A 7-step cheat sheet for building AI agents, from system prompt to evals
mdancho84 · x · 2026-09-05
Data scientist mdancho84 shared a cheat sheet breaking AI agent construction into seven steps:
- System Prompt: define the agent's role, capabilities, and boundaries
- LLM: pick an engine (GPT-5, Claude, Mistral, or open-source) based on reasoning needs, latency, and cost
- Tools: API access, code interpreters, database queries, web search — up to 20
- Orchestration: use frameworks like LangChain, AutoGen, or CrewAI for reasoning, task decomposition, and multi-agent collaboration
- Memory: short-term via context window, long-term via vector DBs (Pinecone, Weaviate, Chroma)
- UI: an intuitive chat interface or business automation workflow
- AI Evals: test on real-world tasks with TruLens, Rebuff, or custom evals to measure effectiveness, reliability, and safety
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