Cloud-Native to AI-First Engineering: Expert Insights

What does #AI-first engineering look like in the field? A good panel coming up with firms who are leading in the space and #building modern platforms.

What does it actually take to move from cloud-native to AI-first engineering? Tomorrow, we’re bringing together leaders who are already doing it: • Chris Haire (CTO, VSCO) • Jason Davenport (Google) • Saurabh Gupta (CTO, Credit One Bank) • Ravi Bellur (Founder, Precession Partners) In this live panel, they’ll break down how organizations are: → Evolving architectures with Google Kubernetes Engine → Embedding AI into real-world applications with Vertex AI → Scaling data platforms across complex environments → Rethinking developer workflows for an AI-first era If you're thinking about what’s next for your platform strategy, this is a conversation worth joining. Last chance to register: https://lnkd.in/dzRC_kB7

Cloud-native → AI-first. How do you actually get there? Catching this panel tomorrow with CTOs from VSCO & Credit One Bank + leaders from Google breaking down real architecture shifts. Worth your hour 🎯

Anthropic is strengthening its infrastructure leadership, bringing in a senior executive from Microsoft to help keep its systems online and scalable. Eric Boyd, who spent over 16 years at Microsoft, will join Anthropic as head of infrastructure, according to a LinkedIn post on Tuesday. “AI is accelerating at an incredible pace,” Boyd wrote. “The impact of Claude Code in the last 6 months, and particularly the last two months, just shows the power of what is possible.” The move comes as Anthropic grapples with a spike in demand for its AI tools, including Claude Code, a product designed to streamline software writing and debugging. Read more: https://lnkd.in/giCAy23x

Today, we’re excited to announce a transformative evolution in observability: A hyper-scalable, AI-native, cloud-agnostic, fully integrated observability transformation framework designed to unlock end-to-end telemetry synergy across your entire digital value chain. Powered by a quantum-adjacent telemetry intelligence layer, this introduces a zero-friction, outcome-oriented observability enablement paradigm—all built on top of an agentic, self-healing observability fabric. Through our proprietary, multi-dimensional maturity model, organizations can: 💥 Operationalize real-time, insight-driven decisioning 💥 Orchestrate cross-platform telemetry harmonization 💥 Activate KPI-aligned observability outcomes 💥 Accelerate next-generation digital enablement at scale — Ready to cut the BS? We are. Skip the jargon, buzzwords, and endless slide decks. Real engineering. Real results. Datadog done right. No BS. Happy April Fools. #Datadog #Observability #SRE #DevOps #NoBS

Everyone talks MLOps, but few measure its true impact on the bottom line. Are you building a sophisticated pipeline for a model that barely moves the needle? I've seen too many engineering teams invest heavily in MLOps infrastructure only to find their models underperforming or their costs skyrocketing. It's time to question the MLOps dogma. Are you tracking model decay and retraining frequency? What's the dollar value associated with each improvement? Focus on business outcomes, not just the tech. Implement robust monitoring, A/B test everything, and ruthlessly cut underperforming models. Tie MLOps investments directly to key performance indicators

I had three engineering leaders last week ask if they could send tech leads our way to see "how AI-forward SRE teams operate in practice." We said yes. I think we're far from perfect, but our team leverages AI to the hilt to do three things extremely well -- 1) aggressively automate things that need to be done twice, 2) getting the right automation to the right person at the right moment, 3) doing both at a fraction of the time and cost of the original Google model Nothing surprising about (1). However, very few teams think deeply about (2). Even fewer take an analytical approach to (3). Having seen this play out a handful of times now, we also developed opinions on THE ONE METRIC that matters most when a team is still early in the journey. Happy to trade notes any time.

I am excited to share the stage at AWS Summit London with Vu San Ha Huynh talking about what it actually takes to build fiduciary-grade AI agents in production and at scale at Thomson Reuters. Session: [AIM302] Agents in the Enterprise — Best Practices with Amazon Bedrock AgentCore We'll cover architecture, tooling, multi-agent patterns, and security. And the part that takes the most iteration to get right: Evaluation. If you can't measure whether your agent is reliable, you shouldn't deploy it. Full stop. Come join us if you're wrestling with the same challenges 👋

The paradox nobody talks about: you invest more in CI every year, and somehow things get slower: Compute spend up. Pipeline times up. Release frequency flat. Engineers context-switching while a regression suite decides their afternoon. One enterprise DevSecOps customer turned on CloudBees Smart Tests and the math changed: Same code. Same coverage. Same CI platform. Fundamentally different tempo. It's 2026. Are you still blanket testing?  Predictive Test Selection uses AI to read every code change and run only the tests that actually matter: with zero trade-off in accuracy or coverage. Every selection logged, auditable, fully under your control. Smart Tests fixes the arithmetic. #DevOps #DevSecOps #CloudBees #SmartTests #CI #PlatformEngineering