Evolution of Agent Landscape From 2022-26

A concise overview of how AI agent engineering evolved across three phases from 2022 to 2026: Phase 1 (weights) focused on training bigger models; Phase 2 (context) shifted to prompt engineering, RAG, and few-shot techniques; Phase 3 (harness engineering) moves intelligence into the surrounding environment — persistent memory, reusable skills, standardized protocols like MCP and A2A, execution sandboxes, and observability layers. The key insight is that the most impactful reliability improvements today come not from changing the base model but from building better infrastructure around it.