his mind on that last point in the past few months. Then we get into how agents search differently than people do: thousands of queries at once, recall over ranking, and a much wider range of latency needs. Saahil traces a query from fan-out to the index to re-ranking, and explains why You.com judges search by whether the agent answered the question, and why he trusts customer evals and benchmarks that change over time more than static public ones. We cover: - Core intelligence versus augmented intelligence, and the self-driving cars that arrived before the roads- Which tasks Saahil expects models to absorb, and where unverifiable rewards slow them down- Why frontier labs keep going vertical while open-model labs give weights away- How stronger models shrank the gap between good and bad harnesses, and how routing across open models can revive them- How You.com post-trains models with its search tool while trying not to overfit to any one model- How agent search differs from human search: query volume, context, ranking and latency- The life of a query: subqueries, lexical and semantic indexes, re-ranking and enrichment- Why static benchmarks can be memorized, and what dynamic benchmarks and customer evals measure instead Mentioned in this episode:- I Paid an AI Agent $8 to Write About its ‘Life’: https://newsletter.chainofthought.show/p/i-paid-an-ai-agent-8-to-write-about- MotionBench: https://motionbench.sh Connect with Saahil Jain: - LinkedIn: https://www.linkedin.com/in/saahiljain/- Twitter/X: https://x.com/saahil9jain- You.com: https://you.com- You.com API docs: https://you.com/docs/quickstart Connect with Chain of Thought host Conor Bronsdon:Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdon More episodes: https://chainofthought.show Chapters:(0:00) Agents run a thousand searches at once(0:25) What survives as AI models get smarter(1:47) Is this the singularity?(4:26) Core intelligence versus augmented intelligence(6:33) Self-driving cars before the roads(7:27) What belongs inside the model(11:00) Where verifiable rewards run out(12:16) Can agents form relationships?(16:58) What model labs own, and what independents can build(20:21) What a harness is(22:39) The model is eating the harness(23:14) Open models and routing revive the harness(25:32) Open versus closed: what the labs keep(31:30) OpenAI, Baseten and the enterprise model budget(33:53) Co-evolving a search tool with the model labs(37:41) How agent search differs from human search(42:42) Life of a query, from fan-out to index(47:22) Measuring retrieval by whether the agent answered(50:21) Static benchmarks, freshness and customer evals(54:19) Why he changed his mind on harnesses 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 CoralBricks for sponsoring this episode. CoralBricks serves open models through one API, fast and at low cost: DeepSeek V4.1 Flash streams at over 300 tokens a second. Get $5 in credits on signup, plus another $5 on your first purchase: https://www.coralbricks.ai/r/Z6V2RE 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 WisdomAI for sponsoring this episode. In WisdomAI's latest benchmark, Claude Code alone topped out at 73% accuracy; WisdomAI closed the gap, delivering accurate answers 3x faster at about a third of the cost. Get "The Blueprint for Trusted AI Analytics": https://wisdom.ai/benchmark
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    57 min
  • How Cisco Secures Personal AI Assistants for 90,000 Employees | DJ Sampath

    How Cisco Secures Personal AI Assistants for 90,000 Employees | DJ Sampath
    Oct 29 2026
    Cisco is giving each of its 90,000 employees a personal AI agent. Some of those same employees are already connecting agents from outside the security boundary without asking IT. DJ Sampath, SVP and GM of AI Software and Platform at Cisco, walks through what had to be in place before the rollout: scanning every prompt, sandboxing each agent, keeping agents multi-tenant, scanning the skills and tools people bring in, and settling which identities an agent may act as.New episodes, the ideas behind them and Conor's essays land in the Chain of Thought newsletter first. Subscribe: https://newsletter.chainofthought.show/We recorded at Cisco's San Francisco office, a block or two from OpenAI, where Cisco was an early Codex design partner. DJ explains how engineers rework their skills files with each model generation, how per-engineer PR bots review agent-written code, and why defenders now have to move at agent speed when an exploit can show up within minutes of a CVE going public.We cover:What Cisco's IT team required before rolling out MyAgent to 90,000+ employeesPrompt injection by email, and why the first integration everyone makes is the riskiestCisco's AI security taxonomy, DefenseClaw, and Project Quarry, a new open source language for describing agent behavior and policyHow the agents are actually used: daily briefs, meeting actions, live dashboards, 2 trillion tokens in a single weekWhy skills files need refactoring every model generationDiscovering and governing shadow agents with zero trust accessMulti-model code review, spec drift, and evals built from pull request tracesHow Cisco Cloud Control was built in about eight months with agentic codingChapters:(0:00) Guardrails tested inside Fortune 100 companies(0:21) Securing 90,000 employee AI agents(8:04) Cisco's open source agent security stack(12:01) Project Quarry: a policy language for agents(17:07) Personal agent use cases at Cisco(19:08) Early design partner on Codex(23:15) Reassessing workflows each model generation(27:27) Shadow agents and zero trust access(30:29) Security as an adoption accelerator(31:39) Multi-model code review for dependability(36:09) Defenders moving at agent speed(41:04) How Cisco Cloud Control was builtConnect with DJ Sampath:X: https://x.com/djsampathLinkedIn: https://www.linkedin.com/in/djsampath/DJ on Cisco Blogs: https://blogs.cisco.com/author/djsamProject Quarry: https://blogs.cisco.com/ai/project-quarry-agent-security-detectionsCisco: https://www.cisco.com/Related episode: Cisco President and CPO Jeetu Patel, "Parenting Your AI Agents for Best Results": https://share.transistor.fm/s/43e97aa6Connect with Chain of Thought host Conor Bronsdon:Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.showThanks 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-podThanks 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/cotThanks to WisdomAI for sponsoring this episode. In WisdomAI's latest benchmark, Claude Code alone topped out at 73% accuracy; WisdomAI closed the gap, delivering accurate answers 3x faster at about a third of the cost. Get "The Blueprint for Trusted AI Analytics": https://wisdom.ai/benchmarkThanks 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.
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    44 min
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