Chain of Thought | AI Agents, Infrastructure & Engineering

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.

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!
Full transcript and chapters: https://chainofthought.show/podcast/79-for-ai-agent-fleets-the-model-is-rarely-the-bottleneck-theory-ventures-andy-triedman/
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.