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Top 10 Enterprise Technology Trends in 2025: Platform Engineering and AI Agents Lead the Charge…

Top 10 Enterprise Technology Trends in 2025: Platform Engineering and AI Agents Lead the Charge…

There are three key requirements when predicting the top 10 most important enterprise software technologies for 2025:

  1. It needs to be real: Only technologies that have demonstrated their value in production environments qualify as “real” and will be included in this list.
  2. It needs to be accessible: For a technology to become a trend it is crucial to be accessible to a wide range of enterprises. Technologies that are only leveraged by a handful of the largest organizations do not qualify.
  3. It needs to have strong business impact: Top technology trends need to move the needle on a business level. This typically means enhancing human productivity, application and infrastructure resilience, performance, or scalability.

Here are places 1–5. 6–10 are included in part 2 of this article.

1. Platform Engineering as the Key to Business Success

Developers create the new product capabilities and features for enterprises to win in the marketplace. Platform engineers are there to ensure that application developers can spend most or all of their time on productive coding by providing a developer platform with self-service access to all of the APIs needed for optimal productivity.

Platform engineering focuses directly on increasing developer productivity by removing overhead tasks from their daily schedule. While platform engineering has been around for almost a decade, many enterprises doubled down on their investments in this discipline in 2024. As so often, this was due to market pressure demanding lower operations cost, more frequent releases, higher resiliency, and more valuable product capabilities to be delivered in a timely manner.

Platform engineering is a complex discipline as it is all about automating and integrating a large number of complex processes and tools that are part of the application development lifecycle (see figure 1). The ideal developer platform provides self-service access to everything developers need for optimal productivity, including built-in monitoring and observability, secrets management, container orchestration and management, image management, continuous deployment, API management, and security scans. Operations teams contribute by automating compute, storage, network, and database infrastructure, as the basis for developer self service.

Where to Focus in 2025

Based on recent research from the Enterprise Strategy Group limited internal expertise, automation gaps, and limitation of tools are today’s most significant bottlenecks holding back the adoption of platform engineering (figure 2). These are the three challenges enterprises need to solve in 2025 to be able to optimally benefit from development platforms as accelerators for developer productivity. Enterprises need to sharpen their focus on three core areas to address these challenges and realize the promise of next-generation platform engineering.

  1. Organizations must invest in building deep platform engineering expertise through partnerships, upskilling, cross-functional teams, and hiring specialized talent.
  2. Closing the automation gap will require a concerted effort to identify high-value processes that can be automated, prioritizing end-to-end workflows that reduce manual overhead.
  3. Mitigating tool limitations involves selecting development tools that align with organizational needs, and extending or integrating existing solutions to close functionality gaps.

By concentrating on these three focus areas throughout 2025, enterprises can position themselves for success in leveraging development platforms to accelerate innovation and drive developer productivity.

Business Impact: 10/10 — Very High

Application developers often spend only half of their time coding productively and the other half is consumed by system maintenance, addressing technical debt, updating tickets and project management tools, morning meetings, managing third -party integration, production support, writing infrastructure code, and so on. Platform engineering aims to reduce or eliminate many of these overhead tasks which does not only free up developer time, but also significantly decreases frustration. At the end of the day, platform engineering is critical to ensure scalability in terms of product development, as it aims at optimizing developer productivity without creating significant additional operations cost.

2. Task-Centric AI: AI Agents Collaborate to Get Stuff Done

After the end of the AI honeymoon, organizations are now looking for tangible use cases that aim to deliver quantifiable value quickly. This requires the simplification of AI tools and a general focus on transparency, explainability, and overall governance. Once this homework has been done, the “age of AI” will truly begin. Agents will be the critical component helping AI sustainably prove its value.

The value of AI lies in making humans more productive and enabling data-driven decision making. Therefore, it is much less important for an AI model to be on top of all benchmarks, than it is critical to be able to define specific goals for the AI to reliably and independently achieve.

As LLMs offer the best results when provided with context and clear instructions related to a specific task, it makes sense to create so-called AI agents that are responsible for completing just one specific task. These agents can be coordinated by ‘team leads’ (figure 4), which are agents higher up in the hierarchy that oversee the interaction between individual agents. Based on this principle, there can be multiple hierarchy levels of agents and ‘team leads’. The code in figure 4 shows the definition of a ‘team lead’ based on the excellent open source agent framework by phidata.

