How agentic AI unlocks platform engineering potential

Many organizations have embraced artificial intelligence (AI) tools to boost developer productivity, typically in the form of code assistants that help individual developers write code faster. While these tools deliver welcome efficiency gains, they only scratch the surface of AI’s potential to transform your development organization.

At the same time, organizations are investing in platform engineering — building integrated toolchains, establishing workflows, and creating standardized processes for software development. However, platform engineering teams will inevitably run into issues with scale: traditional approaches to platform engineering are fundamentally limited by the capacity of human engineers to maintain the platform.

This is where agentic AI becomes a game-changer for your platform engineering investments.

Why your current platform engineering investment isn’t delivering its full potential

Imagine this: You’ve invested in a platform engineering approach with tightly integrated tools and processes. However, you’re not yet seeing the expected return on this investment due to challenges such as:

• Manual processes that can’t scale across the organization
• Difficulty maintaining and updating platform components
• Inconsistent adoption of standards across different teams
• Valuable engineering time spent on routine maintenance instead of innovation

These challenges exist because traditional approaches to platform engineering are fundamentally limited by human capacity. No matter how well-designed your platform is, you need human engineers to implement, maintain, and scale it.

Code development in enterprise software is also highly context-dependent. Languages can have vastly different performance challenges. Junior developers may not have enough context to write prompts effectively. Security and compliance policies may also create unknown restrictions. No single platform engineer can fully grasp every security, network, and application-layer concern across all these scenarios.

How agentic AI transforms platform engineering

Agentic AI systems fundamentally change the equation. Unlike traditional AI assistants that respond only to direct prompts, AI agents have full context into a team’s software development infrastructure. They can initiate actions based on triggers and states, making them the perfect complement to platform engineering frameworks. These autonomous systems can work with minimal human intervention to automate repetitive tasks while still allowing for human oversight on more critical issues.

AI agents can:

• Automatically identify and implement standardization opportunities across your entire organization, ensuring consistency without manual intervention
• Maintain and update platform components by monitoring for security vulnerabilities, performance issues, or outdated dependencies
• Create issue descriptions, reusable templates, and implementation plans based on your existing codebase
• Support developers in generating and updating documentation, including README files, code flow diagrams, and architecture documentation

Learn how agentic AI built on top of a comprehensive DevSecOps platform can help development teams navigate everything from project bootstrapping and deployment to debugging and cross-team coordination.

What to keep in mind when implementing AI agents

Ask yourself these questions when thinking about how to incorporate agentic AI into platform engineering workflows:

Interoperability, scalability, and reliability

• How will AI agents communicate with other agents, including across third-party products?
• How will agents self-correct when they produce unexpected or unwanted results?
• Will the agents be capable of concurrency (working on multiple tasks at the same time)?

Security, governance, and observability

• How will AI agents interact with existing network policies to define what they can and cannot access?
• How will agents interact with multiple data sources?
• Will the use of data by AI agents comply with existing data governance, security, and privacy policies?
• How will agents’ telemetry data be collected, how will their performance be measured, and what will remediation look like when they behave incorrectly?

Developer workflows

• How will developers adapt their processes to integrate with agent-based systems?
• What human supervision mechanisms need to be in place for critical business processes?

Enabling team velocity with AI agents and platform engineering

As AI-powered agents mature, organizations can proactively use these tools to identify patterns, standardize practices, and share knowledge across different development teams without manual coordination.

After participating in a recent early access program for GitLab Duo with Amazon Q — an agentic AI solution for the entire software development lifecycle — Osmar Alonso, DevOps Engineer at Volkswagen Digital Solutions, shared how AI agents have transformative potential for development workflows. “Even in its early stages, we saw how the deeper integration with autonomous agents could streamline our process, from code commit to production,” said Alonso. “We're excited to see how this technology empowers our team to focus on innovation and accelerate our digital transformation.”

Agentic systems show particular promise in “tech mandatory” budget areas that most teams are committed to today, such as reducing technical debt, fixing security vulnerabilities, refactoring automation or infrastructure, and re-platforming legacy apps. These areas are filled with context-rich data and pose barriers to automation that agentic AI can help remove.

For example, platform engineering teams often create templates to standardize and automate processes such as CI pipelines. This traditionally involves significant manual work to identify the right processes to target — those that are widely used, have repeatable steps, and will have the most significant impact across teams. Agentic AI reduces those manual steps.

Rather than relying on human effort to identify processes for standardization, an agentic system can identify all Java-based projects from the past year, analyze the build processes across each, and identify the best candidates for AI-based automation. The system can then create draft templates the team can customize and build on.

Agentic AI is also poised to transform how developers access organizational knowledge and tools. Many companies have invested in internal developer portals (IDPs) as abstraction layers to provide insights and portfolio visibility. However, IDPs are generally static artifacts — someone needs to maintain them and notify developers when new versions of reusable components are available. It's possible that AI agents, as part of a DevSecOps platform, will make IDPs increasingly irrelevant by proactively analyzing context across multiple data sources and tools to provide real-time insights directly within the platform. Agents will also be able to pair with the generative AI capabilities within the platform to deliver personalized, contextual information — such as vulnerability explanations or code suggestions — to each developer based on their role, project, and needs.

Platform engineering has delivered significant value, but many organizations have hit a ceiling in realizing its full potential. Agentic AI plays a crucial role in upgrading platform engineering efforts by automating complex processes, applying contextual understanding at scale, and enabling true team velocity rather than just individual productivity.