Building Multi-Agent Systems with LangGraph
How I reduced a weeks-long report generation process to just one hour using orchestrated AI agents.
When I joined Dokainish & Company as an AI/Software Engineer, I was tasked with an ambitious goal: automate a report generation process that typically took consultants weeks to complete. The solution? A multi-tiered agentic system built with LangGraph.
The Problem
Consulting reports are complex documents. They require gathering data from multiple sources, analyzing trends, generating insights, and presenting everything in a coherent narrative. Our team was spending 2-3 weeks on each report, and the demand was only growing.
Traditional automation wouldn't cut it. The reports required judgment calls, contextual understanding, and the ability to synthesize information in ways that simple scripts couldn't handle.
Why LangGraph?
I evaluated several frameworks for building AI agents. LangGraph stood out for several reasons:
- State Management: Built-in support for complex state machines and conversation flows
- Orchestration: Easy coordination between multiple specialized agents
- Persistence: Checkpoint and resume capabilities for long-running tasks
- Debugging: Excellent tooling for understanding agent behavior
The Architecture
I designed a three-tier agent system:
const reportGraph = new StateGraph({
// Tier 1: Data Gathering Agents
dataCollector: new DataCollectorAgent(),
webResearcher: new WebResearchAgent(),
// Tier 2: Analysis Agents
trendAnalyzer: new TrendAnalysisAgent(),
insightGenerator: new InsightAgent(),
// Tier 3: Synthesis & Output
reportWriter: new ReportWriterAgent(),
qualityChecker: new QualityAgent(),
});Each tier handles a specific responsibility, and agents within a tier can work in parallel when their tasks are independent.
Key Lessons Learned
1. Start with Clear Agent Boundaries
The biggest mistake I made early on was creating agents that were too broad. An agent that "does research and analysis" is too vague. Breaking it into "web researcher" and "trend analyzer" made each agent more reliable and easier to debug.
2. Implement Robust Error Handling
AI agents fail. APIs time out, models hallucinate, and data sources change. Building retry logic and fallback paths into the graph was essential for production reliability.
3. Human-in-the-Loop Checkpoints
For high-stakes outputs like consulting reports, I added checkpoints where humans could review and adjust before the system continued. This hybrid approach caught edge cases that pure automation would have missed.
The Results
After three months of development and iteration:
- Report generation time: 2-3 weeks → ~1 hour
- Consultant review time: 2 days → 2-3 hours
- Quality scores: Maintained at 95%+ client satisfaction
What's Next
I'm currently exploring ways to make the system more adaptive—learning from consultant feedback to improve future reports automatically. The intersection of AI agents and domain expertise is where the real magic happens.
If you're building something similar or want to discuss agentic architectures, reach out. I'm always happy to chat about this stuff.
