Building Effective AI Agents \ Anthropic

Discover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.

Over the past year, we've worked with dozens of teams building large language model (LLM) agents across industries. Consistently, the most successful implementations weren't using complex frameworks or specialized libraries. Instead, they were building with simple, composable patterns. In this post, we share what we’ve learned from working with our customers and building agents ourselves, and give practical advice for developers on building effective agents. What are agents? "Agent" can be defined in several ways. Some customers define agents as fully autonomous systems that operate independen

saved by

• Amir
• Ratan Kaliani
• Emma Guo
• Maanav Khaitan
• Ayush Shukla
• Agnim Agarwal
• Aliyah Sausan
• Senthilnathan Thirumurugan

related reading

• Building Effective AI Agents \ Anthropicanthropic.com
• Demystifying evals for AI agents \ Anthropicanthropic.com
• Effective context engineering for AI agents \ Anthropicanthropic.com
• What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
• Designing agentic loopssimonwillison.net
• When AI builds itself \ Anthropicanthropic.com
• Building reliable AI agents · parth sareenparthsareen.com
• LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
• Ichigo (@iiiichigo_chan) on Xx.com
• How we built our multi-agent research system \ Anthropicanthropic.com
• Agent Evaluation: A Detailed Guidecameronrwolfe.substack.com
• AI Agents: How to build Digital Workers | by Alfredo Sone | Mediummedium.com