Abstract
We demonstrate that allowing context to bleed between specialist agents in a high-stakes AI pipeline introduces compounding error rates across task types. In a controlled experiment comparing isolated vs. shared-context multi-agent architectures, shared state increased hallucination rates by 3.4× in contract extraction tasks and 2.8× in legal research tasks.
Methodology
We built two parallel multi-agent systems — one with strict context isolation between agents and one with shared state — and evaluated them against a test set of 500 documents with known ground truth. Each system used the same base model and agent definitions; only the context architecture varied.
Conclusion
Context isolation is not merely a safety preference — it is a measurable accuracy requirement for multi-agent systems in high-stakes domains. We recommend that production AI systems enforce strict agent-level context boundaries as a core architectural principle.