Building our truck brokering multi-agent system for Community Summit NA taught us more in three weeks than months of reading documentation ever could. The architecture looked elegant on paper. Reality was messier.
This isn't about theory or best practices. This is about the mistakes we made, the surprises we hit, and what actually worked. If you're building multi-agent systems, learn from our scars.
Lesson 1: Agent Communication Is Harder Than You Think
What we expected: Agents pass clean JSON between themselves. Simple.
What actually happened: Communication became our biggest source of bugs.
The technical part—APIs and message queues—works fine. The problem was semantic. When the Load Analysis agent says a shipment is "urgent," what does that mean to the Matching agent? A priority score? A time constraint? A boolean flag?
We spent days debugging random decisions from our Match Scoring agent. The issue? It was interpreting the Load Analysis agent's output completely differently than we intended.
The fix: Explicit data contracts between agents. Not just JSON schemas—documented semantic meanings. Validation layers that catch mismatched expectations early. Extensive logging to trace how data transforms through the pipeline.
Takeaway: Define clear communication contracts upfront. Your debugging self will thank you.
Lesson 2: Start With Three Agents, Not Six
What we did: Designed all six agents from the start—Intake, Load Analysis, Matching, Geographic Optimization, Match Scoring, Route Analysis.
What we should have done: Started with three core agents and added the others incrementally.
When everything is new—orchestration, communication protocols, error handling—debugging six agents simultaneously is overwhelming. You see a bad result and have to trace through six potential failure points.
Week two, we disabled three agents and focused on the core workflow: Intake → Load Analysis → Matching. Once that foundation was solid, adding the other agents was straightforward.
Takeaway: Build incrementally. Get core agents rock-solid before expanding. Slower at first, faster overall.
Lesson 3: The Orchestrator Will Become a Bottleneck
The problem: Our centralized orchestrator handled every communication between agents. As volume increased, it became a performance bottleneck.
The revelation: Not all communication needs orchestration.
Our Route Analysis agent needed real-time updates from Geographic Optimization. These updates didn't need to flow through the orchestrator—it didn't need to know about every location ping or traffic update.
We introduced direct agent-to-agent communication for real-time data streams while keeping strategic coordination centralized. This hybrid approach gave us the performance we needed without sacrificing orchestration benefits.
Takeaway: Start centralized for simplicity. Add decentralized elements for performance-critical paths. Don't be dogmatic—adapt to your actual needs.
Lesson 4: Error Handling Is Not an Afterthought
What happened: Three days before the presentation, our system started randomly failing mid-workflow. Perfect one run, crashed at Matching stage the next. We couldn't reproduce it consistently.
The culprit: We hadn't thought through error handling and retry logic.
When Geographic Optimization occasionally timed out (calling an external routing API), the orchestrator didn't know what to do. Retry? Fail the workflow? Use cached data? We hadn't decided, so it did nothing. The workflow hung.
The fix: A proper error handling strategy:
- Transient errors (API timeouts, rate limits): Automatic retry with exponential backoff
- Data errors (invalid formats, missing fields): Fail fast with clear messages
- Agent failures (crashes, exceptions): Graceful degradation or workflow termination
- Circuit breakers: Stop calling failing services temporarily
We added health checks for each agent and built a monitoring dashboard showing which agents were healthy, which were struggling, and where workflows were stuck.
Takeaway: Design your error handling strategy before you need it. Multi-agent systems have multiple failure points—plan for each.
Lesson 5: Observability Is Your Lifeline
The reality: With six agents making decisions, tracing why the system did something is surprisingly hard.
We'd get a final result—"Truck 47 assigned to Load 203 with a profitability score of 0.73"—and someone would ask, "Why not Truck 51?" Good question. Let us trace through six agents' decision-making processes and get back to you in an hour.
The solution: Comprehensive observability:
- Correlation IDs: Every workflow got a unique ID flowing through all agents
- Decision logging: Each agent logged what it decided and why
- Trace visualization: A simple UI showing the complete workflow path
- Performance metrics: How long each agent took, where bottlenecks occurred
This wasn't just for debugging. During the demo, showing the complete decision-making trace added tremendous value. People could see the "why" behind the system's choices.
