Guide
Context architecture for production AI agents
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What you'll learn
Most agent failures in production aren't model problems — they're context problems. This playbook breaks down why your current architecture is likely the bottleneck, and gives you a concrete framework for fixing it.
• Why context architecture, not model quality, determines whether your agents succeed in production
• The four pillars every production-grade context system must satisfy: Navigate, retrieve, improve, and accelerate
• How to go from fragile, stitched-together context layers to a unified semantic and access layer your agents can actually rely on
Key highlights:
A clear diagnosis of why naive approaches — Text2SQL, REST-to-MCP converters, one-shot RAG — break at scale
A reference architecture mapping each pillar to production-ready component
A 12-question self-audit checklist your team can run against your current stack today
An opinionated point of view on where to start if you're inheriting a context architecture that was stitched together quickly
Give your agents the context they need.
RedisVL delivers real-time semantic search so your agents retrieve the right information at the speed users actually expect.