Agentic Conversations (formally mlops.community)

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

In this episode, we're joined by Josh Collier, FinOps Lead at Superhuman (formerly Grammarly), to explore what it really costs to run AI at scale and why the rules of the game changed faster than anyone expected.

We discuss how AI token costs dropped 80% in two years, why that trend has sharply reversed with frontier models doubling in price, and how Josh rebuilt a single LLM workflow that cost $400k a month down to $80k by rethinking the architecture. He also shares how a cost calculator built in 15 minutes transformed the way his team estimates spend before running experiments, and why research-led optimization is the only kind that works without degrading the product.

Along the way, we cover hidden costs most teams miss, the trade-off between Azure reserved capacity and OpenAI Priority Processing, why fixed subscription pricing is broken in an AI-native world, vendor lock-in risk, and what OpenAI's Guaranteed Capacity announcement really signals about where vendor relationships are heading next.

Superhuman: https://superhuman.com

Josh Collier: https://www.linkedin.com/in/josh-collier-945b7029/
Demetrios: https://www.linkedin.com/in/dpbrinkm

Timestamps:
[00:00] OpenAI Guaranteed Capacity: what's really going on
[01:04] Josh's path into AI FinOps
[02:48] Token costs: the 80% price drop
[04:16] Why costs will only go up
[05:06] External LLMs as financial risk
[07:16] Why subscription pricing is dead
[08:22] The data residency fee nobody notices
[09:33] The cost calculator built in 15 minutes
[10:24] How it changed dev team speed
[13:00] Tracking costs by service and team
[15:33] $400k workflow rebuilt for $80k
[17:13] Why only research can optimize tokens
[20:00] Speculative decoding win
[23:11] One bad query, $40k gone
[26:00] Why Azure PTU was exhausting
[28:59] Shadow traffic load testing
[29:07] Priority processing: no brainer
[31:10] Guaranteed capacity: lock-in signal?
[32:18] The danger of multi-year AI deals
[33:28] Vendor-agnostic proxy as exit strategy

David Soria Parra is an Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP). We explore the biggest evolution of the protocol since its launch, and why MCP is becoming the foundation for the next generation of AI agents.

We discuss why MCP is moving toward stateless communication, what developers misunderstand about state, sessions, and transport layers, and how lessons from real-world deployments at massive scale have shaped the protocol's future. We also dive into MCP v2, SDK migrations, protocol design, extension architecture, governance, developer experience, and how Anthropic thinks about balancing simplicity with long-term flexibility.

Along the way, we explore progressive disclosure, tool search, programmatic tool calling, context bloat, forward compatibility, long-running AI tasks, protocol evolution, open-source governance, observability, and why the future of AI infrastructure will depend on designing protocols that can evolve without breaking the ecosystem.

Timestamps:
[00:00] Introduction
[01:59] Why MCP Had to Become Stateless
[04:28] The Tradeoffs of Stateless Design
[06:13] What We Learned About Agent State
[08:04] Sessions, Models & Implicit State
[09:33] Migrating to MCP v2
[12:19] Lessons from HTTP & Open Source Standards
[18:16] Shipping Fast Without Breaking Everything
[20:35] The Future Complexity of MCP
[22:44] Core Features vs Extensions
[26:47] Progressive Disclosure Explained
[28:16] Solving Context Bloat
[30:50] Why Tool Search Beats Progressive Disclosure
[32:10] The Biggest MCP Anti-Pattern
[34:25] Designing for Forward Compatibility
[38:41] Why "Tasks" Matter
[40:53] JSON, Tokens & Better Tool Calling
[44:44] Observability & Tracing AI Agents
[47:34] Will MCP Ever Be Finished?
[50:22] What's Next for MCP

Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.

Huge thanks to PwC for supporting this episode!

💰 The 30% benchmark — What "good" actually looks like: real efficiency gains clients are reporting across engineering, finance, HR, and supply chain
🔄 Workflows, not use cases — Why isolated pilots and POCs never show up in EBITDA, and how end-to-end workflow redesign does
🧪 Champion vs. challenger — Running a control group against your AI-automated process so ROI is demonstrated, not guessed
📞 Why customer care agents are still freaking hard — Context, CDP integration, billing systems, and voice-to-voice latency
💸 Tokenomics & FinOps — Consumption-based cost surprises, model selection, prompt engineering, and enforcing cost-per-workflow budgets
🔍 Auditing agentic behavior — Using AI to test AI, the missing "SOC 2 for agents," and certifying agents for sensitive use cases
👤 Human in the loop as an evolving scale — From reviewing 50% of outputs down to 10% as trust builds
🧠 88% do AI, 33% scale it — Building a culture of innovation, and why AI usage is showing up in performance reviews
💼 Jobs, reskilling & the operating model reset — Why 75%+ of jobs will be reskilled, not replacedIf you're an AI leader, platform engineer, or exec trying to turn AI experiments into P&L impact, this one's for you.

Links & Resources:
Connect with Manish: https://www.linkedin.com/in/manishdasaur/
PwC AI: https://www.pwc.com/us/en/tech-effect/ai-analytics.html
PwC's 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html

Timestamps:
[00:00] AI Hype vs Business Value
[00:44] API Spend Tracker Widget
[02:18] Tokenomics and FinOps for AI
[06:19] Measuring AI Impact Objectively
[11:07] AI in Support Workflows
[18:05] AI Innovation Culture
[27:16] MCP Servers and SOC 2
[29:14] Human in the Loop in evolving scale
[35:44] AI and Workforce Efficiency
[39:59] AI Transformation and Mindset
[42:19] Wrap-up