Building an AI Agent Is Easy — Designing One That Reasons, Remembers, and Runs Safely Isn't

goyalshaliniuk · x · 2026-10-07

A long-form thread argues that a strong agentic AI system is a complete architecture, not just an LLM with a prompt:

Start from the use case: scope, automation level, success metrics, and risk level
Pick models by reasoning ability, tool use, context window, latency, and cost
Design clear instructions, connect trusted knowledge via RAG, and build memory for cross-task state
Grant controlled access to APIs, databases, search, code, and business systems
Add planning and orchestration: decompose goals, select tools, route tasks, replan on failure
Keep humans in the loop and design for safe production operation

Generic methodology with no concrete case studies — useful as a design checklist.

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