ColaVLA replaces explicit text‑based reasoning with latent‑space inference and a hierarchical parallel planner, achieving lower trajectory error, reduced collision rates and up to ten‑fold faster inference while preserving safety and real‑time performance in autonomous driving benchmarks.
This article provides a comprehensive guide to AI Agent planning modules, covering their core responsibilities, architectural designs, major planning paradigms such as ReAct, Plan‑and‑Execute, Hierarchical Planning and Reflexion, detailed prompt engineering, execution frameworks, and practical case studies in data analysis and intelligent customer service.
Eko is an open‑source, cross‑platform agent framework that lets developers combine natural‑language prompts with programming code to create production‑grade virtual employees, featuring mixed agentic representation, environment‑aware architecture, hierarchical planning, visual‑interactive perception, and a multi‑level hook system for safe, controllable automation.
