From Monitoring to AI Autonomy on Enterprise Data
Enterprise data platforms AI refers to integrated systems that collect, store, process, and analyze structured and unstructured data while embedding artificial intelligence to automate operations, improve decision-making, and safely connect models, agents, and infrastructure in a unified environment. The shift under way is from reactive monitoring to proactive AI autonomy infrastructure: instead of humans watching dashboards, AI agents observe systems, flag risks, and often remediate issues on their own. At the same time, unstructured data management has moved to the center of the stack because documents, file shares, logs, and media make up the bulk of enterprise information. In this landscape, Datadog, Rubrik, Databricks, and newer platforms are converging on similar goals—AI-powered autonomy and intelligence—but with different architectures for integrating AI with existing observability, security, backup, and analytics foundations. Their choices define how enterprises will build AI agent development workflows, govern data, and control risk at scale.
Datadog: AI-Powered Observability and Security at Platform Scale
Datadog is expanding from observability into AI autonomy infrastructure by launching more than 100 new capabilities at its DASH event, all aimed at helping customers “drive autonomy and manage growing AI and security complexity.” A key move is Bring Your Own Cloud, which deploys Datadog directly into a customer’s environment so data is processed and indexed in their own cloud object storage. This reduces the tradeoff between observability cost and visibility as AI-driven log volumes grow. Bits AI, Datadog’s suite of agents, is also evolving from root-cause investigation into more autonomous incident and development actions, linking detection, evaluation, and infrastructure changes. According to Datadog leadership, the company invests about 30% of revenue into R&D to stay ahead of operational complexity. Architecturally, Datadog keeps AI close to production telemetry, favoring embedded agents and unified dashboards over separate AI stacks.
Rubrik Annapurna: AI-Ready Unstructured Data Without Duplication
Rubrik’s Annapurna aims squarely at unstructured data management, creating what it calls an AI-ready unstructured data layer for enterprise Data Intelligence platforms. Operating on Rubrik Security Cloud, Annapurna auto-discovers, scans, and indexes billions of files across NAS, S3, and other object stores, then publishes a queryable catalog into a lakehouse without moving source files. Rubrik notes that unstructured data accounts for about 90% of modern enterprise footprints, yet organizations often duplicate entire environments into data lakes only to use less than 10% of the data for AI operations. Annapurna inverts this pattern by activating data in place and aligning infrastructure costs to actual consumption. Image_index: 1 is Rubrik Annapurna, but I should not mention that here. For AI agent development and analytics teams, this architecture means they can connect their preferred enterprise data platforms AI tools to a clean catalog of files, metadata, and policies without building heavy ETL pipelines.
Databricks Agent Bricks: A Platform for AI Agent Development
Databricks approaches AI autonomy from the data and analytics side with Agent Bricks, a platform for building high-quality AI agents that can reason over enterprise data. Since launch, customers have built more than 100,000 agents, with Databricks processing over 1 quadrillion tokens per year of agent workloads. The company frames the core agent loop as only 1% of the work; the other 99% lies in deployment, security, evaluation, monitoring, and context. Agent Bricks addresses this by giving developers broad model choice—from proprietary frontier models to open-source and smaller specialized models—tightly integrated within Databricks’ security boundary. It also focuses on context and control, unifying data and AI so agents can retrieve the right information while respecting governance and cost constraints. Architecturally, Databricks turns the data lakehouse into a control plane for AI agent development, linking model selection, retrieval, telemetry, and governance in one environment.
How the Approaches Compare—and What Emerging Players Need
Viewed side by side, these enterprise data platforms AI strategies show different bets. Datadog embeds AI agents directly into observability and security workflows, emphasizing fast detection and autonomous action on live systems. Rubrik builds an unstructured data layer that feeds any downstream analytics or AI stack, focusing on cataloging and activating file estates without duplication. Databricks concentrates on AI agent development itself, turning the lakehouse into a unified platform for models, context, and control. Emerging players must decide where to situate their AI autonomy infrastructure: close to telemetry, close to storage, or close to analytics. They also need clear answers on unstructured data management, model choice, and governance. For enterprises, the comparison is less about picking a single winner and more about assembling a stack where observability, security, backup, and analytics systems cooperate to power reliable, governed AI agents across unstructured and structured data.
