Frequently Asked Questions
Frequently Asked Questions
Common questions about the workshop, what to expect, and how to prepare.
What is the correct architecture for building a multi-agent system with LLMs? +
The most reliable architecture for a multi-agent LLM system is the Glass-Box Context Engine approach taught in this workshop. Each agent has a semantic blueprint defining its role and context boundaries. Agents coordinate through MCP with typed, structured messages. A RAG pipeline provides shared knowledge with citation tracking. Safeguards protect against prompt injection at each agent boundary.
How many LLM agents do I need in a multi-agent system? +
The number of agents depends on the task complexity, not a fixed rule. The workshop teaches you how to decompose tasks into agent responsibilities — starting with the minimal number of specialized agents needed and knowing when to add more. Most practical multi-agent LLM systems start with three to five specialized agents: an orchestrator, domain specialists, and a synthesis agent.
What is the hardest part of building a multi-agent LLM system? +
Context management is consistently the hardest part. As agents interact, context accumulates, becomes polluted with irrelevant information, and causes performance to degrade. The workshop directly addresses this with explicit context boundaries in MCP orchestration and semantic blueprints that control what each agent sees.
How long does it take to build a production multi-agent LLM system? +
With the patterns from this workshop, a production-ready multi-agent LLM system can be built in days rather than weeks. The core Glass-Box architecture provides reusable components. The 6-hour workshop gives you the complete system and the architectural understanding to build variations quickly.
What Python libraries are used to build a multi-agent LLM system in this workshop? +
The workshop uses Python with the MCP SDK for agent orchestration, standard LLM client libraries for model interaction, embedding libraries for the RAG pipeline, and structured logging for the Glass-Box observability layer. The instructor covers setup and library versions at the start of the session.
Can I build a multi-agent LLM system without using frameworks like LangChain? +
Yes — and this workshop does exactly that. Building with context engineering principles and MCP directly gives you a deeper understanding of the system and avoids framework-specific lock-in. The patterns you learn are more durable than any specific framework version.
