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To operationalize Platform Engineering 2.0, extend the internal platform you already run so it can serve AI-era workloads and users, while keeping platform-as-product, golden paths, and self-service. In practice that means finding the repeated problems your teams face, standardizing what is common, shipping a small platform people actually use, and adding AI-specific capabilities only when validated workloads require them.
“Platform Engineering 2.0” is a framing, not an industry standard. It comes from a report by Weave Intelligence, commissioned by Broadcom, which presents it as an extension of platform as product, golden paths, and self-service internal developer platforms (IDPs). CNCF uses the same term in a July 2026 article. Treat both as planning vocabulary, not a certification your organization must meet.
What the term adds, and what it keeps
The report’s central argument is that AI introduces new workloads and a new class of platform user, but does not require abandoning the operating model that made internal platforms work. It proposes that the IDP evolve into what it calls an Agentic Development Platform, retaining its operating model while expanding its substrate. “Agentic Development Platform” is the report’s own label, so use it as shorthand for that proposal rather than as a product category.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteCNCF’s July 2026 article on AI-native workloads makes a similar point from the community side. It says platform as product, developer productivity, golden paths, and shift-left security remain relevant, and it treats AI-native infrastructure and composability as the new concerns. Atulpriya Sharma, Co-Organizer of the CNCF Platform Engineering Technical Community Group, describes the scope change this way:
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What started as a developer productivity function is now the centralised governance layer for the enterprise – enforcing cost discipline, security posture, and AI readiness across every team. The platforms that can absorb that scope without structural debt aren’t the ones built around fixed architectures. They’re the ones built to be composable from day one.
The practical consequence is that the platform’s remit widens from developer productivity to governance across cost, security, and AI readiness, and composability becomes a design requirement from the start rather than a later refactor.
Foundations to keep before you add anything
CNCF defines platform engineering as planning and providing platforms through people, processes, policies, and technologies, in pursuit of business outcomes. The CNCF Platforms White Paper gives a planning-friendly definition of a digital platform:
A digital platform is a foundation of self-service APIs, tools, services, knowledge and support which are arranged as a compelling internal product.
The white paper attributes this sentence to Martin Fowler and Evan Bottcher. Four foundations from this model should survive any AI-era extension:
- Platform as product. The platform has users, a backlog, documentation, and an owner who answers for adoption.
- Golden paths. Supported routes that let teams deliver common workloads without rebuilding the same plumbing.
- Self-service. Users consume shared capabilities directly rather than filing tickets for each request.
- Shift-left security. Security configuration is built into the shared patterns developers already use.
A practical sequence for operationalizing it
Work through these six steps in order. Each one depends on the output of the previous step, and skipping ahead usually means building capabilities nobody asked for.
1. Find the repeated user problem
Map your internal users, their delivery workflows, the points where they stall, and the infrastructure or operational work that is repeated across teams. CNCF’s maturity model states that platforms are shaped for each organization and may begin as simply as documentation for using third-party services. Its intended audience includes leaders, platform teams, enterprise architects, and application teams. Do not assume you need a sophisticated portal or a large dedicated platform group at the outset.
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2. Decide what to standardize, buy, configure, or build
CNCF’s March 18, 2025 guidance on scaling platform building recommends considering market and cloud-provider services for common commodity needs, then filling organization-specific gaps through platform integration or custom capability. Industry-specific compliance processes are a typical example of a gap that a generic service will not close. Look for needs repeated across teams, such as security configuration, provisioning, observability, and delivery workflows, and sort them into common capabilities versus requirements unique to your organization.
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Two cautions apply to this decision:
- A public cloud or SaaS toolset is not, by itself, your internal platform. CNCF’s guidance notes that general-purpose tools can leave gaps in governance, tailored developer experience, and organization-specific workflows.
- The platform team does not have to build every backing capability. The CNCF Platforms White Paper describes the platform as a thin layer that can compose managed services with internal implementations.
3. Deliver a minimum useful platform and learn
Start with the smallest set of capabilities a user needs to complete a real workflow, release it, collect feedback, and iterate. CNCF warns that a big-bang platform makes it harder for builders and users to form feedback habits. Its 2025 guidance recommends moving from a minimum viable product toward a “Thinnest Viable Platform” (TVP), which means continuously removing unused features and custom components that have become commodity services.