#### This code will not run on its own. It is there only to illustrate what
#### a teamlead agent might look like. 
#### You would then hvae to define the 3 agents that are part of the team.

team_lead = Agent(
    name="Team Lead",
    model=OpenAIChat(id="gpt-4o"),
    team=[software_developer_agent, tester_agent, bug_fixer_agent],
    instructions=[
        "Coordinate the workflow: first have the Software Developer Agent create the code, then the Tester Agent test it, then the Bug Fixer Agent fix issues if any are found.",
        "Summarize the final state of the software."
    ],
    show_tool_calls=True,
    debug_mode=True,
    save_to_file=True,
)

It is hard to overstate the wide range of use cases agent frameworks can be applied to. Want agents to create a handy news digest consisting of content from all of your favorite news sites, blogs, and any other publication? Maybe have another agent to turn the digest into a daily dashboard for online viewing? Why not have one more agent responsible for feedback learning?

Where to Focus in 2025

In 2025 organizations need to focus on making agent workflows easy to create, enhance, manage, and use for a wide audience of people of various technical levels. Simplifying the use of AI agents involves a lot more than slapping a GUI in front of them and let the user click together their own agents and workflows. The real challenge lies in creating continuously enforced guardrails from a security, compliance, cost efficiency, and accuracy perspective to provide centralized governance across many complex agent workflows.

Business Impact: 10/10 Very High

Making teams of AI agents available to human staff will allow technical and non-technical job roles to iteratively automate many of the currently time consuming and painful overhead tasks they need to suffer through on a daily basis.

3. Kubernetes for Everyone

Kubernetes is incredibly scalable, but it is not simple for operators and DevOps teams to adjust to a completely policy-driven (declarative) approach were everything that’s ‘hardcoded’ limits future scalability. Software and cloud vendors need to continue to work on offering simple, pre-integrated, and easy to configure Kubernetes clusters.

Instead of coding, developers often spend an exorbitant amount of time dealing with the deployment, configuration, and management of their application and the surrounding Kubernetes services and infrastructure. For example, when deploying to a managed Kubernetes service such as AWS EKS, developers need to address integrations with numerous different cloud services related to networking and security, storage and data, and monitoring and observability. Chart 4 offers some basic insights into the complexity of even basic Kubernetes environments, making it difficult for traditional IT teams to switch over from traditional VM-based infrastructure to environments leveraging containers and Kubernetes clusters for scheduling.

Nate Ceres and Sean McKenna, both senior product marketing managers at Microsoft, sum up this challenge in this 1:27 minutes long conversation (recorded at KubeCon 2024 in Salt Lake City).

In a nutshell, Nate and Sean understand that they need to simplify the provisioning, integration, running, security, and cost management of their Kubernetes clusters to allow a broader range of organizations to run Kubernetes applications.

Jefferey Gregor (General Manager at OVH Cloud) talks about the importance of addressing the “very high learning curve of Kubernetes” by offering less experienced organizations the ability to consume fully managed Kubernetes clusters that reduce the number of services developers need to worry about.

Where to Focus in 2025

In 2025 vendors of Kubernetes clouds and Kubernetes-based platforms need to make it easier for a wide audience to deploy and manage Kubernetes clusters. This requires delivering a spectrum of Kubernetes solutions with differing degrees of pre-integration and flexibility.

Business Impact: 8/10 Very High

Kubernetes is today’s standard pathway toward policy-driven scalability of application environments. No having the ability to leverage Kubernetes often limits an organization’s agility, operational flexibility and scalability.

4. Unified Data Access in Real Time

In 2025, we will see the rise of GraphQL platforms offering a single endpoint for developers to query data sources across the organization and beyond. This requires platform engineers to step up and provide a universal GraphQL interface for developers to work with.

Siloed data sources force developers to use multiple REST APIs, direct database queries, or other protocols. Developers need to manually handle data fetching, transformation, and aggregation for each source. They rely on the API backend to get the data they need and they need to make multiple network requests to run a query across different API endpoints. This can lead to data inconsistencies due to data changes while the query runs. Debugging these inconsistencies and managing the overall complexity of a fragmented landscape of REST APIs at scale can quickly become time consuming for developers.

Advantages of GraphQL queries over Rest API queries:

  • Single REST Endpoint: Instead of multiple calls from the client side, you consolidate everything into /aggregated-users.
  • Hidden Complexity: All the data-fetching (two external APIs, one database query) and the aggregation logic remain on the server.
  • Reduced Boilerplate: Clients simply call one endpoint and get the merged data, rather than juggling multiple requests and merges.

