Engineering at Anthropic: Inside the team building reliable AI systems
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How we contain Claude across products
As agents grow more capable, so does their potential blast radius. The engineering question is how to cap it. Here’s what we’ve learned building containment for claude.ai, Claude Code, and Cowork.
An update on recent Claude Code quality reports
Scaling Managed Agents: Decoupling the brain from the hands
How we built Claude Code auto mode: a safer way to skip permissions
Harness design for long-running application development
Eval awareness in Claude Opus 4.6’s BrowseComp performance
Quantifying infrastructure noise in agentic coding evals
Building a C compiler with a team of parallel Claudes
Designing AI-resistant technical evaluations
Demystifying evals for AI agents
Effective harnesses for long-running agents
Introducing advanced tool use on the Claude Developer Platform
Code execution with MCP: Building more efficient agents
Beyond permission prompts: making Claude Code more secure and autonomous
Equipping agents for the real world with Agent Skills
Effective context engineering for AI agents
A postmortem of three recent issues
Writing effective tools for agents — with agents
Desktop Extensions: One-click MCP server installation for Claude Desktop
How we built our multi-agent research system
Claude Code: Best practices for agentic coding
The "think" tool: Enabling Claude to stop and think in complex tool use situations
Raising the bar on SWE-bench Verified with Claude 3.5 Sonnet
Building effective agents