Takeaway: Observability isn't optional. Build it in from the start for debugging, optimization, and explaining your system to stakeholders.
Lesson 6: Test With Realistic Data
Our mistake: Early testing used clean, simple data. "Truck A at Location X needs to go to Location Y." Everything worked beautifully.
Then we tested with real-world data—trucks with maintenance schedules, loads with special handling requirements, routes with toll roads and weight restrictions. The system fell apart.
The Load Analysis agent couldn't handle commodity classifications it hadn't seen. Geographic Optimization didn't account for truck height restrictions. Matching made legally impossible pairings (refrigerated loads to dry van trucks).
The fix: A comprehensive test dataset including edge cases, real-world constraints, data quality issues, and high-volume stress tests.
Takeaway: Your multi-agent system is only as robust as your test data. Invest in comprehensive, realistic test scenarios.
Lesson 7: Documentation Is Not Optional
The trap: We moved fast, iterated constantly. Documentation felt like it would slow us down.
Then Boki needed to modify an agent while I worked on the orchestrator. He asked, "What format does the Matching agent expect?" I said, "Uh, let me look at the code..."
We wasted hours re-explaining decisions, rediscovering why we did things, and reverse-engineering our own system.
What we learned: In multi-agent systems, documentation isn't just helpful—it's essential. When multiple agents (and multiple developers) need to work together, shared understanding is critical.
We created:
- Agent specifications: What each agent does, expects, and returns
- Communication contracts: Data formats and semantic meanings
- Architecture diagrams: How agents relate and communicate
- Decision logs: Why we made specific design choices
Takeaway: Document as you build, not after. Each agent gets its own spec.
Lesson 8: The First Model You Pick Won't Be the Last
What happened: We started with one Claude model for all agents. It worked well for most but was overkill for simple agents and underpowered for complex reasoning tasks.
The optimization: Different model configurations for different agents:
- Simple validation tasks: Lighter, faster models
- Complex reasoning: More powerful models
- Real-time operations: Models optimized for latency
Our Match Scoring agent needed sophisticated reasoning. Our Intake agent needed fast validation. One-size-fits-all didn't work.
Takeaway: Design your system to support per-agent model configuration from day one. You'll want this flexibility as you optimize.
Lesson 9: Your Demo Will Break in New Ways
Murphy's Law of Multi-Agent Systems: No matter how much you test, the live demo will surprise you.
We ran the demo hundreds of times successfully during prep. During the actual presentation, we hit an edge case we'd never encountered. One agent returned data in a slightly different format, and downstream agents didn't know how to handle it.
Our error handling caught it, and we showed the system gracefully failing and recovering. It made for a better demo—showing real-world resilience rather than a perfect, unrealistic scenario.
Takeaway: Build in graceful degradation and recovery. Make failures informative and recoverable, not catastrophic.
Lesson 10: The Technology Is the Easy Part
The biggest surprise: Building the multi-agent system was challenging but straightforward. The hard part was everything else.
Getting stakeholder buy-in. Explaining how the system works to non-technical audiences. Managing expectations about what agents can and can't do. Addressing concerns about AI decision-making. Thinking through governance and oversight.
The technology works. The organizational and human challenges determine success or failure.
Takeaway: Plan for the human side as carefully as the technical architecture. Change management, communication, and governance are as critical as your code.
What We'd Do Differently
Starting over tomorrow, we'd:
- Start smaller: Three agents maximum for initial build
- Observability first: Build logging and tracing before complex logic
- Document earlier: Write the spec before the code
- Test realistically: Generate comprehensive test data on day one
- Plan for errors: Design error handling as part of architecture, not an afterthought
- Involve stakeholders early: Show working prototypes weekly
The Bottom Line
Multi-agent systems are incredibly rewarding. When multiple specialized agents collaborate intelligently to solve complex problems, it feels like magic.
But getting there requires embracing complexity, planning for failure, and learning from mistakes. These lessons cost us late nights, debugging sessions, and more than a few "what were we thinking?" moments.
By sharing them, we hope your journey will be smoother. You'll still make mistakes—that's unavoidable—but maybe different ones that teach new lessons.
Multi-agent systems are ready for production today. The question is: are you ready to build them?
