Agentic Conversations (formally mlops.community)

Two people, a wrong turn into a back alley, a community garden, and about thirty minutes of arguing about protocols on the streets of Tokyo.
The guest is Angie Jones, VP of Developer Experience at the Agentic AI Foundation, fresh off launching AGNTCon + MCPCon in China before the Tokyo stop. She opens with what she learned there: a mobile-first, super-app world where the integration problem most of us obsess over barely exists, where every conversation about agents is really a conversation about the model, and where companies are now reaching for MCP and A2A precisely because they want to operate outside that ecosystem.The bulk of it is WebMCP - a protocol with a confusing name and, until recently, almost no attention. The pitch: put tool calling in the page itself, so your agent works inside your logged-in session with only the tools relevant to the page you're on, instead of screenshotting an anonymous browser and burning tokens guessing at the accessibility tree. Angie explains why it went from ignored to urgent the moment agentic browsing got good, and why the fix for computer use being slow and hijacking your machine might be a standard rather than a better model.
It closes on agent-to-agent: whether anyone actually wants a marketplace of thousands of agents, or whether the real value is the one agent that has access you'll never get. Plus a well-earned complaint about three-letter acronyms and why researchers are still the only people naming things well.
Timestamps:[0:00] Intro[0:59] Launching the conference in China[1:34] What North America gets wrong about agents[2:23] Super apps versus endless integrations[3:14] What happens when they expand beyond China[3:39] Tencent and A2A in production[4:34] A model-first country[5:56] Chinese coding agents and harnesses[6:59] Tokyo and the conference world tour[7:26] What WebMCP actually is[8:56] Why it has nothing to do with MCP[9:20] Page-level tools and your logged-in session[10:36] Why WebMCP sat unnoticed for months[11:25] Token efficiency and reliability[12:23] The moment computer use got good[13:15] Two real grievances with computer use[14:06] Collaborating instead of surrendering your screen[15:24] A short detour into Tokyo signage[16:15] Why web developers should be excited[17:04] Agents and the loss of first-party data[18:27] Why an agent cannot just buy something[19:23] Inside the agentic commerce working group[20:18] Upsells recommenders and an agent that ignores them[23:27] The commerce protocols to watch[24:19] Why A2A is next[25:31] Publishing your agent as a service[27:13] The case against agent marketplaces[27:55] Why access beats capability[29:31] Google's protocol land grab[30:45] Bring back the cool names[31:39] Amsterdam, San Jose, and what comes next

Tool descriptions tell an agent what a tool does. They don't tell it how to use five tools together, in the right order, following your conventions. That gap is where this conversation lives.
Filmed at AGNTCon + MCPCon in Tokyo with Ola Hungerford, Principal Engineer for AI Enablement at Nordstrom and a maintainer of the Model Context Protocol, who spent the last several months turning a pattern everyone was quietly reinventing into an actual MCP extension.
Ola walks through what skills over MCP really means: the server stops being a pile of tools and becomes a distribution channel, handing the agent the instructions, workflows and knowledge it needs only at the moment it needs them. She explains why server instructions weren't enough, how progressive discovery keeps context from exploding, and why the same mechanism works for memory and preferences even when no tools are involved.Then it gets into the harder parts. What belongs in the MCP spec versus the agent skills spec. Why passing custom front matter through opens a rug pull and prompt injection surface nobody wanted. Where skills start to look like sub-agents, and why there's still no standard way to declare which servers a skill depends on. And the honest problem underneath all of it: how do you standardize something while everyone is still finding out what it's actually for, without breaking a hundred things the next time you change your mind?
Timestamps:[0:00] Intro[0:21] AI enablement at Nordstrom[0:33] What skills over MCP actually is[1:29] The MCP server as a distribution channel[2:01] Server instructions versus skills[3:11] Distributing knowledge and memory[4:23] Progressive discovery explained[5:23] Where the idea came from[6:59] From draft to official extension[8:19] What early adopters changed[8:51] Front matter and custom metadata[9:57] Rug pulls and prompt injection risk[10:57] Will any of this get standardized[12:10] Marrying two very different specs[13:03] Skills as personas and sub-agents[13:53] The missing dependency standard[15:15] How the extension actually works[16:19] What harnesses still need to support[16:59] Consent and skill integrity[17:56] Where skills over MCP goes next[18:43] Why cramming 200 tools fails[19:41] Standardizing before you know the answer[22:18] Is git the wrong tool for agents[23:19] Picking tools for the actual persona[23:51] Trying to be less productive[25:48] The anxiety of idle agents[27:15] Why she keeps a robot on her desk[28:22] If the agent feels the friction, does it matter[29:41] Efficiency, waste, and caring enough[30:52] Letting an agent debug for you[32:03] Choosing your rabbit hole

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