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.
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Controlling Token Growth with Chat Reducers
How to control token growth in Microsoft Agent Framework with message-count and summarizing chat reducers, including setup, tradeoffs, and when each approach fits.
State Management and Chat History
How to manage short-term and persistent conversation state in Microsoft Agent Framework using AgentSession, StateBag, and a custom ChatHistoryProvider.
Use the Aspire Dashboard Standalone
Stop setting up complex observability stacks for local dev. Learn how to run the Aspire Dashboard as a standalone, lightweight OTLP viewer for any language.
Chat vs. Streaming: Don't Keep Your Users Waiting
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.
Zero to First Agent
How to initialize the Microsoft Agent Framework, connect to Azure, OpenAI or local Ollama models and execute your first asynchronous agent run.
Microsoft Agent Framework: Introduction
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.
Context Compression in .NET
Reduce token cost and latency in .NET by compressing RAG context with a cheap summarizer model or an IChatClient middleware pipeline.
Debugging LLM Timeouts in .NET
A repeatable local setup for timeout triage in .NET LLM workloads using Aspire, OpenTelemetry, and Ollama.
Local LLMs in .NET
A minimal .NET starter for running local LLMs with Ollama + OllamaSharp behind IChatClient—no API keys, streaming chat, system prompts, and capped conversation history.