I’ve been experimenting with Microsoft‘s new Agent Framework (MAF) – but instead of connecting to cloud systems, I’ve been running it entirely offline on an Amazon EC2, private cloud, instance. My goal was to see whether this new, unified framework could function offline, be used with offline LLM’s and process PDFs (of RFPs in this case), extract questions, and even draft answers – all without leaving a secure, private environment.
It worked remarkably well. But what’s even more interesting is what this means for organizations on multiple fronts: the ability to run sophisticated Agent workflows locally, maintain full control of data, and start automating complex knowledge tasks such as RFP responses, compliance checks, or policy reviews.
What is it?
The Microsoft Agent Framework (MAF) represents a major step forward in Microsoft’s approach to agent development (on the developer friendly end of their Agent spectrum). It combines the best of two other Microsoft technologies earlier technologies, Semantic Kernel and AutoGen, into a single, unified framework – from a business perspective it helps make the decision on which framework to adopt much easier (or at least it will when MAF becomes generally available).
In my setup (it’s a technical list):
- Ollama (qwen3 8b) served as the local LLM, running without API keys or Internet connectivity.
- Docling MCP Server handled PDF-to-Markdown conversion for RFP parsing.
- A MAF agent orchestrated the workflow between these components.
- Everything ran within an Ubuntu EC2 instance hosted on Amazon – but the same architecture can run on-premise, self-contained within private networks.
This architecture creates a self-contained AI environment capable of reading and analysing RFP documents, extracting structured questions, and drafting initial answers. It’s effectively an AI knowledge assistant operating entirely inside your organization’s trusted infrastructure — with no dependency on external APIs or cloud data exchange.
What does it mean from a business perspective?
For business and technology leaders, the implications of this kind of deployment are significant:
- Enhanced Data Security and Privacy: Sensitive or confidential documents (like RFPs and the responses, contracts, or internal reports) never leave your controlled environment. This reduces data-sovereignty and privacy-breach risk, a major factor for some organizations.
- Regulatory and Compliance Alignment: Running locally simplifies compliance with frameworks because the configuration is under your control. The AI stack can be logged, versioned, and is auditable.
- Unified Microsoft AI Ecosystem: MAF consolidates what used to be a split toolkit. Teams no longer need to choose between Semantic Kernel and AutoGen; MAF combines the best of both – meaning consistent architecture, simpler maintenance, and faster innovation cycles.
- Hybrid and Scalable Architecture: Businesses can start small – running MAF locally for sensitive workloads – and scale up with Azure integrations when they’re ready. This hybrid model fits real-world enterprise adoption curves.
- Foundation for Strategic AI Adoption: Beyond RFPs (which was just a test case), the same architecture can support other activities such document analysis and reviews, grant writing, or knowledge extraction.
What do I do with it?
Here are practical next steps for organizations exploring this approach:
- Identify High-Effort, Document-Heavy Processes: Start where time and compliance pressures are greatest (and are low risk to start with) – RFPs, audits, policies, or procurement workflows.
- Evaluate Infrastructure Options: Decide whether local servers, private cloud (e.g., Azure Stack, AWS EC2, GCP), or hybrid setups best match your data governance needs.
- Pilot with Open Models, MAF and MCP: Experiment with Ollama or other open models under MAF to validate use cases without exposing data to public endpoints.
- Establish AI Governance Early: Define how data is handled, logged, and reviewed. Even local AI systems benefit from lightweight governance structures that set boundaries and expectations.
- Scale Strategically: Once validated, expand to additional departments or use cases, layering RAG (Retrieval-Augmented Generation) for better precision and domain grounding.
The emergence of Microsoft’s Agent Framework (MAF) marks a turning point for practical, locally hosted, developer friendly enterprise AI Agents. It unifies Microsoft’s agent tools, supports local deployment (which according to Microsoft is going to get better with direct Ollama support), and makes AI autonomy and data control achievable. For organizations cautious about cloud exposure but eager to capture AI efficiency (and fully in the Microsoft ecosystem), this is a compelling path forward.
If your business handles sensitive information or manages complex, repetitive document workflows, it’s time to explore what a local-first, MAF driven, GenAI strategy could do for you. The technology is catching up with the governance and risk expectations (especially when MAF becomes generally available) that enterprises demand – and it’s almost ready to be put to work.