Maintained reading path from EnhanceLearning.AI — practitioner-grade articles for engineers, architects, and technology leaders building production AI-native systems.
Topic on the site: Agentic AI · Full library: enhancelearning.ai/articles
A curated reading path for Agentic AI. It is not a code SDK — it points to the foundation deep-dives on EnhanceLearning.AI so you can align on concepts, critique designs, and ship agents that hold up in production.
This topic covers agent anatomy, harness engineering, loop design, and the hard line between agents and classical automation. You will find guidance on termination criteria, tool use, and why many "agents" in the wild are still workflows in disguise.
Engineers and architects building agent products, copilots with side effects, or internal automation that must be auditable.
- The Difference Between an AI Assistant and an AI Agent — Where suggestion ends and independent action begins in AI-native systems — a precise boundary teams use loosely but rarely define in architecture reviews.
- Why Agentic AI is Not RPA With a Language Model — How agentic reasoning differs from RPA bolted to an LLM — and why that misconception picks the wrong controls, evals, and failure modes.
- Automation, Orchestration, and Agentic AI Agency — Clear definitions of automation, orchestration, and agentic agency in AI-native engineering — so teams stop using three different words for the same slide.
- The Anatomy of an Agentic AI System — Core parts of a real agentic system—perception, reasoning, planning, action, state, and bounds—and how they fit in production architectures.
- Why Most AI Agents Are Workflows in Disguise — Tell true agentic reasoning from deterministic orchestration in disguise — and assess whether your system chooses next steps or follows scripts.
- Loop Engineering for Agentic Systems — How to design agent loops that terminate: observe, decide, act, verify — with budgets, escapes, and feedback that does not spin forever.
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