🤖Agentic AI Evolves Through Three Paradigms

The evolution of agentic AI systems, explained

Agentic AI systems have evolved through three distinct paradigms: Chains, Graphs, and Code. Chains represent the initial era of linear workflows, offering low latency and compute costs for narrow tasks. Graphs introduce explicit structure and operational control, enabling sophisticated capabilities and multi-agent systems. Code harnesses treat execution as a first-class primitive, utilizing sandboxed environments and iterative refinement loops. Developers must treat LLM outputs as untrusted input, decouple planning from execution, and invest in observability. This evolution is crucial for real-world applications and system robustness.