Your agents aren't failing. Their context is.
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Most enterprise AI teams have hit an AI ceiling, not because models aren't capable, but because their context layer is broken. According to our research, 80% of AI projects stall due to data, governance, or infrastructure gaps.
The Context Engineering Maturity Model introduces a diagnostic framework that shows where your organization stands today in terms of delivering vital real-time context to agents, what's holding agents back, and the architectural steps to build a production-ready real-time context engine. You’ll get:
• The four failure modes that stall enterprise AI deployments
• The four pillars of a production-ready context engine
• A maturity model to assess your organization's current stage
• A prioritized roadmap to advance to the next level
Agent capability is doubling every nine months; context infrastructure is the new bottleneck.
The four failure modes: Fragmentation, opacity, speed degradation, and non-accumulation are diagnosable and fixable.
A context system that accumulates memory compounds in value over time in ways that are difficult to replicate from scratch.
Teams that adopt open standards like MCP now will avoid costly re-platforming as the AI landscape evolves.
Learn more about our real-time context engine, Redis Iris.
