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GitHub 4 months ago 16
Agent-Native is a framework for building applications that function both as traditional apps and AI agents from a single codebase. The framework uses shared actions that power multiple interfaces—UI, agent, HTTP, MCP, A2A, and CLI—with included runtime features for chat, tools, memory, and observability. Developers can now build apps that work across surfaces without duplicating work or choosing between agent and application architectures.
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TLDRocket
20 October 2026
Today’s biggest thread is the build-out of “agent infrastructure” that makes AI assistants feel less like chat windows and more like tools with their own computers, workflows, and controls. Daytona, a startup branding its stack as an “agentic cloud,” raised a $60 million Series B to a $1 billion valuation, aiming to speed up sandbox provisioning for agent “computers” with a 60-millisecond setup time—an attack on the bottleneck of traditional VM spin-up. IBM, Red Hat, and contributors also pushed in the same direction with llm-d framework v1.0, adding operational controls for distributed in-house inference, including a router that captured 97% cache reuse in an agentic coding test and improvements for multi-tenant QoS, zero-downtime upgrades, and request routing.
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