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Modern Product Discovery: Design Sprints, Agentic Coding, and Cross-Team Engineering

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In modern software development, writing code is rapidly approaching near-zero marginal cost. With autonomous coding agents and prompt-to-UI engines, the engineering bottleneck is no longer how fast we can produce software. Instead, a new, far more dangerous failure mode has emerged: building the wrong thing at ten times the speed. When execution becomes cheap, the only sustainable competitive advantage is judgment . How do we ensure we solve real customer problems? When should we run a Design Sprint versus continuous discovery? And how do we architect solutions that span multiple engineering squads without drowning in bureaucracy? This guide connects the dots across modern product discovery, the evolving Design Sprint, and cross-team engineering execution. 1. The Foundation: The Classic Design Sprint Developed by Jake Knapp at Google Ventures, the traditional Design Sprint is a time-boxed, five-phase framework engineered to bypass endless organizational debate and...
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Infrastructure as Code for the Agentic Cloud: AWS-First and Multi-Cloud Patterns

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Infrastructure as Code for the Agentic Cloud How the infrastructure strategy changes when you move from an AWS-first agent platform to a genuinely multi-cloud architecture — and where SST, Terraform, CDK and the Agent Platform API fit. Infrastructure as Code has traditionally been concerned with relatively predictable things: networks, compute, databases, queues, storage, identity and deployment pipelines. Agentic systems change the shape of that infrastructure. A production AI agent is not simply another process running in a container. It increasingly depends on an ecosystem of capabilities around it: model access, runtime isolation, memory, tool connectivity, workload identity, authorization policy, evaluation, tracing, guardrails and communication with other agents. The major cloud providers are responding by building increasingly complete agent platforms. AW...
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Beyond Agent Frameworks: The Emerging AI Harness Stack

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Why LangChain, Strands, Pi, Omnigent and a new generation of agent infrastructure point to a shift in how we build AI systems For the last few years, much of the conversation about building AI applications has revolved around agent frameworks . LangChain became one of the best-known examples. Then came alternatives such as LlamaIndex, AutoGen, Semantic Kernel, CrewAI, Pydantic AI, Mastra, Strands, Google ADK and the OpenAI Agents SDK. The usual comparison looked something like: Which framework has the best tool calling, memory, RAG, multi-agent support and model integrations? That is increasingly the wrong question. By the middle of 2026, a different architecture was becoming visible. The basic model-and-tools loop was no longer the interesting part of an agent system. Pydantic put it particularly clearly when announcing Pydan...
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