Multi-agent systems: the complexity goes somewhere
NarrativeFlow is the most architecturally ambitious thing I've built. Multiple OpenAI agents orchestrated through LangChain, each one responsible for a different part of a B2B go-to-market system — brand narrative generation, email sequence creation, campaign scoring. They share context, hand off results, and in theory produce better output than any single prompt would.
In practice: it works, and the failure modes are just harder to find.
What multi-agent gets you
The genuine win is specialization with shared context. An agent that only generates brand narratives gets a tighter, more focused prompt than one doing five things at once. When I'm debugging why the email sequence sounds off, I can isolate which agent in the chain produced the problem. Single-agent systems give you one place to look. Multi-agent systems give you the right place to look.
The other win is parallelism. When I need brand scoring AND email drafts AND campaign analysis, running them in sequence is slow. Running them concurrently with a shared context payload is fast.
What it costs you
Debugging a multi-agent failure means tracing through the full chain to figure out where the context degraded. If agent #1 summarizes the brand narrative slightly wrong, agent #3's email will be subtly off in a way that's hard to pin to anything. The error propagates quietly.
I also underestimated how much time I'd spend on the wiring — the scaffolding that routes inputs and outputs between agents, handles retries, and ensures one agent's failure doesn't silently corrupt the next one's input. That code isn't glamorous, it's not the part anyone thinks about when they talk about multi-agent systems, and it took longer than any of the actual agent logic.
The lesson: multi-agent is the right answer when the problem is genuinely modular. When it's not — when you're really just adding complexity to feel like you're doing something sophisticated — a single well-prompted agent is faster to build and easier to maintain.