Frequently Asked Questions
Frequently Asked Questions
Common questions about the workshop, what to expect, and how to prepare.
Why are LLM agents unreliable in production? +
LLM agents fail reliably in production for four main reasons: context pollution (irrelevant information accumulating in the context window), context rot (performance degrading as context grows), hallucination without accountability (no mechanism to verify claims against sources), and coordination failures (agents working from contradictory assumptions). Context engineering addresses all four with structural solutions rather than prompt tweaks.
What is the most effective technique for making LLM agents reliable? +
The most effective reliability technique is explicit context management — controlling precisely what information each agent receives rather than letting context accumulate organically. This means semantic blueprints for structured agent instructions, MCP for typed context passing between agents, and RAG for knowledge retrieval with citation verification. The Glass-Box architecture makes this system observable so you can measure and improve reliability.
How do semantic blueprints make LLM agents more reliable? +
Semantic blueprints replace open-ended prompt text with structured specifications that define an agent's goal, relevant context, constraints, output format, and available tools. This structure reduces the interpretive variability that causes unreliable behavior — the agent has less room to misinterpret its task and more explicit guidance about what constitutes a valid response.
How do I prevent LLM agent hallucination in production? +
The workshop covers several hallucination prevention approaches: RAG with citation verification that grounds claims in retrieved sources, output validation that checks responses against known constraints, the Glass-Box observability layer that makes hallucination events visible for analysis, and MCP-structured tool use that replaces model knowledge with verified data sources where reliability is critical.
How do I test whether my LLM agents are reliable before deploying to production? +
The workshop covers a testing framework for LLM agent reliability including: component tests for individual agent behavior, integration tests for multi-agent coordination, adversarial tests with injection attempts and edge cases, and regression tests that verify reliability does not degrade as the system evolves. The Glass-Box architecture makes testing easier by making agent behavior observable.
Can I make existing LLM agents more reliable without rebuilding them? +
Yes. The workshop covers both building reliable agents from scratch and incrementally improving existing systems. Common improvements that can be applied without full rebuilds include adding semantic blueprints to existing prompts, introducing MCP for agent coordination, and adding a lightweight Glass-Box logging layer for observability.
