Frequently Asked Questions
Common Questions About Transparent and Explainable AI Agents
Everything you need to know before registering.
What does transparency mean for an AI agent in production? +
Transparency for a production AI agent means that for any output the system produces, you can trace exactly: which knowledge sources the agent used (RAG citations), what instructions guided its reasoning (the semantic blueprint), which other agents contributed to the output (MCP orchestration log), which safeguards were evaluated and what they found (safeguard audit trail), and what output validation was applied before delivery (validation log). This complete traceability is what the Glass-Box architecture provides.
What is explainability in AI agents and how does the Glass-Box architecture achieve it? +
Explainability means being able to produce a human-readable explanation of why the AI agent produced a specific output. The Glass-Box architecture achieves explainability by maintaining structured logs of every decision that can be rendered into natural language explanations: 'This response was generated using the following retrieved sources, guided by this semantic blueprint, in coordination with these other agents, after passing these validation checks.' The workshop covers generating these explanations from Glass-Box log data.
How does the Glass-Box architecture differ from post-hoc explainability techniques? +
Post-hoc explainability techniques like LIME and SHAP attempt to explain a model's decision after the fact by approximating the model's behavior. The Glass-Box architecture captures the actual decision process as it happens: the real context that was provided to the agent, the real retrieval results that informed the response, the real coordination steps that assembled the final output. This in-process transparency is more accurate and more actionable than post-hoc approximation techniques.
Can I make an existing black-box AI agent transparent without rebuilding it? +
Partially. You can add Glass-Box logging to the inputs and outputs of an existing agent to capture what it received and what it produced. However, the internal decision process of a black-box agent (which retrieved documents it used, how it weighted different context sources) cannot be made transparent without architectural changes. The workshop covers the incremental path from partially transparent logging to fully transparent Glass-Box architecture for existing systems.
How does Glass-Box transparency help users trust AI agent outputs? +
Glass-Box transparency builds user trust by making AI agent outputs verifiable: users can see which specific sources were cited for each claim, can verify that those sources support the claim, and can understand the reasoning process that produced the response. This verifiability transforms AI from an opaque oracle that must be trusted on faith to an accountable system whose outputs can be checked and challenged. The workshop covers how to surface Glass-Box transparency information in user-facing interfaces.
What Glass-Box transparency features are most important for enterprise AI deployments? +
Enterprise AI deployments prioritize four Glass-Box transparency features: audit trail completeness (every interaction is logged with sufficient detail for compliance review), knowledge source traceability (every factual claim can be traced to a specific retrieved source), access control logging (which users and agents accessed which knowledge resources), and safeguard evaluation records (what content moderation and safety checks were applied to each output). The workshop covers implementing all four for enterprise production deployments.
