Agentic Conversations (formally mlops.community)

A year ago, Palo Alto Networks built dashboards to track AI spend. Today those dashboards are useless, and the team that built them thinks that's the whole story.
Recorded at FinOps X in San Diego, this conversation brings together Abhinav Lad, who leads cloud and AI finance at Palo Alto Networks, and Kuntal Patel, who runs the cloud engineering function behind it. They explain what happened when agents entered the picture, and AI stopped behaving like a service anyone could forecast.
The short version: consumption went from linear to exponential almost overnight. Agents are goal-oriented rather than task-oriented, so they plan, call tools, verify, fail, retry, and keep looping until they hit the outcome, and every iteration is billable.
So how do you run finance on top of that? Abhinav and Kuntal walk through the metrics that replaced their old forecasts: adoption rate, cost per user, AI as a percentage of revenue - and the budget limits that let engineering leaders choose between the newest model and a longer runway. They get into the open question of whether a cheaper model saves money or just burns more tokens thinking. They explain why an AI gateway became the control plane for cost and security at the same time, why retry caps belong in the design phase instead of the postmortem, and how FinOps starts to resemble product QA once the bill becomes the clearest signal that something is broken.
They close on a warning worth sitting with: cost per million tokens is a number that means almost nothing on its own, and a value story built on it will point you somewhere you don't want to go.

Palo Alto Networks: https://www.paloaltonetworks.com

Abhinav Lad: https://www.linkedin.com/in/abhinav-lad
Kuntal Patel: https://www.linkedin.com/in/kuntalpatel35
Alex Salkever: https://www.linkedin.com/in/alexsalkever

Timestamps:
[0:00] Intro
[1:00] Who runs FinOps for AI at Palo Alto Networks
[2:10] Last year's AI dashboards are already useless
[4:26] Agents turned linear forecasts exponential
[7:27] Three traits that make agents expensive
[8:34] The hidden bill: RAG, vectors and egress
[9:16] Cost per user and adoption rate
[11:21] Giving engineering leaders a budget
[12:07] Using DORA metrics to prove value
[13:53] Where DORA stops fitting AI
[16:20] Does the cheaper model actually save money
[17:57] Why you need an AI gateway
[20:05] Inside Prisma AIRS
[21:00] Three cost models for three use cases
[22:52] Forecasting lessons from Electronic Arts
[24:03] Runaway agents and endless loops
[25:59] Capping retries before they burn cash
[28:06] Writing cost policy at design time
[29:01] When FinOps becomes product QA
[32:17] Explaining AI spend to the C-suite
[34:51] Valuing AI beyond engineering
[37:04] Crawl, walk, run: where they are today
[38:20] Why cost per million tokens is meaningless
[39:26] Closing thoughts

In this episode of Agentic Conversations, we sit down with Ambud Sharma, Principal Engineer at Pinterest, responsible for general technology efficiency, fresh off delivering a controversial keynote on AI infrastructure optimization at scale.

Ambud walks us through his Five Layer Cake framework - a structured approach to driving efficiency across every level of the AI stack, from silicon and hardware procurement to model selection, inference engine design, and governance. We explore how decisions compound across layers to unlock real business growth, and how the wrong choices can lock you into expensive commitments for years.

We stress test the framework against two very different business models: what the stack looks like if you are building the next Cursor, and how it changes entirely if you are building the next YouTube. Along the way we cover hardware immutability, inference engine warm-up costs, GPU occupancy, context switching, quantization trade-offs, model routing, and why experimentation discipline is the only thing that keeps AI infrastructure costs from getting out of control.

We also look at how this framework holds up in the emerging agent era, what changes when agent-to-agent communication becomes the norm, and why agent traffic just passed bot traffic on Cloudflare. The conversation closes on a deceptively simple takeaway: there is no silver bullet, and experimentation at every layer always comes first.

Pinterest: https://about.pinterest.com/

Alex Salkever: https://www.linkedin.com/in/alexsalkever
Ambud Sharma: https://www.linkedin.com/in/ambud

Timestamps:
[0:00] Introduction and the controversial keynote
[2:09] The five-layer cake explained
[4:30] Why hardware decisions are irreversible
[6:47] Two business models: building Cursor vs YouTube
[10:42] Applying the five layers to a YouTube-style company
[14:23] Experimentation as the core efficiency method
[17:09] Inference stack: context switching and warm-up costs
[20:07] Model layer: why changing models breaks everything
[24:10] When you should not use an LLM at all
[26:41] Governance and routing: right model for the right task
[29:20] Horror stories of unchecked token spend
[31:37] Experimentation discipline without stifling innovation
[34:35] How the five layers change in the agent era
[36:05] Agent-to-agent communication and governance complexity
[38:27] Core takeaway: experimentation first at every layer

AI models are starting to act like appliances, locked into one narrow way of working, instead of the flexible infrastructure they used to be. Drew Breunig, an AI and data strategist working with the Overture Maps Foundation, joins us to explain why, and what it means for anyone building something that doesn't look like Claude Code.

