Agentic Conversations (formally mlops.community)

Demetrios
  • 4.6 (25)
  • Technology
  • Updated Weekly

Relaxed conversations and technical deep dives around AI Agents. This Show is brought to you by the Agentic AI Foundation where the leading agentic open-source projects like MCP, Agents.md, and Goose live. See more at aaif.io

  1. 2d ago

    Inside a Game Where AI Agents Never Sleep

    What happens to an MMORPG when half the players never log off? Issac Lee, who runs AI and blockchain experiments at NEXUS, has been testing that idea. His team is filling game worlds with agent players instead of scripted NPCs, so a new title feels alive from day one. We get into why agent-generated content could be the next step after studio-made and user-generated games. We also cover how agent populations might solve the chicken-and-egg problem every multiplayer launch faces, and what it means when your fellow players are relentless and never need sleep. Isaac walks through the NEXUS lab: a text-to-3D world builder for Roblox, an AI pipeline that turns ONE store novels into short web dramas, a loop-based tool for natural pixel animation, and Da Vinci, the internal multi-agent framework the team uses to ship. Then the conversation turns to infrastructure. We talk about why NEXUS went from 200 hand-defined agent roles to a much thinner harness, whether custom harnesses still matter now that Claude and Codex exist, and why the Kimi release pushed the company to start building its own small data center for inference. We close on context: how much of your life you should hand to an AI, what personalized meeting notes could look like, and why shopping agents may force every ecommerce site to rethink who it's designing for. Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkmIssac Lee: https://www.linkedin.com/in/issacley/?locale=ko Timestamps:[0:00] Cold open[0:57] AI in gaming at NEXUS[2:19] Text to 3D and the last 10 percent[4:38] AI companions and coaches[6:35] Agent players in MMORPGs[8:27] From UGC to agent-generated content[11:07] Agents that never sleep[12:58] Taming fuzzy prompts[19:24] Inside Da Vinci[23:10] Undercover QA agents[27:14] Do harnesses still matter[31:52] Self-hosting open models[41:33] Lab experiments and Bob[50:42] How much context is too much[54:22] Marketing and shopping agents

  2. 5d ago

    How a Logistics Giant Keeps AI Data Locked Down

    Picture a startup with ten engineers hammering away on AI and one person lying awake over a $20,000 bill that hasn't arrived yet. That's the scenario we put to Jason Ward, who handles FinOps for AI at C.H. Robinson and recently joined the FinOps Foundation's AI working group. Jason's first answer is not glamorous: tag every AI resource so you know who owns it. The rest of the episode is what that makes possible. He walks through how C.H. Robinson runs AI across order entry, quoting, booking, and tracking, and why the team routes Anthropic models through Vertex AI to keep data locked down. Then come the metrics. Jason uses AI to dig through his own observability platform for signals he didn't know were there. One of them is how chatty a model is. That signal turned a prompt bloat alert into a bug in the code that kept retrying and burning tokens. Its opposite, context starvation, burns tokens too: a model with too little context keeps failing and trying again. We also cover why agentic and conversational workloads need separate baselines. Jason explains why cost per order is the easy win, and why most of the real work doesn't fit into neat discrete tasks. That's where his experimental cost per thought metric comes in, with reasoning ratio and cache hit rate alongside it. His advice is simple: your AI is the best tool you have for understanding your AI. Alex Salkever: https://www.linkedin.com/in/alexsalkever Jason Ward: https://www.linkedin.com/in/jward2 Timestamps:[0:00] Cold open[0:33] Meet Jason Ward from C.H. Robinson[1:18] AI use cases in logistics[1:52] Azure OpenAI and Vertex AI[2:19] Why keeping data in-house matters[2:44] How developers use AI day to day[3:40] Using AI to hack AI observability[4:21] Measuring how chatty a model is[5:45] Advice for a startup afraid of its AI bill[6:33] Step one is tag every AI resource[7:05] The Copilot billing blind spot[7:44] Why spend is only half the story[8:12] Break down cost by app and by model[9:09] The prompt bloat that exposed a bug[10:03] Context starvation[11:28] What goes into the AI spend report[12:21] Discrete tasks are low-hanging fruit[12:58] Agentic vs conversational workloads[14:12] Know the workflow before you report on it[15:02] The CTO's end goal[16:08] Cost per thought[17:59] Reasoning ratio and cache hit rate[19:06] Dynamic model routing[20:04] Use your AI to improve your AI

