August 14, 2025
5 min read
Darryl K. Taft
AI Agents Transform Platform Engineering at Microsoft
Microsoft's platform engineering team is pioneering the use of AI agents to implement security standards and manage developer infrastructure at scale, marking a new era in platform engineering. As both corporate vice president of product in Microsoft’s Developer Division and general manager of the company’s first-party engineering systems, Amanda Silver oversees what might be the world’s largest platform engineering operation. Her team ensures that thousands of Microsoft engineers across hundreds of products build software that is secure, consistent, and maintainable while preserving the developer velocity essential for Microsoft’s competitiveness. Traditionally, this effort was human-driven: creating standards, generating thousands of action items, and relying on developers to implement them correctly. Over the past year, Silver’s team has shifted to using AI agents to replace manual toil, fundamentally changing how platform engineering operates at scale.The Scale Challenge: Managing 10,000 Tickets and More
A recent example highlights the scale of Microsoft’s platform engineering challenges. As part of Microsoft’s Secure Future Initiative — dubbed "the largest cybersecurity engineering project in history" — Silver’s team needed to update authentication libraries across all Microsoft codebases. This security requirement affected thousands of repositories and millions of lines of code. Previously, the team created tens of thousands of tickets for developers to manually update their codebases based on detailed troubleshooting guides. This process was slow, inconsistent, and diverted developers from feature work to infrastructure compliance — work Silver describes as "soul-draining" and ripe for AI automation. Similar challenges included updating vulnerable dependencies, modernizing build pipelines, enforcing consistent logging practices, and integrating new security scanning tools. Each initiative generated thousands of tickets, requiring human interpretation and resulting in uneven implementation.Enter AI Agents: From Tickets to Autonomous Implementation
Silver’s team now uses "coding agents," AI systems capable of understanding complex technical requirements and autonomously implementing changes across codebases. Instead of issuing tickets, platform engineers feed troubleshooting guides and implementation requirements directly into AI agents. These agents analyze codebases, understand context, and either autonomously submit pull requests or provide developers with nearly complete solutions requiring minimal review. For the authentication library update, AI agents identified all code locations needing changes, generated context-aware code updates, created pull requests with detailed explanations, and handled edge cases and legacy patterns."In some cases, it can actually autonomously submit the pull request. In other cases, it just helps get the developer much further along," Silver explained.
Scaling Consistency Across Engineering Systems
The AI-driven approach has expanded beyond authentication updates to other critical areas:- Dependency Management: Automatically identifying and updating packages with known vulnerabilities across thousands of repositories, understanding dependency trees, compatibility, and testing implications.
- Pipeline Modernization: Updating build and deployment pipelines to adopt newer, more secure patterns while preserving existing functionality.
- Security Scanning Integration: Deploying new security tools, configuring scanning rules, handling legacy exceptions, and integrating results into developer workflows.
- Code Quality Standards: Enforcing coding standards, refactoring patterns, and best practices across diverse codebases, replacing extensive manual code reviews. Previously, these initiatives would generate thousands of tickets and require months of manual work. With AI agents, Microsoft can push changes across its entire codebase in weeks, achieving higher consistency and reducing developer disruption.
- Context-aware code analysis
- Incremental implementation
- Developer workflow integration
- Feedback loops
- Risk assessment This approach signals a shift from enforcement to enablement, scaling expertise, accelerating iteration cycles, reducing developer friction, and improving quality consistency.
- AI Agents Transform Platform Engineering at Microsoft
- What is Platform Engineering?
- Accelerating Developer Velocity with Effective Platform Teams
- How AI Agents Will Change the Web for Users and Developers
- AI Agents and LLM Training Data
- AI Crypto Market Platform - LLM Training Data
Implications for Platform Engineering Teams
Microsoft’s success depends on several technical pillars:Broader Industry Impact
Silver believes these techniques will become industry standards. Microsoft’s platform is "the epicenter of where developers get their tools and their platforms," offering an opportunity to influence the broader ecosystem. For startups, AI-driven platform engineering can provide enterprise-grade capabilities without large teams, accelerating growth. Enterprises will evolve platform engineering roles from manual implementation to AI orchestration. Building confidence in AI-generated changes requires transparency, rigorous testing, and gradual rollouts. Existing tools and processes may need adaptation to support AI-driven workflows.Looking Ahead: The Future of Platform Engineering
Silver envisions smaller, more strategic platform engineering teams focused on designing systems and standards rather than manual implementation."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 aspects of their role that they enjoy," she said.Platform engineering will shift from reactive maintenance to proactive system design, with AI-driven systems continuously monitoring and automatically resolving issues across infrastructure.
Frequently Asked Questions (FAQ)
What are AI agents in the context of platform engineering?
AI agents, in this context, are sophisticated AI systems capable of understanding complex technical requirements and autonomously implementing changes across codebases. They can analyze code, identify issues, generate solutions, and even submit pull requests, automating tasks traditionally performed by human engineers.How do AI agents address the scale challenges faced by platform engineering teams?
AI agents can process and act upon a vast number of tasks simultaneously, which is crucial for managing large codebases and numerous repositories. Instead of creating thousands of manual tickets, AI agents can identify and fix issues across all affected code, significantly reducing the time and effort required compared to manual processes.What are the key technical pillars enabling AI agents in platform engineering at Microsoft?
The success of AI agents in this domain at Microsoft relies on several key technical pillars: context-aware code analysis, incremental implementation, seamless integration into developer workflows, robust feedback loops, and thorough risk assessment.What are the broader implications of using AI agents for platform engineering in the industry?
The use of AI agents is expected to become an industry standard. For startups, it can provide enterprise-grade capabilities without the need for large teams, thus accelerating growth. For established enterprises, it means platform engineering roles will likely shift from manual implementation tasks to overseeing and orchestrating AI systems.How does Microsoft build confidence in AI-generated changes?
Microsoft builds confidence in AI-generated changes through transparency, rigorous testing of the AI agents' outputs, and gradual rollouts of their capabilities. Adapting existing tools and processes to support these AI-driven workflows is also a critical step.Crypto Market AI's Take
The application of AI agents in platform engineering, as demonstrated by Microsoft, showcases a significant leap forward in how complex technical challenges can be managed at scale. This mirrors the broader trend in the technology sector where AI is being leveraged to enhance efficiency and accuracy. In the cryptocurrency space, similar AI-driven approaches are revolutionizing how market data is analyzed, trading strategies are developed, and operational tasks are automated. Our platform at Crypto Market AI utilizes advanced AI agents to provide real-time market intelligence and sophisticated trading bots, aiming to democratize access to powerful financial tools for both individual and institutional investors. This transformation from manual processes to autonomous systems is not just about efficiency; it's about unlocking new levels of insight and capability, much like how Microsoft is streamlining its engineering processes.More to Read:
Source: AI Agents Transform Platform Engineering at Microsoft by Darryl K. Taft, published August 13, 2025.