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Single-Agent Patterns

What you'll be able to do

Pick the right loop for an agent before you build it, and know the failure each loop brings - the decision that comes before every agent build.

⌁ The loop inside tool-using assistants and coding agents - Claude and ChatGPT tool use, LangGraph and Google ADK agents, AWS Strands agents, and customer-support automation.
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Create an account to unlock the 6 sections, the workbench, and askThili.
Begin

Sections

1. Single-Agent Patterns: ReAct, Plan-and-Execute, Reflection, Tool Use
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2. Lesson 1 - ReAct: think, act, observe
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3. Lesson 2 - Plan-and-Execute: plan the job, then do it
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4. Lesson 3 - Reflection: check your own work
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5. Lesson 4 - Tool Use: pick the right tool, with the right arguments
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6. Lesson 5 - Choosing a pattern
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Dig deeper

📄ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022, arXiv:2210.03629)
paper
📄Plan-and-Solve Prompting (Wang et al., 2023, arXiv:2305.04091)
paper
📄Reflexion: Language Agents with Verbal Reinforcement Learning (Shinn et al., 2023, arXiv:2303.11366)
paper
📄Toolformer: Language Models Can Teach Themselves to Use Tools (Schick et al., 2023, arXiv:2302.04761)
paper
🔗Building Effective Agents - Anthropic Engineering
article
🔗Groq - free API keys for open models
docs