Multi-Agent Orchestration: When One Model Isn't Enough
We reached for multi-agent orchestration on a document-processing pipeline for a legal tech client after a single large prompt started failing on complex contracts — too many instructions competing for attention in one context window, and the model would quietly skip steps under load. Splitting the task into a router, an extraction agent, and a validation agent fixed the skipping problem almost immediately.
The trade-off nobody warns you about is debugging complexity. A single-agent failure is one prompt to inspect. A five-agent pipeline failure means tracing through multiple handoffs to find where the breakdown happened, and early on we lost real time to failures that were actually just one agent passing malformed output to the next without any validation in between.
We now treat every agent-to-agent handoff as a contract, with a defined schema and validation at the boundary, not a shared vague understanding of "what the next agent expects." Adding JSON schema validation between the extraction and validation agents on that legal contract pipeline caught roughly 15% of runs that would otherwise have silently passed bad data downstream.
Cost and latency scale with agent count in ways that surprise people — three sequential LLM calls isn't three times the cost of one, because each agent needs enough context to do its job well, and that context often duplicates across agents. We moved from a fully sequential pipeline to a mix of parallel and sequential steps where possible, cutting end-to-end latency by about 35% without changing what each agent actually did.
Our rule of thumb now: don't split into multiple agents until you've confirmed a single well-structured prompt genuinely can't hold the task, not because it sounds more sophisticated in an architecture diagram. Most tasks we're asked to "multi-agent" turn out to need better prompting and structured output first.
