Jeff Sandquist

At Intuit's recent Tech Leadership Forum, I talked about one of my favorite concepts: ambidextrous leadership. When you're an ambidextrous leader, you can have two competing ideas in your mind at the same time, and you lead through both of them. For example, should you behave like a fast-moving company that builds lots of scrappy things and experiments constantly with customers? Or should you behave like a platform company that delivers rock-solid capabilities and drives leverage and reuse? The answer for Intuit is yes—to both. These two behaviors compete with each other and they’re supposed to. It’s the job of leaders to navigate them both at the same time.

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Had a lively discussion with emerging leaders of Microsoft Silicon Valley about leading through “do more with less” in an AI-everywhere world. Key takeaways: • Burnout prevention starts with subtraction. Automate and eliminate work that doesn’t matter. Protect attention for judgment, creativity, and customer impact : the things AI can’t replace. • Steadiness is a behavior. Clear language, consistent decision cadence, and simple meeting rituals create calm even when teams fear they’re “not on critical AI work.” • When everything is Priority #1, nothing is. A real triage playbook - right room, right metrics, courage to defer - helps teams focus instead of fearing obsolescence. • Burnout needs intervention, not inspiration. Create think time, rebalance workload, and offer support. Small habits (breathwork, gym, walk-and-talks, tech quiet hours) help people reset in a rapidly changing AI landscape where expectations spell "ship this yesterday!" • Failure is resilience. Safe-to-fail experiments help teams learn, stay relevant, and realize that growth - not perfection - is the antidote to AI-era anxiety. Leadership today is about creating clarity, psychological safety, and meaningful work so our teams can stay energized and confident in an AI-accelerating world. Ty to the WAM team, Microsoft SVC.

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Interested in how we test our systems at Amazon? In our new article in Communications of the ACM, Ankush Desai and I write about the evolution of systems correctness and testing practices at AWS. We cover a mix of classic approaches, formal approaches, and new techniques like deterministic simulation. https://lnkd.in/gwdncKM4

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People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.

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The Rundown: Shopify CEO Tobi Lütke released an internal memo mandating AI proficiency across the commerce giant, declaring that teams must prove AI can’t do the job before seeking additional resources and making AI a “baseline expectation.”

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How do you turn today’s AI models into a trusted, unified system that actually executes real-world humans tasks—across apps, chains, and devices—without requiring every user or business to build custom integrations?

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re:Invent Day 1 is done and it was packed! My day began with a roundtable with some of our best partners talking about how we can better help customers migrate and modernize their AI workloads and use cases. Then Priya Arora and I presented on-stage on how Agentic AI is opening opportunities for partners, from ISVs and SIs, to startups. We shared data from our survey we did with BCG showing the trends in customer adoption of AI, focusing on the industries and use cases with the highest momentum. We also uncovered areas where customers need the most help and support as they roll out their agentic use cases. After that I sat down with 3 key partners, grabbed lunch with a few AWS peers I don't get to see often enough, and had a lot of ad hoc hallway meetings. (It's hard to walk 10 feet at re:Invent without seeing someone you know) But my highlight was launching the new AI Competency and Agentic AI categories for partners. I had a chance to join the AWS OnAir team to talk about it. My team was the driving force behind the launch and I'm proud of what this means for our partner community. We spent months listening to partners tell us they needed a way to stand out in customer conversations around agentic AI. The new AI Competency creates three distinct specialization paths. Agentic AI Applications recognizes partners delivering production-ready autonomous systems. Agentic AI Tools validates partners providing the infrastructure and tooling that makes agent development possible. Agentic AI Consulting Services distinguishes partners with proven expertise helping enterprises design, build, and scale agentic deployments. Each path requires demonstrated technical depth and validated customer outcomes, not just certifications or marketing claims. We launched with 60 partners who achieved the AI specialization. That's the highest number of launch partners in any AWS Competency program so far. Partners like Loka, Anthropic, LangChain, and Mission are already proving their ability to deploy AI systems that handle real business processes autonomously. That validation matters when enterprises are making critical technology decisions. Partners achieving these specializations gain access to funding, co-marketing resources, and priority placement in customer engagements. But what matters most is the market differentiation. When customers are evaluating dozens of partners claiming agentic AI expertise, this competency provides a clear signal about who has actually done the work. For enterprises evaluating partners, this competency provides the differentiation signal you need. These partners have solved the hard problems of moving agents from demo to production, from single use case to enterprise platform. The timing matters. Enterprises are moving past whether to deploy agentic AI and into how to do it at scale. Partners with proven expertise become increasingly valuable as deployment complexity increases. That's what makes this competency so important.

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Today, Adobe announced the general availability of AI agents that will reshape how businesses build, deliver and optimize customer experiences and marketing campaigns. Powered by the Adobe Experience Platform (AEP) Agent Orchestrator, Adobe is also creating an AI platform for businesses to manage and customize agents from Adobe and across third-party ecosystems — ensuring agents can understand context, plan multi-step actions, refine responses and more. #AIAgents #MarketingInnovation https://lnkd.in/g8MQpwvw

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Quick 10-minute primer on how goose works as an AI agent, how it fits into the broader AI ecosystem, and how it uses MCP to interact with tools, automate tasks, and enhance productivity.

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The simultaneous emergence of Claude Cowork and Clawdbot (now rebranded as Moltbot) is a clear signal of where consumer AI is headed this year: genuine end-user productivity through semi-autonomous agents. The days of purely prompt-based, one-shot results are giving way to systems that can handle real, ongoing work based on high-level requests. These tools are starting simple, but their trajectory is unmistakable: they will likely evolve quickly into capable digital assistants running 24/7. If recent AI trends hold, most of us will soon dedicate at least one (if not several) always-on machines or cloud instances to handling our routine personal and professional chores. This shift could free up significant time and mental energy, allowing us to focus on higher-leverage activities with far greater returns. Security, however, remains the single biggest hurdle. Granting AI agents access to local files, email, calendars, or messaging apps introduces real risks such as prompt injection, data leakage, unintended deletions, or worse. While tools like Cowork use sandboxes and careful permissions, and self-hosted options like Moltbot keep data local, vulnerabilities persist and demand vigilance. For now, the wisest path is cautious experimentation: start small, isolate sensitive data, monitor closely, and contribute feedback to help shape safer, more reliable agents. These early steps are opening exciting new chapters for everyday users. AI, it appears, won't just answer questions, but will truly work alongside us.

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Part 2 of my ongoing series From Vibe Code to Production-Ready is live! This one dives into Claude, Gemini, and MCPs (Zen + Serena) -- not just as shiny tools, but as a framework for moving from quick prototypes into reliable systems: Command patterns, context, and the practices that make "vibe coding" scale. Part 1 explored why vibe coding eventually breaks down, and Part 2 is the bridge: where experimentation starts becoming engineering. Part 3 will explore agent command patterns: Compacting, Context, and Claude Code Ops Read here: https://lnkd.in/e8-dnaYC #AI #MCP #ClaudeAI #Gemini #AgenticAI #Engineering #ProductDevelopment #VibeCoding #FromPrototypeToProduction #TechLeadership

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