Effective harnesses for long-running agents
Agents still face challenges working across many context windows. We looked to human engineers for inspiration in creating a more effective harness for long-running agents.
Agents still face challenges working across many context windows. We looked to human engineers for inspiration in creating a more effective harness for long-running agents.
We’ve added three new beta features that let Claude discover, learn, and execute tools dynamically. Here’s how they work.
Direct tool calls consume context for each definition and result. Agents scale better by writing code to call tools instead. Here's how it works with MCP.
Claude Code's new sandboxing features, a bash tool and Claude Code on the web, reduce permission prompts and increase user safety by enabling two boundaries: filesystem…
Claude is powerful, but real work requires procedural knowledge and organizational context. Introducing Agent Skills, a new way to build specialized agents using files and folders.
Context is a critical but finite resource for AI agents. In this post, we explore strategies for effectively curating and managing the context that powers them.
This is a technical report on three bugs that intermittently degraded responses from Claude. Below we explain what happened, why it took time to fix, and…
Agents are only as effective as the tools we give them. We share how to write high-quality tools and evaluations, and how you can boost performance…
Desktop Extensions make installing MCP servers as easy as clicking a button. We share the technical architecture and tips for creating good extensions.
Our Research feature uses multiple Claude agents to explore complex topics more effectively. We share the engineering challenges and the lessons we learned from building this…