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

What's new in agent-framework-mlx: Tool Calling, Agent Framework 1.12.1 and more

Thu, 30 Jul 2026 08:00:00 +0000

Back in December I introduced the MLX Integration Library for Agent Framework, a small library that lets you plug local MLX models into Agent Framework applications as a regular chat client, sitting right next to your cloud-backed agents. Agent Framework has been moving quickly since then, and rather than letting the library drift behind, I spent some time catching up and, in the process, added one of the features people kept asking me about: tool calling.

PII guardrails for .NET applications - Part 2: Agent Framework agents

Fri, 26 Jun 2026 08:00:00 +0000

In part one of this little series I introduced TasmanianDevil, a standalone, offline PII detection and de-identification engine for .NET. We saw it on its own - detecting and validating PII, anonymizing it with a range of operators, the reversible encrypt/decrypt round-trip, structured JSON and CSV redaction, and the optional multilingual NER add-on.

That engine is useful anywhere, but the place I built it for is AI agents. In this part I will wire it into Microsoft Agent Framework (MAF) agents through AgentGuard, so PII is handled automatically around the agent - on the way in, on the way out, and in the results that come back from tool calls.

Chain of Agents: Collaboration between local and remote language models with Agent Framework

Thu, 21 May 2026 07:06:14 +0000

This post continues a series on hybrid architectures that combine local Small Language Models (SLMs) with cloud Large Language Models (LLMs). We have already looked at the Minions pattern, which offloads bulk text extraction to a local model to reduce cloud API costs, and the SLM-default, LLM-fallback pattern, where a local model handles the majority of queries and only escalates to the cloud when confidence is low. Today we look at a third pattern: Chain of Agents (CoA), introduced in the paper “Chain of Agents: Large Language Models Collaborating on Long-Context Tasks” by Wang et al. (2024). Where the previous patterns were primarily motivated by cost, CoA is motivated by a different challenge - what happens when your document is simply too long to fit into any model’s context window?

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).