The concept of the TVP is closely tied to how platform teams draw their boundaries. CNCF’s platform-building guidance points to the book Team Topologies: Organizing Business and Technology Teams for Fast Flow when explaining it, and it is a useful next read for leaders deciding who owns what.
The CNCF White Paper describes consistent user experiences such as web portals, project templates, and self-service APIs. Treat these as examples of how a platform can present capabilities, not as a mandatory stack. The sources discuss portals, templates, self-service APIs, pipelines, databases, secrets management, and observability as capability categories; they do not establish that any particular product in these categories is superior.
4. Run the platform as a product
Keep user research, prioritization, documentation, adoption work, and ongoing operations under the same ownership. CNCF describes the platform team’s role as reducing repeated work, enabling reuse, and embedding governance in shared patterns. Measure the outcomes that matter to your organization. The CNCF White Paper lists these as intended value areas:
- user friction and time spent on common workflows
- adoption and task completion
- delivery performance
- reliability
- security and compliance
- cost
These are categories to track, not promised improvements. Establish a baseline for each before you release a capability, so that you can tell whether it changed anything.
5. Use maturity as a roadmap, not a badge
CNCF’s Platform Engineering Maturity Model defines four levels: Provisional, Operational, Scalable, and Optimizing. Assess each dimension independently, because a team may show characteristics of several levels at once. The model’s own guidance is that greater maturity brings heavier demands on funding and staff time, and that reaching the highest level should not be a goal in itself. Martin Fowler, quoted on the CNCF maturity page, puts it directly:
The true outcome of a maturity model assessment isn’t what level you are at but the list of things you need to work on to improve.
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Use the dimensions below to turn an assessment into a list of next steps.
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| Dimension | Question to ask in your assessment | Note from the model |
|---|---|---|
| Investment | Are funding and staffing sufficient for the next level we want to reach? | Higher maturity requires more funding and staff time. |
| Adoption | Which teams use the platform, and which still build their own paths? | Assessed separately from the other dimensions. |
| Interfaces | Are our interfaces custom processes, standard tooling, self-service, or integrated services? | Progresses from custom processes through standard tooling and self-service solutions toward integrated services. |
| Operations | Who runs the platform day to day, and how is that work measured? | Assess against the level descriptions in the model itself. |
The maturity model includes an illustrative scenario in which automation saves $10m/year. That is an example used to explain the model, not a measured result or a sector statistic, and it should not be used as a benchmark for your business case.
6. Add AI-era capabilities in response to actual needs
Start with an inventory rather than a list of AI features. Document the following for your current and planned AI workloads:
- Workloads: which models, pipelines, or model-serving jobs teams run, and what they need from compute and data access.
- Users: developers, platform teams, and, where applicable, agents acting on behalf of users.
- Infrastructure: compute allocation and model-serving workflows.
- Governance: the security and policy requirements each workload must meet.
- Cost: the controls needed to keep spending visible and bounded across teams.
Then decide whether the existing platform needs new interfaces, compute allocation, model-serving workflows, or controls. The CNCF article and the report describe these as possible areas of evolution. They are not evidence that every organization needs every capability. Keep the established foundations and add only what validated workloads require.
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When you compare options for any capability, assess each against these criteria:
- Fit to internal workflows and to organization-specific policy or compliance needs.
- User experience, discoverability, and quality of self-service.
- Integration and composability across backing services.
- Operational ownership, reliability, and maintenance burden.
- Cost, and the people and funding investment required at your maturity level.
- Ability to meet validated AI workload, security, and governance needs without overbuilding.
These criteria are synthesized from the CNCF and report sources, not a vendor ranking. The sources do not establish a vendor comparison or a measured head-to-head evaluation, so use the criteria to structure your own trial rather than to choose a product.
Quick Recap
Further reading
- CNCF Platforms White Paper, from CNCF TAG App Delivery, for the definitions of digital platforms and the thin-platform model.
- Platform Engineering Maturity Model, from CNCF TAG App Delivery, for the four levels and assessment dimensions.
- Scaling Platform Building: Balancing What is Unique to Your Org and Common Across Teams, from CNCF, March 18, 2025, for build-versus-buy reasoning and the TVP concept.
- Platform engineering 2.0: An evolution for the AI era, from PlatformEngineering.org and Weave Intelligence, commissioned by Broadcom, for the report’s framing and its Agentic Development Platform proposal.
- Evolving platform engineering for AI-native workloads, from CNCF, July 6, 2026, for the community view on governance and composability.
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