How AI Changed Software Development Forever — Building the Agentic Future with Andre Baltieri [MVP]

Artificial intelligence is changing software development at a speed we have rarely seen before.Developers have already moved from writing every line of code themselves to working with AI assistants that can generate code, explain unfamiliar systems, create tests, debug applications, and automate repetitive work.But according to Microsoft MVP Andre Baltieri, that is only the beginning.In this episode of M365.FM, Mirko Peters sits down with Andre for a deep dive into the transition from traditional software development to AI-assisted development, coding agents, agentic architectures, Microsoft Agent Framework, .NET, RAG, context engineering, security, and the economics of generative AI.

FROM .NET IN 2003 TO THE AI ERA

Andre takes us back to the early days of .NET and C#, when learning a new Microsoft technology often meant purchasing official training and traveling to another city.Since then, software development has moved through desktop, web, mobile, cloud, containers, microservices, and serverless computing.Andre argues that the transition to AI feels fundamentally different. Instead of simply introducing another platform or framework, AI introduces a new way for humans to interact with software.

AI ASSISTANTS VS. AI CODING AGENTS

There is an important difference between having an AI assistant inside your IDE and delegating work to an agent.An assistant can explain code, suggest refactoring, answer questions, and help developers understand their applications.An agent can receive a goal, create a plan, divide the work into smaller tasks, use tools, coordinate additional agents, and implement significant parts of the solution.Andre explains how this is already changing his own development workflow, with AI now generating much of the code he previously would have written manually.

SPEC-DRIVEN SOFTWARE DEVELOPMENT

As agents become more capable, specifications become increasingly important.Instead of describing every implementation detail, developers can define requirements, architecture, constraints, and expected behavior and allow agents to determine how parts of the implementation should be completed.This shifts developer attention from simply producing code toward defining what should be built and why.

MICROSOFT AGENT FRAMEWORK

The conversation moves into Microsoft Agent Framework and its role in bringing AI capabilities into existing applications.Andre explains how the framework brings together capabilities associated with Semantic Kernel and AutoGen and provides developers with tools for connecting models, orchestrating workflows, using MCP, implementing RAG, handling data ingestion, and exposing application functionality to AI.For .NET developers in particular, this can significantly reduce the amount of integration code required.

WHY .NET STILL MATTERS IN THE AI ERA

Python remains one of the dominant languages in AI development, but Andre argues strongly that .NET and C# are extremely well positioned for enterprise AI applications..NET continues to evolve rapidly, while Microsoft's AI tooling increasingly gives C# developers native access to modern AI capabilities.Organizations with years of business logic already implemented in .NET may therefore have a major advantage: they do not necessarily need to rebuild everything before introducing AI.Existing functionality can instead be selectively exposed to agents and AI-powered applications.

FROM DETERMINISTIC SOFTWARE TO AGENTIC SYSTEMS