Memos

Production agents fail silently — not from bad prompts, but from bad context structure. Here are the 7 failure modes that context graphs eliminate before they compound.

Gartner's 2026 predictions for data and analytics describe an autonomous agent future. Every prediction points to the same architectural gap: agents need context graphs to make reliable decisions at scale.

The bottleneck isn't model capability — it's context. Without a structured context layer, data agents fail because enterprise data is messy and undocumented.

Most AI agent evaluation frameworks test wrong things. Discover 5 structural gaps between passing evals and production-ready agents, and how to fix them.

A practical atlas of 12 recurring AI agent failure patterns in production, with root causes, detection signals, and architecture fixes.

AI agent testing in production reveals structural failures that staging environments can't catch. Learn the 4 testing patterns that undermine reliability.

Discover why traditional monitoring fails AI agents in production. 5 structural patterns expose how teams miss decision failures while dashboards stay green.

RAG improves recall but does not govern decisions. For production agents that take action, the gap between retrieval and reliability is structural.

Session memory, vector stores, and thread-based memory all degrade under production load. The problem isn't storage — it's structure.

The next wave of AI failures won't come from the models — it comes from state drift.

A knowledge graph maps reality. A context graph governs decisions.

The missing infrastructure for reliable AI agents.