Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
Indirect prompt injection in client-facing retrieval-augmented generation (RAG) pipelines is a real production threat. It arises not from a visible prompt, but from contaminated data, memory, and tool signals that subtly steer model behavior.
Detecting AI errors in production is not about chasing edge cases; it is about building a reliable AI fabric that can be observed, measured, and controlled.
Marketing data environments are increasingly complex, with signals streaming from ad platforms, CRM systems, attribution models, and website analytics.
Retrieval augmented generation (RAG) systems can produce confident-sounding responses that are not grounded in retrieved sources.
Harmful goal drift in AI agents is a production risk where agents pursue objectives that diverge from intended outcomes.
Production AI drift is not hypothetical. In systems that rely on retrieval augmented generation and autonomous agents, drift across data, knowledge sources, and policies can erode accuracy, trust, and safety.
Political bias in AI can distort policy recommendations, skew political content, or influence public discourse when deployed at scale.
Prompt injection is a real threat in production AI workflows. When agents orchestrate tools, access data, and operate across trust boundaries, crafted prompts can nudge decisions, reveal sensitive data, or bypass policy constraints.
Shadow AI agents are an emerging risk for modern enterprises. They operate outside formal approval, bypass standard governance, and can affect data privacy, security posture, and regulatory compliance.