Chain of Thought | AI Agents, Infrastructure & Engineering

DataCamp tested an early version of its AI tutor against its classic courses, and the tutor won: willingness to pay, engagement and satisfaction were all higher. It was also easily ten to twenty times more expensive per hour of learning than DataCamp's previous setup. Financially, Jonathan says, shipping the better product would have made the business worse.
He expects that cost to fall until it looks like the cost of serving DataCamp's interactive courses. The only question, he says, is whether that takes four to eight weeks or another year.
He's building the tutor on that bet.
Jonathan Cornelissen is co-founder and CEO of DataCamp and is joined by Yusuf Saber, DataCamp's Chief AI Officer and the founder of Optima, the learning company DataCamp acquired. They walk Conor through how the tutor works: teaching behaviors it has to show on cue, evals that test each one repeatedly, a mix of models picked for speed and cost, and a latency budget measured in milliseconds.
We cover:
- How DataCamp turns what great human teachers do into about 30 core behaviors, and how the tutor gives feedback: one main issue at a time, hints phrased as questions, and asking before it reveals a solution
- The A/B test against DataCamp Classic, and why Jonathan calls today's tutor the worst one DataCamp will ever have
- The models behind the tutor: Claude Sonnet handles about 70% of the work, Gemini Flash used to handle much of the fast work, and a cheaper tier is starting to take on more
- Why open models keep failing on speed, hosting and caching, even after one outperformed DataCamp's best model at the time
- Going from almost nothing to a run rate of over a million hours of learning a year, and why the same three-hour course can take one learner half an hour and another six hours
- Pre-generating responses while the learner thinks, and why a stronger model lets them aim for cache hit rates in the mid-90s
- What Bloom's tutoring research says, why doing better on a test isn't proof of learning, and what happens to junior roles when AI takes the grunt work
Connect with Jonathan Cornelissen:
LinkedIn: https://www.linkedin.com/in/jonathan-cornelissen/
Twitter/X: https://x.com/CornelissenJo
DataCamp: https://www.datacamp.com
DataCamp on X: https://x.com/datacamp
Connect with Yusuf Saber:
LinkedIn: https://www.linkedin.com/in/yusuf-saber-ba380b15/
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

Chapters:
(0:00) Better product, worse economics
(0:59) How the tutor helps a struggling learner
(7:09) From static courses to AI tutoring
(9:25) Multi-agent architecture behind the tutor
(13:10) Scaling costs and testing open models
(18:30) Economics of cost per learning hour
(22:13) What great looks like for DataCamp
(25:33) Latency budgets and precomputed lesson plans
(32:53) Measuring learning outcomes with Bloom's research
(39:20) AI skills gap for early-career workers
(43:22) Evals built on tutor behavior expectations
(50:12) Closing thoughts on how skills are changing
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 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 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

Tony Holdstock-Brown is the CEO and co-founder of Inngest, which built durable workflows for background jobs before the agent wave arrived. He says most Inngest customers no longer log into the UI. Their agents check on what they have built through the API, the CLI and MCP, and people who build with agents activate at about 93 percent, against roughly 40 percent before.
Holdstock-Brown defines a long-running agent as one that lives for months and builds up its own context. Running one means orchestrating sub-agents, sandboxes and state for every customer, and he argues that orchestration layer becomes the product. He and Conor Bronsdon also get into why agent load compounds, how to score an agent from production events, and what Inngest learned from running out of compute.

If your users never open your product themselves, how it behaves for their agents is the whole experience.

We cover:
Why durable workflows, a series of steps with checkpoints, mapped cleanly onto agents
Holdstock-Brown's definition of a long-running agent: it lives for months and builds its own context
Inngest's shift from UI traffic to API, CLI and MCP, and the 93 percent activation rate among people building with agents
How agent load compounds when every user's agent makes thousands of API requests a day
Tracking events and outcomes for every agent run, a lesson Holdstock-Brown ties to KISSmetrics and A/B testing
Explicit step APIs compared with compiler directives, and why a step is close to tracing
Running out of compute: bare metal, bandwidth limits and building on FoundationDB
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) Why agents need durable infrastructure
(1:13) How Inngest started before agents
(3:53) Defining a long-running agent
(6:08) Orchestration becomes the product
(8:40) Customers' agents replace the UI
(10:40) Reliability when agent load compounds
(16:40) State, memory and personal agents
(20:50) Personal agents for mainstream users
(28:07) Step APIs vs compiler directives
(31:27) Code review agents and security
(40:38) Running out of compute
(46:55) Building your first long-running agent

Links from the episode:
How the Claude Code team does code review (Anthropic): https://claude.dev/blog/solving-code-review-at-anthropic/
Claude Duet announcement: https://claude.com/resources/articles/claude-duet
Clean Code by Robert C. Martin: https://www.informit.com/store/clean-code-a-handbook-of-agile-software-craftsmanship-9780132350884
Software Engineering is Dead. Long Live Product Engineering (Conor's essay): https://newsletter.chainofthought.show/p/software-engineering-is-dead-long
OpenClaw: https://openclaw.ai
Chain of Thought with Claire Vo: How AI Velocity is Rewriting the Rules for Engineering Leaders: https://share.transistor.fm/s/5178eb0a
Chain of Thought with Jaime DeLanghe: Slack Wants to Be the Context Harness for Code: https://share.transistor.fm/s/274962be
Chain of Thought with Laurie Voss: AI Codes: Product Engineers Decide What to Build: https://share.transistor.fm/s/6deea963
Chain of Thought with Wen Sang: Genspark's Bet: AI Agents Become the Users of Software: https://share.transistor.fm/s/c898a0e8
Connect with Tony Holdstock-Brown:
X: https://x.com/itstonyhb
Inngest: https://inngest.link/cot-pod
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 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

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.
Full transcript and chapters: https://chainofthought.show/podcast/82-asking-your-team-to-automate-their-jobs-with-ai-clouderas-shayde-christian/
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 job
Connect with Shayde Christian:
LinkedIn: https://www.linkedin.com/in/shaydechristian
Cloudera: https://www.cloudera.com
Links from the episode:
Cloudera and Mistral partnership announcement: https://mistral.ai/news/mistral-x-cloudera/
MotionBench: https://motionbench.sh
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
Chapters:
(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 graduates
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

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/ConorBronsdon
LinkedIn: 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

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+ employees
Prompt injection by email, and why the first integration everyone makes is the riskiest
Cisco's AI security taxonomy, DefenseClaw, and Project Quarry, a new open source language for describing agent behavior and policy
How the agents are actually used: daily briefs, meeting actions, live dashboards, 2 trillion tokens in a single week
Why skills files need refactoring every model generation
Discovering and governing shadow agents with zero trust access
Multi-model code review, spec drift, and evals built from pull request traces
How Cisco Cloud Control was built in about eight months with agentic coding
Chapters:
(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 built
Connect with DJ Sampath:
X: https://x.com/djsampath
LinkedIn: https://www.linkedin.com/in/djsampath/
DJ on Cisco Blogs: https://blogs.cisco.com/author/djsam
Project Quarry: https://blogs.cisco.com/ai/project-quarry-agent-security-detections
Cisco: https://www.cisco.com/
Related episode: Cisco President and CPO Jeetu Patel, "Parenting Your AI Agents for Best Results": https://share.transistor.fm/s/43e97aa6
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 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 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 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
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.