AI Agents Transform Platform Engineering at Microsoft

Microsoft's platform engineering team is pioneering the use of AI agents to automate the implementation of security standards and infrastructure updates across its vast developer ecosystem. This approach is reshaping how platform engineering operates at scale, enabling faster, more consistent, and less disruptive changes. As corporate vice president of product in Microsoft’s Developer Division and general manager of the company’s first-party engineering systems, Amanda Silver leads what may be the world’s largest platform engineering operation. Her team supports thousands of engineers working on hundreds of products, ensuring software is secure, consistent, maintainable, and that developer velocity remains high.

The Scale Challenge: Managing Tens of Thousands of Tickets

Historically, platform engineering at Microsoft involved creating tens of thousands of tickets for developers to manually implement security and infrastructure changes. For example, as part of Microsoft’s Secure Future Initiative, the team needed to update authentication libraries across all Microsoft codebases—a massive effort affecting thousands of repositories and millions of lines of code. Each ticket required developers to interpret complex technical guidance and apply it correctly, leading to inconsistent implementation, slow progress, and significant developer distraction from feature work. Similar challenges arose with updating vulnerable dependencies, modernizing build pipelines, enforcing logging standards, and integrating new security scanning tools.

Enter AI Agents: Autonomous, Context-Aware Implementation

To overcome these challenges, Silver’s team adopted "coding agents"—AI systems capable of understanding technical requirements and autonomously making code changes across repositories. Instead of issuing tickets, the team feeds troubleshooting guides and implementation instructions directly into AI agents. These agents analyze codebases, understand existing contexts, and either autonomously submit pull requests or generate near-complete solutions for developers to review. For the authentication library update, AI agents identified all relevant code locations, generated contextually appropriate changes, handled edge cases, and created detailed pull requests. This drastically reduced manual effort and accelerated adoption.

Expanding AI Agent Use Across Platform Engineering

Beyond authentication updates, AI agents are now used for:

• Dependency management: Automatically identifying and updating vulnerable packages across thousands of repositories, considering compatibility and testing.
• Pipeline modernization: Updating build and deployment pipelines to newer, secure patterns while preserving functionality.
• Security scanning integration: Deploying and configuring new security tools with appropriate rules and exceptions.
• Code quality enforcement: Applying new coding standards and refactoring patterns consistently across diverse codebases.
These initiatives, which previously generated thousands of tickets and months of work, now complete in weeks with higher consistency and minimal disruption.

Implications for Platform Engineering Teams

Microsoft’s AI-driven platform engineering approach depends on:
• Context-aware code analysis
• Incremental and autonomous implementation
• Integration with developer workflows
• Continuous feedback loops
• Risk assessment and gradual rollout
This shift moves platform engineering from enforcement to enablement, scales expertise, accelerates iteration, reduces developer friction, and improves quality consistency.

Industry-Wide Impact and Future Outlook

Silver believes this model will influence the broader industry, enabling startups and enterprises alike to adopt enterprise-grade platform engineering without large teams. Platform engineers will evolve from manual implementers to AI orchestrators. Building trust in AI-generated changes through transparency, testing, and gradual rollouts will be critical. Existing tools and processes may also need adaptation to support AI-driven workflows. Looking ahead, platform engineering teams will become smaller and more strategic, focusing on system design rather than manual implementation. AI will handle the "soul-draining" tasks, freeing developers to focus on creativity and innovation.

“We’re tackling the most miserable, soul-draining parts of the job. We’re transforming them so that developers can really focus on the creative and the aspects of the role that they really enjoy,” Silver said.

Platform engineering will shift from reactive maintenance to proactive, AI-driven system design that continuously monitors and resolves issues across infrastructure.

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Source: Originally published at The New Stack on August 13, 2025.

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Frequently Asked Questions (FAQ)

AI Agents in Platform Engineering

Q: How are AI agents transforming platform engineering at Microsoft? A: AI agents are automating the implementation of security standards and infrastructure updates, leading to faster, more consistent, and less disruptive changes within Microsoft's extensive developer ecosystem. Q: What were the historical challenges in platform engineering at Microsoft? A: Previously, platform engineering involved generating tens of thousands of tickets for developers to manually implement security and infrastructure changes. This led to inconsistent implementations, slow progress, and distracted developers from core feature work. Q: How do AI agents address the scale challenge of managing tens of thousands of tickets? A: Instead of issuing tickets, AI agents are fed technical guidance and autonomously make code changes across repositories, drastically reducing manual effort and accelerating the adoption of updates and standards. Q: What are some specific examples of tasks AI agents are used for in Microsoft's platform engineering? A: AI agents are used for dependency management (updating vulnerable packages), pipeline modernization (updating build/deployment pipelines), security scanning integration, and enforcing code quality standards across diverse codebases. Q: What are the key implications of Microsoft's AI-driven platform engineering approach? A: This approach relies on context-aware code analysis, autonomous implementation, integration with developer workflows, continuous feedback loops, and risk assessment. It shifts platform engineering from enforcement to enablement, scaling expertise and improving quality. Q: How will the role of platform engineers evolve with the adoption of AI agents? A: Platform engineers will evolve from manual implementers to AI orchestrators, focusing more on strategic system design and less on the "soul-draining" tasks, which AI will handle.

Crypto Market AI's Take

The integration of AI agents in platform engineering at Microsoft exemplifies a significant shift towards leveraging artificial intelligence for operational efficiency and scalability. This mirrors the broader trend of AI adoption across industries, including finance and cryptocurrency. At AI Crypto Market, we are similarly exploring how AI agents can revolutionize various aspects of the crypto market, from sophisticated trading strategies to enhanced user experience and robust security protocols. Our platform utilizes AI-driven insights for market analysis and aims to automate complex tasks, much like Microsoft's approach. We believe that embracing AI in these critical functions is key to navigating the complexities and opportunities within the evolving digital asset landscape.

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