Submission · Musa Labs Hackathon · Logistics

Integral Logistics: Multi‑Agent System

Built during today’s sprint in Azure AI Foundry

5‑agent architecture aligned to Integral Theory's four quadrants

Try the Demo GitHub Repo

Overview

We prototyped a 5‑agent logistics intelligence: four specialist agents—Individual‑Internal (UL), Individual‑External (UR), Collective‑Internal (LL), Collective‑External (LR)—coordinated by an Integral Orchestrator. Using representative (synthetic) datasets produced with ChatGPT, the system turns human narratives, behavioral cadence, culture signals, and throughput into a traceable 90‑day plan.

$1.7M
annualized savings @ 250‑truck fleet
−18%
idle / detention
+7.5%
on‑time delivery (OTD)

Problem

Traditional logistics stacks optimize visible external metrics (LR) while missing internal and cultural causes. Pre‑AI BI cannot parse free‑text driver journals (UL), infer safe cadence beyond compliance logs (UR), or embed vendor culture dynamics (LL). Those blind spots produce avoidable detention, idle time, and missed deliveries.

Why pre‑AI tools fall short

  • Rule dashboards ≠ language understanding
  • No cross‑modal synthesis (qualitative + quantitative)
  • Single‑quadrant focus treats symptoms, not causes

AI Agent Solution

Implemented in Azure AI Foundry using Microsoft’s agentic orchestration. We built one agent per quadrant plus an orchestrator that plans, routes tools, and emits ranked, explainable actions.

UL · Sleep
Embed free‑text sleep logs → fatigue vectors; overlay route risk.
UR · Cadence
Detect unsafe duty cycles; prescribe chronotype‑aligned breaks.
LL · Culture
Analyze vendor feedback; predict dock friction; propose playbooks.
LR · Throughput
Forecast capacity; surface cliffs; attribute cross‑quadrant causes.
Orchestrator
Fuse signals; produce 90‑day plan with sources + confidence.

Hackathon Criteria

Impact by Dollars: predicted $1.7M/yr savings, −18% idle, +7.5% OTD.

Responsible AI: PII scrubs, audit logs, cite‑to‑source, bias checks.

Completeness: running prototype in Foundry but not published; synthetic data used today.

Note: We couldn't get a demo completed during the hackathon itself, and we didn't have a chance to save our code. So the day after, we built another prototype from scratch. Try it here.

Architecture (today’s prototype)

Ingestion

  • UL/LL text → embeddings (vector index)
  • UR/LR numerics → normalized time series
  • Synthetic samples generated with ChatGPT

Agents

  • Role‑pinned prompts + tool calls
  • Light Python stats functions
  • Conversation memory per quadrant

Orchestrator

  • Planner → call agents → fuse results
  • Ranked actions with confidence + citations
  • Audit log for safety/compliance

Execution environment: Azure AI Foundry only · No external services · All artifacts created during the event.

Team

Bre’Shawna Chambers · Jasmine Royal · Dalmo Mendonca · Marques Chambers · Jamie Johnson