Swarm Intelligence for AI Work Queues

The digital equivalent of ant colony pheromone trails. Your agents learn which tasks succeed—without explicit programming.

The Problem

Priority queues don't learn. Task A fails 90% of the time but still gets selected first. Your agents waste cycles on dead ends.

The Solution

Pheromone trails encode success. Agents follow strong signals, explore weak ones. The swarm collectively discovers optimal paths.

After implementing across 13 production projects:

84%
Task Success Rate
up from 62%
1,474
Leads Processed
autonomous
8%
Agent Idle Time
down from 23%

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Video walkthrough + Pareto scoring algorithm + Complete architecture docs

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What's Included

Video Walkthrough
15-min deep-dive into the architecture
Pareto Scoring
The 80/20 algorithm for project prioritization
Source Code
Full swarm-queue Python package
Production Guide
Deploy with Supabase in minutes

Built by Tom Fairhall | AI Business Hub

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