skeske shares insights into creating Lacuna—an agent built with Copilot Studio and Azure AI Foundry—designed to surface hidden assumptions in product decisions and foster reflection within engineering teams.

The Future of AI: Developing Lacuna – An Agent for Revealing Quiet Assumptions in Product Design

Author: skeske

Introduction

The “Future of AI” blog series, by the AI Futures team and Microsoft experts, highlights next-generation AI tools and their real-world applications. This installment profiles Lacuna, a conversational agent created to illuminate subconscious assumptions within product teams.

The Challenge of Hidden Assumptions

Every product artifact—from Jira tickets to product requirement documents—embodies assumptions, many of which go unquestioned. These subtle beliefs can shape what teams define as valuable or intuitive, but left unchecked, they risk steering development based on bias rather than evidence.

The Solution: Lacuna

As a UX Engineer on Microsoft’s Azure AI Foundry design team, the author built Lacuna using Copilot Studio and Azure AI Foundry. The agent analyzes product documents uploaded through Microsoft Teams. By leveraging LLM technology, Lacuna identifies assumptions, both explicit and implicit, by examining language cues such as speculative verbs, declarations, and vague terms.

Methodology

Lacuna applies design risk analysis through three key lenses:

  • Impact: What are the consequences if this assumption fails?
  • Confidence: How much supporting evidence is presented?
  • Reversibility: How difficult is it to change course if needed?

Output and Guidance

For each assumption uncovered, Lacuna provides a risk ranking and suggests lightweight mitigation approaches (e.g., A/B tests, interviews, telemetry analysis) to help product teams validate or challenge their beliefs.

AI Tools Used

  • Copilot Studio for authoring custom prompts and building the conversational interface
  • Azure AI Foundry for AI model services and integration
  • Microsoft Teams as deployment surface for cross-team collaboration

Intended Impact

Lacuna is designed not as a critic but as a collaborator, prompting reflection and awareness around cognitive bias in product development. Its goal is to help teams prioritize the most impactful ways to mitigate risk, not to replace human judgement but to augment it.

Getting Started

Conclusion

Lacuna demonstrates the evolving power of Microsoft AI tools in fostering epistemic humility and challenging assumptions in technical teams. This experiment in agentic collaboration illustrates how LLM-powered solutions can advance reflective engineering practices.

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