Frequently Asked Questions
Common Questions About Building a Production AI System
Everything you need to know before registering.
What production AI system will I build in this workshop? +
You will build a Glass-Box Context Engine: a production-grade multi-agent AI system with a semantic blueprint orchestration layer, MCP-coordinated specialist agents (retrieval, synthesis, validation, moderation), a high-fidelity RAG pipeline with citation tracking, episodic and working memory management, prompt injection safeguards and output moderation, and production deployment configuration with health monitoring and Glass-Box observability dashboards.
How is this production AI system workshop different from building a chatbot tutorial? +
A chatbot tutorial produces a demo that responds to input. This workshop produces a production system that handles real-world conditions: adversarial inputs are rejected by architectural safeguards rather than prompt instructions, context overflow is prevented by explicit memory management rather than context length limits, agent coordination is reliable through typed MCP interfaces rather than informal text passing, and system behavior is observable through Glass-Box logging rather than opaque. The difference is production-grade reliability.
How do I know the production AI system I build will actually be production-ready? +
The Glass-Box Context Engine architecture is specifically designed for production readiness. At the end of the workshop your system has: typed MCP interfaces that prevent agent communication errors, citation-grounded RAG that prevents hallucination, prompt injection detection that prevents adversarial overrides, Glass-Box logging that enables debugging and auditing, and Docker deployment configuration that makes the system reproducibly deployable. These are the properties that define production readiness for an AI system.
What is the hardest part of building a production AI system in 6 hours? +
The context routing layer is typically the most conceptually challenging: designing how the orchestrator assembles the right context package for each agent from the available sources (task state, RAG retrievals, episodic memory, inter-agent results) while respecting semantic blueprint-defined budgets and boundaries. Denis Rothman spends significant time on this module because getting context routing right is what makes the difference between a fragile demo and a reliable production system.
How do I deploy the production AI system built in this workshop? +
Deployment is covered in the final module: containerising each MCP agent server using Docker, configuring the service registry that the orchestrator uses to find agent servers, setting up the Glass-Box logging infrastructure, configuring health checks and restart policies, and establishing a CI/CD pipeline for updating components without disrupting running interactions. You leave the workshop with deployment configuration files ready to use in your own infrastructure.
Can the production AI system built in this workshop be adapted for my specific use case? +
Yes. The Glass-Box Context Engine is designed as an adaptable architecture. Adapting it to a specific use case involves: writing semantic blueprints for your domain-specific agents, populating the RAG knowledge base with your documents, implementing MCP tool servers for your domain-specific capabilities, and configuring the safeguard rules for your specific risk profile. The core orchestration, context management, and observability infrastructure transfers directly to any use case.
