Agentic AI refers to AI systems capable of autonomous decision-making, planning, and executing tasks based on goals—acting like intelligent agents. These systems combine LLMs with tools, memory, and feedback loops to complete complex workflows with minimal human input.

Instantly decompose Azure DevOps work items into hierarchical trees and streamline agile planning in a single action! 🚀

AI-Driven Project Decomposition and Implementation

Loom is a harness stack, primarily for local LLMs, to finish complex tasks. It decomposes work, drives execution through a verified harness, and keeps models on track with structured state instead of chat history. It can route between thinking and acting models, verifies outputs, and exposes a TUI/API/CLI/MCP for both humans and agents.

Krako 2.0 – Energy-efficient, triadic multi-tier inference infrastructure enabling adaptive routing across heterogeneous edge–cloud nodes.

TDNet (Degradation-aware Comprehensive Task Decomposition for Joint Rain and Haze Removal)

Auton — Framework for AI agent delegation, multi-agent orchestration, and adaptive task decomposition. Zero dependencies, OpenAI-compatible. Based on Intelligent AI Delegation research.

Automatically decompose complex prompts into orchestrated, executable workflows for Claude Code. Research-backed prompt chaining and task decomposition.

Intelligent travel planning with multi-agent AI orchestration using n8n

🧠 Self-organizing AI agent with dendrite spawning - decomposes complex goals into neurons, spawns parallel sub-tasks, and aggregates results. Features smart parameter extraction, context isolation, and automatic data compaction. Built for Strava analytics with extensible tool system.

AI-powered task organizer with intelligent decomposition, skill gap analysis, and JIT learning