← All drillsSingle-Agent Patterns
What you'll be able to do- ✓Build a ReAct agent that reasons before every tool call, survives a failing tool, and stops at a step limit
- ✓Build a Plan-and-Execute agent with a planner, an executor and a replanner, and see a stale plan fail
- ✓Build a reflection loop and compare a vague critic, a rubric critic and a rules-as-code critic on the same emails
- ✓Measure tool selection on a scoreboard and fix look-alike tool descriptions
- ✓Put a code guard in front of an irreversible tool so a bad request cannot reach it
- ✓Choose between the four patterns from your own measurements of cost and correctness
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.
Start this internshipCreate an account to unlock the 6 sections, the workbench, and askThili.
BeginSections
1. Single-Agent Patterns: ReAct, Plan-and-Execute, Reflection, Tool Use
🔒 locked2. Lesson 1 - ReAct: think, act, observe
🔒 locked3. Lesson 2 - Plan-and-Execute: plan the job, then do it
🔒 locked4. Lesson 3 - Reflection: check your own work
🔒 locked5. Lesson 4 - Tool Use: pick the right tool, with the right arguments
🔒 locked6. Lesson 5 - Choosing a pattern
🔒 lockedDig 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