How Agentic AI Is Shaping the Future of Platform Engineering

As hybrid cloud adoption accelerates, platform engineering is rapidly evolving to meet the demands of workload automation. Traditional, manual approaches are giving way to intelligent, automated workflows — eliminating operational silos and streamlining the management of complex, distributed environments. Agentic artificial intelligence (AI) represents the next leap forward, enabling platform teams to orchestrate resources, policies and processes across on-premises and cloud platforms with unprecedented autonomy and precision. This new era redefines platform engineering, empowering organizations to build, operate and optimize resilient infrastructure while keeping pace with dynamic business application requirements.

Recent trends in platform engineering add agentic AI capabilities to manage both virtual machine (VM) and container workloads across hybrid environments. Instead of simply automating tasks, agentic AI introduces autonomous systems that can proactively reason, plan and execute complex, multi-step tasks with minimal human intervention. This is creating a new paradigm for building and operating platform engineering pipelines.

The current focus is on building AI agents that can manage the entire lifecycle of workloads, including a wide range of platform engineering tasks, from infrastructure provisioning to operational monitoring and maintenance. This includes sophisticated capabilities such as:

• Self-healing and remediation: Agents monitor real-time metrics and logs to detect anomalies and predict potential issues before they cause an outage. They can then automatically initiate remediation actions, like restarting a failed pod, rolling back a deployment or re-ingesting failed data batches.

• Proactive optimization: Agents analyze usage patterns to recommend and implement resource optimizations for both VMs and containers, such as adjusting cluster sizes, rightsizing instances and scheduling batch jobs during off-peak hours to reduce costs.
  
  
• Automated migration and modernization: Agentic AI is accelerating hybrid cloud migrations by analyzing on-premises VM environments, mapping application dependencies, and generating optimized migration plans and Infrastructure as Code (IaC) templates.
   

These sophisticated agents are being created with capabilities that allow them to operate fluidly across environments — whether on-premises, in public clouds (AWS, Azure or GCP) or within hybrid infrastructures — enabled by the adoption of standardized protocols and centralized controls. Open protocols like Agent2Agent (A2A) facilitate communication and collaboration between agents from different vendors and frameworks, allowing complex workflows that span multiple clouds. That means an agent in Azure can coordinate with an agent managing data in Google Cloud, for example.

Meanwhile, centralized control planes powered by AI agents abstract the complexity underlying these environments. They can handle tasks as varied as provisioning Kubernetes clusters in the cloud, establishing private network connections and deploying containerized applications, all while ensuring adherence to organizational policies.

These remarkable capabilities are being woven into the fabric of internal developer platforms, providing developers with more intuitive, conversational, self-service experiences where natural language requests are seamlessly translated into complex, multi-step actions. Agents learn from every interaction, enabling continuous improvement and ensuring consistent governance and standardization across sprawling environments.

Through automated analysis, these agents identify opportunities for efficiency and generate standardized templates — be it for continuous integration and continuous delivery (CI/CD) pipelines, security protocols or documentation — helping teams maintain best practices with minimal manual intervention.

Agentic AI is poised to transform platform engineering from reactive management to anticipatory optimization, empowering organizations to build resilient, innovative, future-ready infrastructure. As they mature, agentic systems are moving toward a multi-agent, collaborative model, where distinct agents specialize in targeted tasks. These specialized agents would operate as a coordinated team, seamlessly pooling their skills to tackle complex challenges.

For instance, one agent might extract data from monitoring tools, another predicts future resource demands, while a third recommends optimal scaling solutions. This division of labor accelerates problem-solving and reduces errors caused by multitasking or human fatigue while simultaneously freeing up platform engineers and developers to focus on strategic innovation rather than routine maintenance.

Despite all this AI autonomy and automation, the human touch remains vital. Major operational changes, especially those affecting production, must be subject to human review and approval. This "human-in-the-loop" approach ensures that while agents drive efficiency and innovation, oversight and safety are never compromised.

Whether you are starting your automation journey, building a platform engineering pipeline or ready to add agentic AI to your existing pipelines, CDW can help you design, build, code and operationalize your infrastructure and workloads.