Why Your AI Agent Keeps Hallucinating Your Data: Building Deterministic Context Layers

AI agents are increasingly deployed in production, yet they frequently fail, not because of model limitations, but because of unreliable context. The data feeding these agents is often tied to specific datasets or infrastructure, leading to brittle pipelines and unpredictable behavior.

In this talk, I’ll introduce the concept of deterministic context layers, an abstraction that separates what you compute from how you compute it. Using mloda, an open-source Python framework, I’ll demonstrate how plugin-based architecture enables:

• Shareable data transformations across teams
• Seamless switching between dev and production data sources
• Built-in validation and audit trails from source to agent

We’ll analyze why coding agents (Cursor, Claude Code) handle context better than enterprise AI systems, and apply those lessons to your deployments. Expect a live demo showing an agent requesting PII-redacted data with automatic source resolution.