Evolution of agentic surfaces — Gagan Bhat & Isabella Kai He, Anthropic

Anthropic's Applied AI team presents how agents — AI systems that independently solve tasks — should be built to handle long-running production jobs.

They show that outdated solutions become overhead as models become smarter: Sonnet 4.5 was cautious with memory and exited early, so they built in a fix — but when Opus 4.5 was deployed without this behavior, the fix became merely slow redundant code. The key insight is separating the agent's brain (reasoning loop) from its hands (tool execution), making systems 60–90 percent faster and making errors recoverable instead of fatal. A log of everything that happens during a session serves three purposes: seeing what the agent does, retrieving information Claude forgot, and something they call "dreaming" — a nightly job that improves the agent's memory for the next day.

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