Building effective agents \ Anthropic

A post for developers with advice and workflows for building effective AI agents

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

• Karan MJ
• Varun Shenoy
• Freeman Jiang
• s j
• Information Flow
• Jayaditya Sethi
• Mohammed Ibrahim
• Vincent Cheng

related reading

• Building Effective AI Agents \ Anthropicanthropic.com
• LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
• Don’t Build Multi-Agents | Cognitioncognition.ai
• Agent Evaluation: A Detailed Guidecameronrwolfe.substack.com
• Effective context engineering for AI agents \ Anthropicanthropic.com
• Demystifying evals for AI agents \ Anthropicanthropic.com
• Adding payments to your LLM agentic workflowsstripe.dev
• What are agents? 🤔 · Hugging Facehuggingface.co
• Building reliable AI agents · parth sareenparthsareen.com
• What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
• Effective harnesses for long-running agents \ Anthropicanthropic.com
• What is an Agent? | Devinwindsurf.com