The AI community has been obsessed with multi-agent orchestration. We've all seen the demos: a researcher agent passes data to a writer agent, who passes it to a reviewer agent. It looks like the future.
But recent research from Google (and hard lessons from production builders) reveals an uncomfortable truth: multi-agent setups often make things worse.
Google tested 180 agent configurations across top LLMs. Their findings were a wake-up call:
- Multi-agent systems reduced performance by 70% on sequential tasks.
- Independent agents amplified errors by 17x.
The Problem with Context Handoffs
In most business applications, tasks are sequential. Step B relies entirely on Step A being accurate. When Agent A makes a slight hallucination, Agent B accepts it as fact and builds on it. Every agent you add is a new point of failure. Every handoff is where context dies.
The Solution: Keep It Simple
Instead of spinning up complex orchestration workflows, developers should ask themselves: Could a single API call with a really good prompt and rich context solve 80% of this problem? The answer is almost always yes. Complexity sells, but simplicity scales.
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