Agent-Framework on Strathweb. A free flowing tech monologue.

Introducing AgentGuard - declarative guardrails for .NET AI agents

Tue, 24 Mar 2026 07:00:00 +0000

As AI agents become more common in .NET applications, the question of how to keep them safe and well-behaved keeps coming up. Prompt injection, PII leakage, topic drift, tool call abuse - these are all problems that every team building with agents ends up having to deal with, often by hand-rolling ad-hoc checks. Python developers have had libraries like NeMo Guardrails and Guardrails AI to help with this for a while now, but the .NET side has been largely left to fend for itself.

Today I would like to introduce AgentGuard, a library I have been working on to fill that gap - composable, declarative guardrails and safety controls for .NET AI agents.

Introducing the MLX Integration Library for Agent Framework

Thu, 11 Dec 2025 08:00:00 +0000

I’ve recently been working on setting up a bunch of Agent Framework samples, which would showcase the cooperation between cloud agents (backed by LLMs in the cloud) and local agents (running on your own machine). Since I primarily work on a Mac, the natural choice for me was to use MLX as the local model runner, which required a bit of bootstrapping - and felt quite tedious. So, the natural next step was to create a library that would make it easy to integrate MLX models into Agent Framework applications, since there wasn’t one available yet.

Today, I’m excited to announce the release of the MLX Integration Library for Agent Framework! This library simplifies the process of integrating MLX models into your Agent Framework applications, allowing you to leverage local Mac AI capabilities seamlessly alongside cloud-based agents.

SLM-default, LLM-fallback pattern with Agent Framework and Azure AI Foundry

Fri, 05 Dec 2025 08:00:00 +0000

When building AI workflows, we often face a choice: do we use a massive, expensive cloud model for everything (to ensure best reasoning capabilities), or do we cut costs with a smaller local model (and risk hallucinations)? In this post, we’ll explore a “best of both worlds” architecture, as described in the recent survey “Small Language Models for Agentic Systems” Sharma & Mehta, 2025.

We call this the “SLM-default, LLM-fallback” pattern. The premise is simple: route all queries to a fast, private, on-device Small Language Model (SLM) first. Only if that model cannot confidently answer the query, do we escalate the request to a paid cloud model (LLM).