Supercharging AI Agents with Memory on Azure Managed Redis

Co-authored by Purna Mehta, AMR Product Manager, Redis, Inc. and Han Xing Choong, Software Engineer, Redis, Inc.

 

AI agents are quickly evolving from simple chatbots to sophisticated, context-aware systems that reason, orchestrate tools, and learn over time. Memory is at the heart of that transformation. Azure Managed Redis - Microsoft’s first-party service based on Redis Enterprise, can make agent memory management both seamless and production-ready in the Azure ecosystem.

Memory capability enables AI agents to retain the context of interactions with users and systems. By continuously capturing preferences, operational patterns, and unique requirements, agents develop a comprehensive understanding over time. With the addition of vector store capabilities, Azure Managed Redis allows AI agents to efficiently store, index, and search high-dimensional data like embeddings, supporting advanced semantic search for more accurate, real-time responses. This is where Azure Managed Redis can help accelerate building agentic AI apps using Microsoft Agent Framework.

What is Microsoft Agent Framework

Microsoft Agent Framework  is an open-source SDK and runtime designed to let developers build, deploy, and manage sophisticated multi-agent systems with ease. It unifies the enterprise-ready foundations of Semantic Kernel with the innovative orchestration of Autogen.

Understanding agent memory architecture

An agent’s usefulness depends on how and what it remembers. A typical agentic memory architecture contains two key components:

Working short-term memory

A play-by-play of the current interaction, so the agent can track ongoing conversation history and state without asking you to repeat yourself. Microsoft Agent Framework can help manage this short-term or working memory tied to a user’s session. In the Agent Framework SDK, it is implemented as Thread with an associated ChatMessageStore that can be offloaded to Redis.

Durable long-term Memory

Preferences, behavioral patterns, durable facts and interests pieced together from many interactions so the agent can learn and adapt over time. Long-term memory is typically shared across sessions. Within Microsoft Agent Framework SDK, long-term memory is provided through ContextProviders.

Azure Managed Redis can be integrated directly as the context provider to maximize performance, control and advanced features. One of the popular context providers is powered by Mem0. Mem0 handles intelligent memory extraction, deduplication, and contextual grounding while recording these memories in Azure Managed Redis.

Memory in action - travel agent example

To show this in action, we have created a sample agent – an AI travel concierge – which provides time-aware information to generate trip itineraries personalized based on preferences. To achieve this goal, it relies on LLM, external tools, and manages user-specific context with a dual-memory layer.  

This layered memory design means a conversation isn’t a single exchange – it’s a long-running interaction. Agents can draw on past facts and preferences to shape current responses, creating continuity and building toward complex outcomes over time. Below is the agent architecture from the Travel Agent Sample application on GitHub built with Microsoft Agent Framework.

Architecture of a sample agent – an AI travel concierge

How Azure Managed Redis fits this architecture

When building an agent using Microsoft Agent Framework and Azure Managed Redis – these are some of the key attributes for production-grade internet scale applications on Azure:

1. Latest innovation: Azure Managed Redis is a first-party service, building on Redis Enterprise capabilities. Azure Managed Redis brings multiple data types, fast vector similarity search and semantic caching (that helps you save tokens), full JSON support, high throughput, and low latency out of the box.
   
   
2. Reliability and SLAs: Azure Managed Redis offers high availability up to 99.999%. Offers active-active geo-replication and persistent storage, so memory doesn’t disappear between sessions, and agents can scale globally.
   
   
3. Enterprise ready: As a first-party product, Azure Managed Redis integrates with Azure Entra ID for authentication, Azure networking, and security – like Private Endpoints, TLS and encryption at rest - that are available and manageable through Azure Managed Redis in Azure portal.
   
   

Azure Managed Redis brings the performance, simplicity, and scalability of Redis Enterprise directly into the Azure ecosystem, making it easier to build intelligent, stateful agents. With seamless integration into your Azure environment, you can rely on fully managed Redis to power agent memory, and context management. Azure Managed Redis also delivers sub-millisecond lookups that enable real-time agent interactions or intelligent systems that need near real-time responses like recommendation systems, text to speech or speech to speech agents.

Next steps

-          Explore the Travel Agent Sample application on GitHub

-          Check the Azure Managed Redis Documentation

-          Deep dive into Microsoft Agent Framework