Scaling Managed Agents: Decoupling the brain from the hands \ Anthropic
anthropic.com · 2,065 words · saved by 3 readers
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Get started with Claude Managed Agents by following our docs . A running topic on the Engineering Blog is how to build effective agents and design harnesses for long-running work . A common thread across this work is that harnesses encode assumptions about what Claude can’t do on its own. However, those assumptions need to be frequently questioned because they can go stale as models improve. As just one example, in prior work we found that Claude Sonnet 4.5 would wrap up tasks prematurely as it sensed its context limit approaching—a behavior sometimes called “context anxiety.” We addressed thi
saved by
related reading
- How we contain Claude across products \ Anthropicanthropic.com
- Building Effective AI Agents \ Anthropicanthropic.com
- Effective harnesses for long-running agents \ Anthropicanthropic.com
- Effective context engineering for AI agents \ Anthropicanthropic.com
- Claude’s Constitution \ Anthropicanthropic.com
- Harness design for long-running application developmentanthropic.com
- Best practices for Claude Code - Claude Code Docsanthropic.com
- Towards self-driving codebases · Cursorcursor.com
- A Guide to Claude Code 2.0 and getting better at using coding agents – sankalp's blogsankalp.bearblog.dev
- Designing agentic loopssimonwillison.net
- Demystifying evals for AI agents \ Anthropicanthropic.com
- When AI builds itself \ Anthropicanthropic.com