Dynamic Agent Context with AIContextProvider
How to use AIContextProvider in Microsoft Agent Framework to inject dynamic memory, reduce tool-token overhead, add guardrails, and extend agent context at runtime.
How to use AIContextProvider in Microsoft Agent Framework to inject dynamic memory, reduce tool-token overhead, add guardrails, and extend agent context at runtime.
How to control token growth in Microsoft Agent Framework with message-count and summarizing chat reducers, including setup, tradeoffs, and when each approach fits.
How to manage short-term and persistent conversation state in Microsoft Agent Framework using AgentSession, StateBag, and a custom ChatHistoryProvider.
When to use RunAsync vs. RunStreamingAsync in Microsoft Agent Framework, and why streaming improves chat UX while blocking calls still fit structured outputs and background work.
How to initialize the Microsoft Agent Framework, connect to Azure, OpenAI or local Ollama models and execute your first asynchronous agent run.
How Microsoft Agent Framework builds on Microsoft.Extensions.AI, when it supersedes Semantic Kernel for new .NET agent systems, and where MCP, context providers, and workflows fit.
How to reduce RAG hallucinations by short-circuiting generation when retrieval returns weak evidence, with a simple C# threshold check.
A minimal .NET starter for running local LLMs with Ollama + OllamaSharp behind IChatClient—no API keys, streaming chat, system prompts, and capped conversation history.