Generative AI is reshaping digital platforms. Previously, cloud programs focused on migration, stability, and cost. Now, leaders seek intelligence, automation, and proactive experiences. This is where Generative AI and agents come in — and why modern cloud platforms must evolve again.
Many companies believe GenAI is simply calling a large language model through an API. In reality, GenAI is not just a feature you add on top of the platform. It becomes a new layer in the architecture. It changes how systems are built, how data flows, and how operations work. In our Intelligent Cloud Modernisation for AI-Native work at Endava, we describe this as moving from “cloud-hosted” to “cloud-modernised” and then to “AI-Native”. The difference is important: migration puts workloads in the cloud, modernisation changes how they behave, and AI-Native embeds intelligence into the platform itself.
A good way to explain it is to compare AI-enabled versus AI-native. AI-enabled means adding a chatbot, summarizing documents, or generating content. AI-native is when intelligence is embedded end-to-end: data is ready, workloads scale, governance is built in, and the platform evolves continuously. Agents accelerate this shift. They don’t just generate text. They reason, plan, and act. They can call tools, orchestrate workflows, and make decisions inside defined guardrails. This is why agents reshape modern cloud platforms: they force the platform to become smarter, more connected, and more controlled.
The first big change is the need for strong context. A model without context is just a general assistant. Enterprise GenAI requires Retrieval-Augmented Generation, where the model retrieves knowledge from internal sources before generating an answer. This emphasizes the importance of a robust data foundation. In Microsoft's ecosystems, Microsoft Fabric exemplifies how platforms are evolving to meet this need. The main benefit of Fabric is that it unifies lakehouse storage, analytics, and governance, enabling organizations to build reliable data products that GenAI can use more effectively. When structured data, unstructured documents, and metadata are centrally available, RAG becomes more practical and less fragile.
The second change is integration. Traditional platforms used APIs and microservices. Now, agents use tool calling and workflow orchestration. Agents require permissioned access, controlled execution, and audit trails. This is essential for trust. In Azure, organisations often use Azure OpenAI with Azure AI Search, and orchestrate agent workflows with Prompt Flow or AI Studio, triggered via Functions or Event Grid. Microsoft Fabric provides curated data and real-time analytics for agents' context and decision-making.
The third change is how we operate systems. GenAI introduces new operational metrics: token consumption, prompt latency, retrieval latency, hallucination rate, and model drift. This is why playbooks are important; we strongly push for the Build–Run–Evolve cycle. AI solutions cannot be “built once.” They must evolve. Modern cloud platforms need MLOps for model lifecycle, but also AIOps and FinOps to control reliability and cost. Without this, agents can become expensive very quickly, and their behaviour becomes unpredictable.
The final change is governance. Agents can take actions, and actions create risk. A mature platform needs security, privacy, compliance, and responsible AI built in from day one. It must track lineage, manage access, and enforce policies. Microsoft’s ecosystem supports this with Purview, Defender for Cloud, and policy controls that help organisations keep AI solutions safe and auditable.
Generative AI and agents are reshaping cloud platforms, demanding a new, elastic, data-driven, automated, and governed foundation. To move beyond demos, we must modernise the platform and data layer—especially with tools like Microsoft Fabric—so GenAI becomes a scalable business capability. Start modernising to unlock the full value of AI.
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