Hao Cui

An agent has no consistent definition. You can think of an agent as an autonomous or semi-autonomous system that can take actions on behalf of the user in a given environment, state, and make decisions or take actions that can accomplish certain tasks along a given time-frame, either by calling tools, dynamically selecting the next action to take or use a deterministic "workflow" (systems where LLMs and tools are orchestrated through predefined code paths). Using an Agent framework that gives you the ability to built systems in both, a reliable and a dynamic way can accelerate your agent development journey using agent abstractions. This means building Agentic systems that can reliably call tools, store memory (episodic/semantic/procedural), have comprehensive logging and observability, human in the loop workflows and the ability to build various multi-agent patterns flexibly based on your use case. A successful Agentic system in production is usually a combination of both, dynamic and predictable/reliable multi-agent systems. Strands Agents SDK gives you exactly these capabilities by treating each “agent” as a combination of a foundation model plus a suite of tools. You define a prompt and register your tools (decorated functions) in code, then Strands handles reasoning→planning→tool-execution cycles, local testing, and cloud deployment (ECS, Fargate, Lambda, EC2), along with support for all other agent abstractions provided above. Excited to share a hedge-fund analyst multi agent system: This uses the newest Anthropic's Claude 4 Sonnet/Opus that powers the Lead Analyst Agent, routing incoming queries to specialized sub-agents for fundamental, technical, and market analyses. Each specialist is wrapped as a callable tool (using the “agents-as-tools” multi-agent pattern), so the orchestrator never has to implement domain logic itself and can handoff the task to an agent as a tool. For sensitive operations (insider lookups), we utilize a HITL approval step that halts execution until a human grants consent. We also use meta-tooling that enables the Lead Analyst to generate, load, and invoke new custom tools at runtime—whether it’s a portfolio beta calculator or a pricer—without redeployment. Strands also embeds observability (Langfuse) and OpenTelemetry tracing so you can trace reasoning events, tool invocations, errors, and end-to-end workflows in real time. View more information on the code implementation here: https://lnkd.in/gJmwVyGi Code implementation: https://lnkd.in/gzTtJvJq Thanks to 🏄‍♂️ Cagatay Cali for being a reviewer/collaborator on this! Feel free to try it out and reach out with any questions/ideas. #aws #agenticAI #strands #agents #generativeAI

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My Startup Was Working… Until It Wasn’t We had paying customers. We had local press. We were gaining traction. It was 2016. Then UberEats, DoorDash, and Postmates entered Portland with billions in funding. At the same time, Intel—where I worked full-time—announced massive layoffs. Overnight, my 40-hour week almost doubled. Midnight fab shifts, weekend work, endless overtime. My startup didn’t die because the idea was bad. It died because I didn’t have the financial runway to compete. Here’s what that painful chapter taught me: 1. Passion + grit aren’t enough. Startups need time, capital, and breathing room to survive. 2. Corporate is just built for stability. It can’t nurture the creative chaos that startups need. 3. Adaptation beats stubbornness. Sometimes the smartest move is to regroup, save, and come back stronger. So I made a choice: doubled down on my W-2 career, moved to Seattle, and joined Amazon to maximize income. Every dollar earned—and every loan I could pull—went straight into real estate. One property a year turned into two… then into double digits in a single year. Deals kept coming, I kept closing, and I built a local team to keep momentum alive. By the end of 2022, my portfolio was generating more passive income than my paycheck. That’s when WoolooAI.com was born—not as a flashy startup, but as a necessary system to simplify and automate the chaos: acquisitions, CRM, portfolio management, project management, all streamlined. The truth is: - Without the grind of my early startup days, I wouldn’t have landed the Amazon role. - Without Intel’s layoff threats, I wouldn’t have learned the importance of building a financial runway first. - And without the expertise I developed at Amazon, I wouldn’t have gained the skills that helped me build WOOLOO AI. The irony isn’t lost: the same challenges that once crushed me ended up shaping the freedom I enjoy today. 💡 Takeaway: Sometimes what feels like an ending is really just the foundation for the next chapter. 👉 If you want the full story, I wrote it here: https://lnkd.in/geEVye36 💬 I’d love to hear your story too—what twists or setbacks ended up shaping where you are today? #Entrepreneurship #Startups #RealEstateInvesting #AmazonAlumni #FinancialFreedom #LessonsLearned #HumbleGrowth

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Tech hiring in January moves quickly. Teams come back with approved headcount and very little patience for half-ready candidates. If you are in tech and thinking about a move in Jan 2026, try this plan instead. 🧭 Audit your last 12 months 🛠 Tighten your technical story 💬 Prep for technical conversations 🔗 Make your work visible 📡 Signal availability early 🗓 Choose quality over volume January favours candidates who are clear, confident, and prepared. What part of your tech profile are you sharpening for 2026? #TechCareers #TechJobs #TechTalent #CareersInTech #JobSearch #Emmbr #recruitmenttips

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Amazon taught me that scale amplifies interpretation before it amplifies code. Two teams can read the same requirement and build two different systems, both in good faith. One optimizes for freshness. The other optimizes for consistency. Both think they are serving the same goal. The technical issue only appears later, when the outputs meet and the assumptions collide. That is why I became much more disciplined about forcing clarity early. A shared definition often saves more engineering effort than a shared library.

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Rust (or LLVM) is able to optimize what appears to be "copy-construction" into update-in-place when a function consumes a struct and returns a copy of that struct, even with some modifications to the original struct. The functional programming abstractions are truly zero-cost.

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The Age of the TPM Coordinator is ending. The Age of the Autonomous TPM has began. We will see TPMs focus less on being coordinators and more on creating platforms, workflows, and tools to scale program execution.

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This is a huge update that anyone who cares about workload identity or agentic security should be paying attention to. Kong Mesh is the first and only service mesh to natively act as a SPIFFE identity issuer, to interoperate with an existing SPIRE deployment, or to federate between two. This vastly improves interoperability in hybrid and multi mesh deployments. Great work Kuma team! We'll be sharing more about how this can help solve for modern enterprise security challenges in a future post.

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A lot shifted in 2025. AI made some things easier and other things more complicated, and it pushed teams to focus more on resilience. The 2025 State of the Internet Year in Review breaks down what stood out most this year. https://lnkd.in/eh2eZXfA

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