Domain-driven, AI-augmented: The next chapter of platform engineering

Platform engineering has rapidly evolved from a niche practice to a cornerstone discipline driving software delivery at scale. As organizations push further into digital transformation, the role of platform engineering is not just important - it’s foundational.

Drawing from the ideas explored in Effective Platform Engineering (Manning Publications) and incorporating the latest industry trends in 2025 with a particular focus on AI, let’s dive deeper into what makes modern platform engineering successful and sustainable.

The platform engineering renaissance

The current wave of platform engineering is about empowering teams, enabling autonomy, and accelerating software delivery, without forcing rigid standards or reinventing the wheel for every team.

Today’s most effective platforms:

• Treat developers as customers.
• Provide self-service experiences with strong Golden Paths.
• Enable high-quality, reliable delivery pipelines without heavy-handed mandates.
• Foster domain-aligned abstractions instead of lowest-common-denominator solutions.

The rise of Internal Developer Platforms (IDPs), platform as a product thinking, and AI-augmented workflows has turned platform engineering into a true business enabler.

Understanding domains: More critical than ever

One of the most overlooked pillars of successful platform engineering is understanding the domains.

In 2025, with software ecosystems becoming more complex (especially with AI agents, edge workloads, and domain-specific applications), aligning platforms to the real cognitive models and workflows of different engineering teams is non-negotiable.

Effective platforms recognize that:

• Different teams need different abstractions: A data engineering team building streaming pipelines works within a completely different set of assumptions, skill sets, and operational rhythms than a mobile app development team focused on user experience and frequent releases. A platform that serves both must honor these differences at the abstraction layer.
  
  
• One-size-fits-all models erode trust and slow adoption: Forcing teams into rigid workflows designed for "generic" users leads to frustration, circumvention, and shadow IT. Trust in the platform, and by extension, in the platform team, is predicated on empathy for domain-specific ways of working.
  
  
• Domain-driven platform engineering ensures platforms support, not stifle, differentiated team needs: By embedding domain understanding into platform design - such as offering specialized CI/CD golden paths, domain-specific observability bundles, or AI-augmented workflows tailored to each team's context - the platform becomes a true enabler of velocity and innovation, rather than a bureaucratic obstacle.

This mindset shift mirrors the success stories of companies like Spotify (with their domain-aligned "squad" model and platform abstractions), Airbnb (with their domain-specific frameworks for mobile and data), and more recently, Snowflake (where AI-augmented domain specializations across data engineering, analytics, and AI workloads have fueled massive adoption).

In a world where AI copilots and autonomous agents are starting to co-own software delivery pipelines, domain-driven platform engineering isn’t just an improvement: it’s a foundational requirement for staying relevant.

Platform as a product: Not just a buzzword

Today, "platform as a product" is no longer just good advice, it’s essential for survival. Internal platforms are competing for mindshare against a growing array of external tools, open-source solutions, and even AI-augmented development environments. If internal platforms don't act like real products, treating developers as customers, they will simply be bypassed.

The best platform teams in 2025 embrace this reality by operating with the same rigor and customer-centricity as world-class product organizations. They:

• Conduct regular user research, interviews, and journey mapping, just like external product managers do. Understanding the daily pain points, goals, and workflows of developers is foundational to building platforms that get adopted.
  

• Define clear and actionable personas — such as front-end developers optimizing for rapid iteration, SREs prioritizing stability and observability, ML engineers managing complex pipelines, and now even AI agents collaborating with human teams.
  Each persona has distinct needs, and the platform must offer tailored experiences to meet them.
  

• Measure success with quantifiable, developer-centric metrics including:
  
  • Developer Satisfaction Scores (DevSat)
  • Platform Net Promoter Scores (Platform NPS)
  • Golden Path adoption rates (usage of curated, opinionated workflows)
  • Time-to-First-Deployment (TTFD)
  • Friction Logs (capturing barriers encountered by users)
• Invest in dedicated Technical Platform Product Managers (TPPMs) who are responsible for bridging technical capabilities with user desires, shaping the roadmap based on real usage patterns rather than internal assumptions. These TPPMs act as the voice of the customer within platform engineering teams, ensuring that user empathy translates into platform functionality.

Simply "building useful tools" is no longer enough. Platform teams must create coherent, delightful, and opinionated products - complete with intuitive documentation, polished onboarding experiences, AI-augmented workflows, and measurable outcomes that developers want to use, not feel forced to use.

The organizations leading the next wave of platform engineering, from hyperscalers to fintech startups, are the ones treating internal platforms as first-class products, relentlessly focused on user experience, iteration, and value delivery.

