AI agents don’t fail because the model is wrong. They fail because the harness is sloppy.
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 governed MCP tool execution 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 handles authentication, policy enforcement, and audit for MCP tool calls. 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: Enforce governed MCP tool execution with built-in auth, policy, and audit
Performance: Optimize repeatable tool calls with intelligent caching
Accuracy: Design shared agent memory across sessions
Hear directly from Arcade’s co-founder and Redis’ AI field leader, both serving production financial service customers, as we break down the architecture that makes secure AI agents actually provide enterprise value.
Speakers
Arcade.dev
Sam Partee
Co-founder
Redis
Tyler Hutcherson
Applied AI Engineering
RSVP
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