Artificial Intelligence with Platform Engineering

Arguably the two biggest themes of the past few years, especially in the enterprise are Platform Engineering (PE) and Artificial Intelligence (AI). Both have become buzzwords and delivered some initial wins but haven't yet lived up to their full promise. What if the best way to meet the sky high expectations is to combine AI and PE?

As we've covered before platform engineering has emerged as the next evolutionary step to address the overload introduced by the explosion of software that has occured over the next decade, and the subsequent pressure it applied to cloud and DevOps teams. At its core, platform engineering is about creating reusable, self-service platforms and patterns that reduce developer toil and shrink the context window a developer needs to be productive. Instead of every team reinventing the wheel, platform engineers design, deploy and operate purpose built platforms that:

• Standardize Workflows: Provide templates and blueprints for common tasks.
• Improve Discovery: By leveraging a software catalog changes, relationships, dependencies, tooling and documentation can be rapidly understood and contextualized by developers.
• Enable Self-Service: Allow developers to deploy resources, monitor applications, and troubleshoot issues without external dependencies.
• Enhance Security and Compliance: Embed best practices into the platform itself, reducing risk and context switching for other teams.

For example, a platform engineering team might create an internal developer portal (IDP) powered by Flightdeck / Backstage. This portal includes at it's heart a software catalog and consolidates the view of downstream tools such as CI/CD pipelines, observability, and cloud infrastructure, offering developers a heads-up display for understanding, building and managing applications.

The Role of Artificial Intelligence in Platform Engineering

The age of artificial intelligence is transforming every aspect of technology, and platform engineering is no exception. There are two scenarios at play here; using AI within PE and building platforms for AI delivery. While there may be some overlap the two uses are different enough to warrant discussion as stand alone topics.

In the first scenario, AI is being baked into various aspects of tools and processes that the PE team uses themselves, some of which may be dog-fooding of tools they deliver to developers, others will be used primarily by the PE team.

But this raises the complex questions which led us to the second scenario. If a new agentic workflow is deployed to support developers, who is building the underlying infrastructure, specification and interfaces for that suite of capabilities? How are these new systems governed and secured?

In our view, that should be the platform engineering team. The same strategy and standards should be used here as with other platforms.

This is where we see platform engineering will be headed over the next couple of years: the platform team leverages AND provides AI capabilities to developers.

What We Call AI Platform Engineering (APE)

This presents a significant improvement over previous generations of tooling in both the developer and DevOps sphere. Instead of fighting headwinds getting developers to use new tools or adopt broken processes, developer platforms will come with intelligence baked in from day-1. Most importantly, they will provide access to the intelligence where developers and operators already work. In essence these platforms will bring the water to the horse.

The implementation and approach taken here can range from a chat-based LLM in place of search functionality, to a code generation assistant, or even a fully agentic workflow. The underlying tooling provides the same core functions, but with massively improved flexibility, performance and interfaces that leverage advances in AI.

Our opinion is that this functionality should be delivered by the platform engineering team in a workflow that is already native for developers and operations teams. Rather than yet another web interface or application, bring these capabilities directly into the IDE, terminal and collaboration tools where people already do work. This is precisely what we have already done with Flightdeck, and are actively adding even more of these types of capabilities.

Enhanced Context and Awareness

AI can already analyze patterns in logs, metrics, and traces to identify bottlenecks, optimize workflows, and increasingly even predict failures or redress them as they occur. This dramatically reduces the amount of toil a developer, SRE or associated team encounters.

Using a graph-backed developer platform helps provide improved context for AI and developers. This scenarios provides a few really useful implementation patterns:

• Leverage Retrieval Augmented Generation (RAG), producing locally relevant results for queries.
• Assist developers by summarizing status and enriching status with meaningful context from downstream systems and agents.
• Suggest improvements to application code based on historical and organizational data and locally relevant patterns.
• Connect disparate data sources through composable workflows.

Intelligent Context Management

As developers switch between tasks and tools they are often overwhelmed trying to find things. A given piece of software is often represented differently in each tool a developer uses. Similarly each tool often has a different workflow. All of this requires the developer to maintain a mental model of what they are working on. A well-designed and integrated AI can consolidate and present relevant context faster, reducing the burden on the developer. For example:

• Summarizing downstream tools, code and discussions about a specific software entity.
• Highlighting unresolved dependencies or seemingly unrelated changes in code or configuration.
• Proactively surfacing documentation, code or API references related to ongoing work.

The Future of AI Platform Engineering

As AI continues to evolve, so will the demands on platform engineering. It is hard to predict the future, but we currently think this is what it looks like as these domains converge:

• Hyper-Personalized Developer Tools: Platforms will leverage AI to adapt dynamically to individual workflows and preferences, offering contextual suggestions, automated task management, and real-time feedback tailored to each developer's unique needs at each moment.
• Autonomous Systems: Fully self-healing and adapting platforms will become the norm, using advanced AI-driven monitoring and diagnostics to identify, resolve, and even prevent issues before they impact production. These systems will not only fix common errors but also optimize resource usage and system configurations autonomously. The governance and operations of the agents and platforms will fall to the platform engineering team.
• Standardized Patterns: As AI becomes integrated into more and more developer tools and platforms, developers will likely rely on standardized patterns, frameworks and tools to ensure success.
• AI-Driven Collaboration: Future platforms will act as intelligent intermediaries between teams and team members, summarizing discussions, suggesting next steps, and highlighting key dependencies across projects. By fostering seamless communication and coordination, AI will reduce friction in team-based workflows.
• Continuous Learning Systems: AI-enabled platforms will incorporate learning loops that evolve based on user interactions, feedback, and emerging patterns. These systems will refine their capabilities over time, offering increasingly accurate insights, recommendations, and solutions to complex problems.

Underlying all of this is a significant question. Who runs these systems? Right now many companies are allowing the usage of AI to occur on an individual-to-individual basis with minimal controls, legal agreements or technical strategy.

In the model we propose the APE team is responsible for platform definition, operation and the standardization of communication patterns and governance.

Conclusion

Platform engineering is the natural progression of decades of innovation in systems administration and DevOps. In the age of AI, it represents an opportunity to define and govern the AI implementations leveraged by the organization. Through investment in AI Platform Engineering, organizations can empower their teams, streamline workflows, and unlock new levels of innovation – all while ensuring the security, standards and governance of AI are there from day-1.

With the right strategy and tools, platform engineering can become the foundation for success in an increasingly complex and competitive landscape.

Want to learn more about how we are extending Flightdeck for these scenarios and other roadmap items? Let's talk!