Agentic Engineering vs AI Engineering: how the two disciplines actually differ
Pavan_Belagatti · x · 2026-11-04
The author clarifies two often-conflated concepts:
- AI Engineering: the broader discipline of designing, building, evaluating, and operating AI-powered applications — RAG, fine-tuning, model evaluation, inference, even agents; typically closer to AI/ML/data engineers' day-to-day work.
- Agentic Engineering: engineering systems where AI agents plan, use tools, manage state, coordinate workflows, and take goal-directed actions with approvals; more associated with software developers.
He stresses the two aren't mutually exclusive — developers can do AI engineering and vice versa; the difference is mainly in day-to-day focus. An accompanying diagram contrasts a multi-agent incident-response flow (alert → metrics/logs/deployment agents → correlation → human approval → rollback) with a classic RAG Q&A pipeline.
More from coding & agent
- Phone Harness (YC F26) open-sources a harness that lets AI agents control real phones — ycombinator · 2026-11-05
- The vibe coding blind spot: people can't articulate their AI-built products' benefits — rms80 · 2026-11-05
- Inside a16z's agent podcast: trust is the top fear among 147 tracked AI assistants — a16z · 2026-11-05
- Non-dev built an agent-only eSIM store; a Claude agent bought one with real USDC — Apprehensive_Big5042 · 2026-11-05
- Google Cloud's five-rule playbook for evaluating whether agent skills are worth their tokens — rseroter · 2026-11-05
- Google DeepMind 工程师将登 AIE CODE 峰会,讲述删掉 90% 评估框架的故事 — _philschmid · 2026-11-05