Multi-Agent Workflow Automation
A Claude-based multi-agent system for a regional logistics company — processing 10,000+ daily decisions autonomously, coordinating routing, dispatch, and exception handling without human bottlenecks.
The Challenge
A regional logistics company with 200+ drivers and 50+ client accounts was hitting a coordination wall. Every morning, dispatch managers spent 3-4 hours manually planning routes, assigning drivers, and resolving conflicts. Throughout the day, exceptions — traffic, weather, vehicle issues, customer requests — required constant human intervention.
The decision volume was growing faster than the team. Adding more dispatchers wasn't economically viable. They needed a system that could handle routine decisions autonomously, escalate genuine exceptions, and learn from how their best dispatchers thought about complex scenarios.
Previous attempts with rule-based routing software had failed. Logistics decisions involve too many interdependencies, too many soft constraints, and too much contextual knowledge to encode in rules. They needed something that could reason about tradeoffs.
Multi-Agent Architecture
We designed a Claude-based multi-agent system with specialized agents handling different decision domains, coordinated by an orchestrator agent. The key architectural principle: each agent is narrow enough to be reliably good at its domain, but the system as a whole can handle the full complexity of daily operations.
Orchestrator Agent
The central coordinator. Receives incoming requests (new deliveries, exceptions, driver status changes), determines which specialist agents to invoke, and assembles final decisions. Uses Claude to reason about priority and sequencing.
Routing Agent
Optimizes delivery routes given driver locations, delivery windows, vehicle capacity, and real-time traffic. Integrates with mapping APIs. Reasons about tradeoffs between time, cost, and customer priority.
Driver Assignment Agent
Matches deliveries to drivers based on location, availability, vehicle specs, and historical performance. Understands soft constraints like driver preferences and client-specific requirements.
Exception Handler Agent
Processes the unexpected: late deliveries, vehicle breakdowns, customer reschedules, weather events. Generates recovery options, estimates impact, and decides what to handle autonomously vs. escalate.
Communication Agent
Drafts and sends updates to drivers and customers. Handles the communication coordination that consumed significant dispatcher time — status updates, delay notifications, confirmation messages.
Technical Implementation
The system is built on LangGraph for agent orchestration, with Claude as the reasoning model for all agents. We use claude-3-5-sonnet for routine decisions (speed-optimized) and escalate to claude-3-opus for complex exception handling.
A critical design decision: every agent action is logged with full context — the input, the reasoning chain, and the output. This audit trail serves two purposes: compliance and continuous improvement. The dispatch team can review decisions, provide feedback, and those corrections feed back into the system's context for similar future decisions.
The system processes events via a queue, with the orchestrator handling prioritization. Standard routing decisions run in under 2 seconds. Complex exception handling may take 8-15 seconds but is handled asynchronously, so it doesn't block the main workflow.
Key Takeaways
Narrow agents outperform general agents
A routing agent that only does routing is more reliable than a single agent trying to handle everything. The orchestrator pattern keeps complexity manageable and makes individual agent behavior predictable.
Escalation design is the product
The value of this system isn't in the decisions it makes — it's in correctly identifying which decisions require human judgment. Getting escalation thresholds right took more iteration than the agents themselves.
Audit trails enable trust
The dispatch team adopted the system faster than expected because they could see the reasoning behind every decision. Explainability wasn't a nice-to-have; it was what made humans comfortable ceding control.
Tech Stack
Build a multi-agent system?
We've built multi-agent systems for logistics, financial services, and operations teams. The architecture patterns transfer.
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