Loom is an AI harness, that can be used with local or cloud LLMs, for complex tasks. It decomposes work, drives execution through a verification harness, and keeps models on track with structured state instead of history. It can route between thinking and acting models, verifies outputs, and exposes an APP/TUI/API/CLI/MCP for both humans & agents.

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! 🚀

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

AI-Driven Project Decomposition and Implementation

Make any LLM 10x smarter. Recursive DAG decomposition + temporal knowledge graph = free models that match expensive ones. 30/30 tasks passing at $0.0006 total cost.

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

Autonomous coding agent harness built on Claude Code SDK — implements Anthropic's long-running agent pattern: scan → plan → code → validate → git-commit in a loop. Turn requirements into working code with one command.

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

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

Enable deterministic distributed execution for AI workflows with replay-safe billing, contention safety, and trust-driven scheduling.