AI agents don’t fail because the model is wrong. They fail because everything between the model and the real world is held together with duct tape.
In high-stakes environments, like financial services, that’s not an option.
Join Redis and Arcade and learn how to build enterprise-ready AI agents using a loan servicing reference app. We’ll show you how to combine a purpose-built MCP runtime with production-grade context management so your agents are secure, accurate, and performant from day one.
No theory. No vague frameworks. Just a clear, repeatable blueprint for building agents that work in the real world.
Arcade is the runtime for AI agents in production, handling agent authorization, policy enforcement, and audit for every tool call. Redis powers shared agent memory and coordination across sessions and optimized tool use with caching. Together, you get an architecture pattern you can ship with confidence in regulated environments.
We’ll break down these core components:
Security: Govern MCP tool execution with built-in auth, policy enforcement, and auditability
Quality: Deploy agent-optimized tools purpose-built for reliability and token efficiency
Accuracy: Design shared agent memory, session coordination, and intelligent caching
Hear directly from Arcade’s co-founder and Redis’ AI field leader, as we break down the architecture that makes secure AI agents deliver real enterprise value.
Speakers
Arcade.dev
Sam Partee
Co-founder
Redis
Tyler Hutcherson
Applied AI Engineering
RSVP
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