Evolving foundations: What’s new in 2025

Since the publication of the original article, a few key shifts have become increasingly relevant:

In short, AI, economics, and user-centricity are more deeply intertwined with platform engineering today than ever before.

Common pitfalls (still) to avoid

Even as platform engineering matures, many organizations still fall into classic, and costly, traps that undermine adoption and impact. In a domain-driven, AI-augmented world, avoiding these pitfalls becomes even more crucial:

• Overengineering without user validation: Platform teams sometimes build highly complex features based on assumptions rather than real user needs. Without domain-informed user research and rapid feedback loops, complexity grows for complexity’s sake, leading to bloated systems that intimidate rather than empower developers.
  

• Mandating usage without delivering tangible value: Forcing teams onto a platform without clearly solving their domain-specific pain points breeds resentment and circumvention. Adoption must be earned, not enforced, by delivering real, measurable value that improves the day-to-day lives of each team member.
  

• Neglecting marketing and advocacy: One of the most overlooked aspects of Domain-driven platform engineering is internal marketing and advocacy. Now, why internal marketing? This is not an engineering platform. But therein lies the commonality. Remember that this is a paradigm shift built on top of the platform engineering principles. A platform is not just a technical artifact: it's a product competing for attention and trust within the organization. In domain-driven environments, developers need to see how the platform fits into their specific world, how it simplifies their workflows, accelerates their outcomes, and aligns with their domain-specific realities. Successful platform teams actively market features, tell user success stories, highlight domain-specific wins, and build an internal brand around the platform. If you build it, they will not automatically come, especially in a complex, multi-domain organization where cognitive overload is already high.

• Ignoring telemetry and feedback signals: Without robust instrumentation, platform teams fly in the dark. Domain-driven platforms must track not only basic usage but also domain-specific adoption patterns, developer satisfaction, golden path utilization, friction points, and impact on broader business goals. Instrumentation isn't just about KPIs: it’s about understanding where different domains are thriving, struggling, or diverging in their needs, which enables the continuous evolution of the platform.
  

Modern platform teams must think like product teams, market like growth teams, and operate like site reliability teams.

What’s next: Building the autonomous platform

Looking ahead, the frontier is clear:

• Self-healing platforms that proactively optimize developer workflows and infrastructure.
• Event-driven, intelligent control planes that predict bottlenecks and suggest improvements.
• Composable developer experiences tailored to personas and domains—augmented by GenAI, but never dictated by it.
  

Internal developer platforms will increasingly blend AI, observability, cost management, and DX optimization into seamless experiences. ​In the evolving landscape of AI-integrated platform engineering, the Model Context Protocol (MCP) emerges as a pivotal standard, facilitating seamless interactions between AI agents and enterprise systems. MCP enables AI tools to access and manipulate infrastructure components securely and contextually, ensuring that AI-driven actions are both relevant and compliant with organizational policies. Canyon AI exemplifies the practical application of MCP in platform engineering. By integrating MCP, Canyon allows developers to engage with their infrastructure through natural language, translating requests into secure, deterministic actions. This approach minimizes the risks associated with AI hallucinations and unauthorized changes, as all actions are confined within predefined, compliant workflows. Canyon's design ensures that AI assistance enhances developer productivity without compromising system integrity.

However, platform engineering isn’t just about technology. It’s about enabling human creativity on a large scale.

Conclusion

Going forward, effective platform engineering is about far more than shipping tools or standing up infrastructure. It’s about empowering developers, amplifying team autonomy, and accelerating innovation through thoughtful, sustainable platform ecosystems that are deeply aligned with real-world domains.

The platform is the product: a living, evolving product that must be curated, marketed, measured, and refined with the same discipline as any customer-facing offering. The developers are your customers: diverse, domain-specialized, and expecting experiences that are intuitive, intelligent, and aligned with how they work. The future is domain-driven, AI-augmented, and business-aligned, where platforms don't just support technology delivery, but actively shape how companies compete, scale, and differentiate.

The winners of the next era will be those who recognize that platforms are no longer neutral back-office utilities. They are force multipliers for innovation, catalysts for cultural transformation, and strategic assets that directly drive business outcomes.

Domain-driven platform engineering, amplified by AI, is not a luxury as we see in solutions like Mia-Platform. It is the foundation for building organizations that are resilient, adaptable, and capable of unleashing human and machine creativity on a large scale.

The next chapter of platform engineering has already begun. The question is: Will your organization lead it, or be left behind?