Having a unified AI layer in place brings advantages beyond increased developer productivity, as it allows for centrally applied security policies, RBAC, monitoring, and management of each API.

Where to Focus in 2025

There are five key areas organizations will need to focus on in 2025:

  1. Create a universal GraphQL gateway that consolidates that provides data from multiple sources, such as REST APIs, databases, external services behind one central GraphQL endpoint.
  2. Invest in platform engineering to be able to centralize authentication, authorization, and compliance processes of the new GraphQL layer.
  3. Orchestrate data fetching, transformation and aggregation on the server side instead of burdening the developer to write code to achieve this task.
  4. Offer real-time capabilities to allow developers to subscribe to self-defined data streams.
  5. Centralize observability and management of the GraphQL layer to ensure resilience and optimize resource alloocation.

Business Impact 10/10

GraphQL data platforms are an extension of developer platforms and therefore aim at the same goal: saving developer time. At the same time, GraphQL platforms constitute an excellent foundation for deploying LLMs, as they unify data access under a single query layer and enable flexible retrieval of the context LLMs need — ultimately streamlining how AI-driven solutions are built, tested, and maintained across the organization.

5. Bare Metal, VMs, App Containers and WASM Containers Will Co-Exist

In 2025, we expect to see a concerted effort by organizations to bring consistency into the management and operation of application environments, no matter if they run in the cloud or on-premises and whether they are deployed on VMs or containers. This will lead to a significant consolidation of the application stack, eliminating much of the need for redundant management teams and tools.

Managing traditional VMs and containers through different teams has become a significant pain point for many organizations. This is often amplified by different teams being responsible for cloud infrastructure and for infrastructure located on premises. Then there are often different management tools, observability and monitoring platforms, security and compliance tools, and orchestration and automation tools for each one of the different deployment permutations.

Kalyan Ramanathan, VP of Marketing and Prashant Rathi, Director of Product Marketing at Portworx, talk about the need for a unified set of enterprise data services to efficiently and consistently deliver databases, backup and restore, disaster recovery, archiving, data analytics, data integration, data catalogs, etc. Applying one consistent set of data services to any application, no matter if it runs on bare metal, VMs, application containers, or WASM containers, can significantly lower operational complexity, increase data consistency and cost efficiency, and optimize security and compliance.

Dan Ceruli, product leader at Nutanix talks about Nutanix Enterprise AI as their new platform that simplifies the implementation of AI capabilities for virtualized and containerized applications alike.

Business Impact: 8/10

Running bare metal workloads, VMs, application containers and WASM containers on the same platform can bring a number of key advantages:

  1. Reduced operational silos can lead to the consolidation of staff, tools, and processes.
  2. Consistent data services foster predictable performance, increased data integrity, and enhanced resource utilization.
  3. Having a single management plane frees up time for developers and operators to focus on innovation instead of having to spend time on redundant tasks.
  4. DevOps teams can select the optimal deployment target for each project, without increasing operational complexity.
  5. Having a unified operations platform simplifies the implementation of AI capabilities, due to significantly reduced integration requirements.

Where to Focus in 2025

In 2025 organizations will invest in the unification, standardization, and automation of platform operation:

  1. Choosing a common set of tools for monitoring, observability, orchestration and automation of bare metal, VM-based, container-based and WASM container-based workloads becomes critical.
  2. Organizations will focus on establishing centralized security and compliance policies across any type of application stack.
  3. The focus on standard processes and toolchains will allow simplified application modernization without the pressure of having to migrate away from a legacy platform.
  4. Developers will require unified pieplines and a unified self-service catalog that works across the different types of environments.
  5. AI and machine learning will be used to optimize resource allocation and ensure resilience of unified environments.

Conclusion for Part 1

All of these trending technologies focus on enabling organizations to deliver better software faster, more frequently, and at a lower cost. At the same time, the overarching movement is toward consolidation and simplification of operations. By eliminating redundant tools and processes, companies free up human operators to address critical, policy-driven automation in infrastructure and application lifecycle management — ultimately boosting agility, reliability, and security. As a result, teams can devote more energy to innovation and value creation, rather than getting caught up in repetitive manual tasks.

Part 2 of this article is available here.

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