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Dynamic Context Injection with Custom Context Providers
The Challenge
Building AI agents that respond to user-specific data often requires injecting dynamic context into conversations. While you can embed context directly in system prompts, managing this becomes challenging in multi-turn conversations where user profile information needs to be consistently available throughout the interaction. Template-based system prompts alone aren't flexible enough to handle these dynamic scenarios effectively.
The Solution: Custom Context Providers
The Microsoft Agent Framework (MAF) offers a powerful solution through custom context providers. These providers enable you to inject context dynamically during each agent invocation, ensuring that your AI agent always has access to the most relevant, up-to-date user information.
Example: Personalized Song Recommendations
Our sample demonstrates this approach by creating an agent that generates personalized song recommendations based on user demographics. The implementation showcases two custom context providers—UserAgeProvider and UserGenderProvider—that work together to provide a complete user profile.
Key Features Illustrated
The code example highlights several important capabilities:
- Multiple Context Providers: Both UserAgeProvider and UserGenderProvider independently fetch user-specific information and inject it into the agent's context
- Automatic Context Aggregation: The framework seamlessly combines context from multiple providers using an AggregateContextProvider behind the scenes
- Lifecycle Hooks: The invoking function prepares context before each invocation, while the invoked function allows post-processing and inspection
This pattern scales elegantly—whether you need to inject two pieces of context or twenty, the framework handles the complexity of merging and managing multiple providers automatically.
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