Frequently Asked Questions
Common Questions About Fixing Unreliable LLM Agents in Production
Everything you need to know before registering.
My LLM agents work in testing but fail in production — why? +
Production environments expose failure modes that testing misses: longer conversation histories that overflow context windows, adversarial inputs that exploit prompt injection vulnerabilities, edge cases in user requests that agents misinterpret, and concurrent requests that cause shared state conflicts. Context engineering addresses all of these systematically rather than patching individual failures.
What is the fastest way to improve unreliable LLM agent production behavior? +
The highest-impact improvement is adding semantic blueprints to your agents: replacing unstructured prompts with explicit role definitions, knowledge boundaries, output format specifications, and task constraints. This single change significantly reduces interpretive variability that causes unreliable behavior. The workshop covers retrofitting semantic blueprints to existing agents as well as building new ones correctly.
How do I diagnose why my LLM agent is unreliable in production? +
Without observability you are guessing. The Glass-Box logging layer provides systematic diagnosis: every agent input, context window contents, reasoning steps, and output are logged with structured metadata. This lets you identify the specific context states and input patterns that trigger unreliable behavior, rather than discovering failures after users are affected.
How many production failures can context engineering prevent? +
Context engineering directly addresses the most common LLM agent production failure categories: context overflow (explicit context management), hallucination (citation-grounded RAG), coordination failures (MCP with typed interfaces), prompt injection (input validation safeguards), and agent role confusion (semantic blueprints). These categories account for the large majority of LLM agent production failures.
Should I rebuild my unreliable LLM agents from scratch? +
Not necessarily. The workshop covers both incremental improvement patterns and full rebuilds. Many unreliable agents can be significantly improved by adding semantic blueprints, introducing MCP for coordination, and adding the Glass-Box observability layer without a complete rewrite. The decision depends on how deeply the reliability problems are embedded in the current architecture.
What is the ROI of fixing LLM agent reliability with context engineering? +
Reliable LLM agents reduce the cost of human review and correction, enable deployment to higher-stakes use cases, reduce the frequency of production incidents, and allow faster iteration because the system behavior is predictable. The investment in context engineering architecture typically pays back quickly in reduced operational overhead and expanded deployment confidence.
