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Anthropic introduced a dedicated "think" tool that gives Claude a scratchpad to reason through complex multi-step tool use situations before acting. In tests, adding the think tool significantly improved accuracy on tasks requiring careful sequencing of tool calls. A simple but effective technique any agent developer can adopt immediately.
Anthropic achieved then-state-of-the-art performance on SWE-bench Verified with Claude 3.5 Sonnet using only a bash tool and a text editor — no scaffolding tricks. The post details the technical approach and what the benchmark results imply about real-world software engineering capability. A landmark result that established Claude as a serious coding model.
Anthropic's foundational guide to building reliable AI agents — covering when to use agents vs. simpler pipelines, how to design tool interfaces, how to handle failures gracefully, and the core patterns (augmented LLM, routing, parallelisation, orchestrator-subagent) that underpin most production agent systems. Still one of the best starting points for anyone new to agent development.
Anthropic introduced Contextual Retrieval — a technique that prepends chunk-specific context to each piece of text before embedding, dramatically improving RAG retrieval accuracy. Combined with BM25 reranking, it reduced retrieval failure rates by up to 67% compared to standard RAG. A practical technique that any team using RAG with Claude can adopt today.
