In the past year, the enterprise AI ecosystem has gained enormous capability and zero consensus.
Developers now have a remarkable set of tools for building AI agents: OpenAI’s frameworks, Anthropic’s Claude tooling, LangChain, LangGraph, CrewAI, Microsoft AutoGen, and a growing list of alternatives. Each promises to coordinate reasoning loops, manage multi-step task execution, and connect agents to tools and APIs. For experimentation, the progress has been substantial. Teams can now assemble sophisticated agent workflows in days that would have taken months two years ago.
But I’ve watched this pattern before. In over two decades of building and selling distributed systems platforms, I’ve seen the same dynamic play out across nearly every major infrastructure shift: the tools for consuming a new capability arrive before the infrastructure for governing it does. The gap that emerges isn’t immediately obvious in development environments. It becomes obvious in production.
That’s exactly wh
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Microsoft clipper spreads by USB, swaps addresses, and sniffs BIP39 via Tor; TrapDoor taints dev packages; hardware wallets carry supply-chain caveats. Defenses that help.
Collaboration spans memory and storage AI architecture design, supply agreement, enterprise AI adoption and a strategic investment BOISE, Idaho, June 22, 2026 — Micron Technology, Inc. today announced a strategic […]
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Amazon's potential $3T valuation highlights the growing influence of cloud and AI investments, reshaping tech market dynamics and investor strategies.
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