Drew walks through his "Winchester Mystery House" idea: what happens once code gets so cheap to write that the only real bottleneck left is feedback. From there we dig into DSPy: signatures, the GEPA optimizer, and the brand-new Flex optimizer, which rewrites your code instead of just your prompt, complete with a real before-and-after on cost and accuracy. We also get into why so many AI-built apps and websites end up looking identical, the actual difference between an agent and a workflow, what Drew learned a year after shipping a code library with no code in it, and why he thinks the most valuable thing you can do right now is close the laptop and go talk to people.

CMPND: https://www.cmpnd.ai

Drew Breunig: https://www.linkedin.com/in/drewbreunig/
Demetrios: https://www.linkedin.com/in/dpbrinkm

Timestamps:
[0:00] Cold open: when Claude Code tries to call itself
[1:19] Biggest AI news: labs trading diversity for reliability
[2:35] How harnesses get trained into models over time
[5:41] The problem: your harness starts fighting the model
[9:13] When do you need your own harness?
[10:02] The Winchester Mystery House warning
[16:13] The blank page problem: why everything looks the same
[20:50] Infrastructure vs appliances: the thesis lands
[22:40] Current tool loadout: GLM, Kimi, Claude Code, Pi
[27:04] The Raspberry Pi personal agent running on Slack
[31:00] Crystallizing tasks: when to replace AI with pure code
[33:10] DSPy explained: separating what from how
[35:23] How prompt optimizers actually work
[39:31] DSPy pre-dates ChatGPT: model-agnostic programs
[44:00] Why you still need to ship the code, not just the spec
[50:00] Don't plan more than a month ahead anymore
[54:00] Coaching agents all day feels productive — it isn't
[57:58] The dopamine of building with agents vs. why you still need human feedback

Every engineer at Wayfair can now see, in real time, exactly what their code costs, and that's on purpose. Brent Eubanks, FinOps Architect at Wayfair, walks us through what happens when you stop treating AI spend as a finance problem and start treating it as an engineering one.The story that sticks with you: a team was burning $400k a month on an LLM-driven workflow, until they flipped the whole thing on its head - hard-coded logic doing the heavy lifting, AI called in only when it's actually needed. New spend: $80k. It's the kind of move that only makes sense once you realize more agentic autonomy isn't always the win it's sold as.From there we get into the machinery Wayfair has built to make cost ownership feel less like a punishment and more like a superpower: guardrails baked straight into coding copilots, predictive alerts that catch a team blowing its budget before the month even ends, a "spend captain" system that pushes budget decisions down to the people closest to the work, and a genuinely strange new question every tech leader is starting to ask: if you were staffing a company with a team of AI agents instead of people, what would that org chart even look like?Wayfair: https://www.wayfair.comBrent Eubanks: https://www.linkedin.com/in/brenteubanksDemetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:[00:00] Intro & teaser [01:14] Meet Brent (FinOps Architect, Wayfair) [02:34] Making AI adoption easy for developers [03:30] Three-area cost governance framework [04:45] Automated FinOps guardrails in pull requests [07:29] Predictive analytics: 400% over budget warnings [08:26] The 400% spender: asset or liability? [10:06] The "spend captain" model [12:13] The goal: 1000x more token use in two years [13:05] The thought experiment [15:08] The $400k workflow rebuilt for $80k [16:42] Hardcoded first, LLM as fallback [19:49] Engineers now own their full cost stack [20:13] The FinOps companion that works overnight [24:10] Guardrails injected before the pull request [26:17] Recurring tasks become automated skills [27:46] Sankey diagram: cost per agent step in real time [28:40] "The bill didn't blow up - that doesn't happen by accident"

OpenAI's Codex developer experience lead sits down with a former comedian turned ML engineering lead for a conversation about what happens when computer use agents stop being a novelty and start actually running your day.

The conversation moves through building an AI-powered morning brief that reads every email, Slack message, and tweet before you've even opened your laptop, letting pinned threads check in on themselves every 30 minutes, and a skills system built to mirror how a person actually writes and reviews code. There's a close look at the guardrails and permission layers that keep an autonomous agent from pushing to the wrong repo or replying to the wrong tweet, how a codebase merging thousands of pull requests a day survives thanks to self-healing review before anything hits CI, and the idea of AI deference - when an agent should push through a task alone versus stop and ask for help.

The back half gets personal: why developing taste and vocabulary now matters more than working harder, what it actually takes to delegate to an agent the way you'd onboard a new employee, and why this might be the year voice-orchestrated computer use finally makes everyone feel like they're talking to Jarvis.

OpenAI: https://openai.com
Monaco: https://www.monaco.com

Jason Liu: https://www.linkedin.com/in/jxnlco
Mihail Eric: https://www.linkedin.com/in/mihaileric
Demetrios: https://www.linkedin.com/in/dpbrinkm

Timestamps:
[00:00] Intro and guest backgrounds
[01:36] Why computer use beats plain API calls
[09:11] Building an AI-powered morning brief
[10:17] Self-monitoring threads that check in on their own
[18:26] How OpenAI reviews thousands of PRs a day
[19:42] Self-healing pull requests before CI even runs
[23:07] Building review skills from teammates' habits
[30:52] Why hard work stops being the differentiator
[35:03] Introducing the idea of AI deference
[42:44] Learning to delegate like hiring your first assistant
[46:22] Why voice beats typing for giving agents context
[50:56] The Tony Stark Jarvis analogy for this year