Frequently Asked Questions
Common Questions About Fixing AI Agents That Fail at Scale
Everything you need to know before registering.
At what point do AI agents typically start failing at scale? +
AI agents typically show the first scaling failures when conversation length grows beyond a few turns (context rot), when two or more agents share state without explicit boundaries (context pollution), or when the task requires coordination between more than two agents (coordination complexity). The context engineering patterns in this workshop address all three inflection points before they become production failures.
How does context engineering make AI agents scale? +
Context engineering makes AI agents scale through three mechanisms: semantic blueprints that keep each agent focused on its specific domain regardless of system complexity, MCP-typed communication that prevents the informal coupling that breaks at scale, and the Glass-Box observability that makes system behavior visible at any scale so problems can be detected and fixed before they cascade.
What happens to RAG pipelines when AI agent systems scale? +
RAG pipelines under scale face concurrent access from multiple agents, growing knowledge bases with increasing retrieval latency, and citation chains that become complex as multiple agents retrieve and reference the same documents. The workshop covers RAG architecture patterns that handle these scale challenges: connection pooling, retrieval caching, and distributed citation tracking.
How do I test AI agent behavior at scale before production deployment? +
The workshop covers load testing strategies for multi-agent AI systems including simulating high-concurrency agent invocations, generating adversarial edge case inputs at scale, monitoring context window utilization under load, and measuring citation coverage degradation as retrieval volume increases. The Glass-Box logging makes scale test analysis practical by capturing detailed metrics automatically.
How many agents can be orchestrated reliably with the patterns from this workshop? +
The MCP orchestration patterns taught in this workshop are designed to scale to complex multi-agent systems with many specialised agents. The practical limit depends on your infrastructure capacity and context management strategy. The workshop teaches the architectural patterns that maintain reliability as agent count grows, rather than fixing a specific numerical limit.
What infrastructure do I need to run AI agents at scale? +
The workshop focuses on architectural patterns rather than specific infrastructure requirements. The key infrastructure considerations for scaled AI agents are compute for LLM inference, memory for embedding and RAG retrieval, storage for Glass-Box logs and episodic memory, and network for MCP agent-to-agent communication. The instructor covers these requirements and scaling patterns during the production deployment module.
