Frequently Asked Questions
Common Questions About Building a Production AI Copilot
Everything you need to know before registering.
What multi-agent architecture powers the production copilot built in this workshop? +
The production copilot is built on the Glass-Box Context Engine with four specialised agents: a retrieval agent that queries the RAG knowledge base with citation tracking, a domain specialist agent that processes domain-specific queries using retrieved knowledge, a synthesis agent that assembles complete responses with full citation attribution, and a moderation agent that validates responses before delivery. The orchestrating copilot agent coordinates these four specialists through MCP and maintains session context through episodic memory.
How does the production copilot maintain context across a multi-session conversation? +
Multi-session context is maintained through the episodic memory system: at the end of each session, the memory manager compresses the session into a structured summary (key facts established, user preferences identified, decisions made, tasks completed) that is stored in the episodic memory store. At the start of each new session, the memory manager retrieves relevant episodic summaries and injects them into the copilot's context, giving the copilot appropriate continuity without replaying entire conversation histories.
How do I ensure the production copilot's responses are always grounded in verified knowledge? +
Response grounding is enforced through the citation-grounded generation pattern: the synthesis agent's semantic blueprint requires that every factual claim in the response explicitly references a retrieved source from the RAG pipeline. The moderation agent's citation coverage validator checks that all claims are cited before delivering the response. Uncited claims trigger a retry loop that sends the synthesis agent back to retrieve the missing supporting evidence or explicitly flags the claim as uncertain.
What explainability features should a production AI copilot expose to users? +
Production AI copilot explainability features include: a sources panel that lists the documents retrieved to support the response, a confidence indicator that shows the retrieval confidence for the primary sources, a reasoning summary that explains the specialist agents consulted and the key steps in the response generation, and a feedback mechanism that lets users flag inaccurate or hallucinated content for review. The Glass-Box architecture provides all the data needed to populate these explainability features.
How do I handle the production AI copilot's knowledge cutoff for time-sensitive topics? +
Knowledge cutoff handling for time-sensitive topics uses metadata filtering in the RAG retrieval layer: each retrieved document includes a timestamp, and the synthesis agent's semantic blueprint instructs it to acknowledge when the most relevant sources are older than a defined freshness threshold. For topics where recency is critical, the copilot can be configured to invoke external data sources through MCP-connected tool servers that fetch current information, while the RAG pipeline handles historical and reference knowledge.
How do I measure and improve the production AI copilot's quality over time? +
Production copilot quality improvement uses the Glass-Box data as the primary feedback source: citation coverage metrics reveal knowledge base gaps, safeguard trigger rates identify emerging adversarial patterns, user correction rates (when users explicitly correct or reject copilot responses) indicate quality failures, and session abandonment patterns correlate with specific query types that the copilot handles poorly. Each quality metric connects to a specific improvement action in the context engineering architecture.