  3. Oct 1

    The Caveman Prompting Challenge

    Caveman prompting has one rule: why use many words when few do the trick? It saves tokens on the way in and on the way out. Push it too far, though, and the output falls apart. So how far is too far? Nobody has benchmarked it yet, and that question opens our conversation with James Barney, Head of Forward Labs at MetLife. James spends his days connecting new AI capabilities to old business problems across dozens of regulatory regimes, and he still finds time to push code. He explains how the FinOps Foundation's AI working group took on the most basic question: which model for which workload, and why the answer always comes down to cost, speed, and accuracy. We get into Anthropic's launch pricing for Fable, why a million tokens is easy to price and hard to explain, and why every stakeholder eventually tells you what they really care about once you name the wrong North Star. From there it gets practical. Start with the smartest model, then step down and add harness until quality holds. Treat exploration tokens like local builds and production tokens like pipelines. Govern agents the way you govern people, with proactive blocks, reactive checks, and policy as code an agent can actually read. We close on a bigger shift. When a chat window can pull from every dashboard at once, do we still need dashboards? James thinks mostly not, with one catch he calls the latent shopper problem: some insights only come from browsing data you did not know to ask about. Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkm James Barney: https://www.linkedin.com/in/james-barney Timestamps: [00:00] Cold open [01:03] Meet James from MetLife [01:11] What AI enablement means at a global insurer [03:27] Inside the FinOps Foundation AI working group [05:03] The caveman skill challenge [06:37] Why we need a CaveBench [07:43] Anthropic's Fable launch pricing [09:34] Planning for surprise model releases [11:45] Measuring AI value beyond cost [12:47] Finding your unit metric [14:15] Tying token spend to business outcomes [16:35] Explore first, then optimize [18:12] AI that tunes itself [20:13] Do R&D tokens count [23:24] When the experiment becomes the product [25:38] Personal agents and daily briefings [27:39] Governing agents that run just because they can [30:37] The layers of AI governance [31:56] Proactive and reactive guardrails [32:59] Policy as code for agents [34:41] Stop the click ops [35:31] Is the modern UI obsolete [36:36] MCP apps and chat as the new browser [38:07] How AI gathers data differently than humans [40:37] The latent shopper problem [42:12] Staying close to your data

  4. Sep 28

    AWS Has 16,000 APIs. Can MCP Handle It?

    What happens after you’ve built your first MCP server and actually have to make it work in the real world? In this episode, James Ward dives into the more advanced side of MCP: observability, evals, tool design, code mode, authentication, and the challenges that appear once agents start using your server at scale. We also get into how AWS thinks about MCP across roughly 16,000 APIs, why inefficient tool design often gets blamed on MCP itself, and whether the future could involve more constrained, human-reviewable alternatives to full code mode. Along the way, James shares a great example of an AWS documentation change that accidentally triggered prompt injection warnings from agents, showing just how complicated testing across different models and harnesses is becoming. If you’re already building with MCP and want to understand what comes after the “hello world” stage, this one goes deep. Timestamps: [00:00] “This Code Is Gobbledygook” [00:44] What Happens After You Build an MCP Server? [02:11] The MCP Patterns You Actually Need in Production [03:48] Why Your Agent Is Making Too Many Tool Calls [05:29] How to Make MCP Use Fewer Tokens [08:44] Is MCP Actually Inefficient? [10:11] “We Built a Lot of Pretty Crappy MCP Servers” [11:25] The MCP 2.0 Migration Problem [14:02] Why AWS Is Rethinking Code Mode [15:35] The Problem With Letting Agents Write Python [19:31] How Do You Measure Agent Experience? [22:11] Finding Out Why an Agent Failed [23:42] Hundreds of Evals for Four Cents [24:36] The Evaluation Matrix Gets Massive [26:28] AWS Accidentally Triggered a Prompt Injection Warning [30:16] Should MCP Servers Expose Only Five Tools? [31:02] AWS Has 16,000 APIs. Now What? [34:09] One Super-Agent or Thousands of Specialized Agents? [36:55] The MCP Authentication Problem [40:15] What’s Coming at AgentCon

  5. Sep 24

    Skills over MCP on the streets of Tokyo

    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

  6. Sep 18

    Walking Tokyo Talking Agent Protocols

    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

  7. Sep 14

    Why Cost Per Million Tokens Is A Useless KPI?

    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

  8. Sep 4

    The Five-Layer Cake Approach to Scaling AI Without Wasting Money

    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

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Relaxed conversations and technical deep dives around AI Agents. This Show is brought to you by the Agentic AI Foundation where the leading agentic open-source projects like MCP, Agents.md, and Goose live. See more at aaif.io

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