Databricks State of AI Agents report signals enterprise shift

The Databricks State of AI Agents report points to a clear transition underway in enterprise AI adoption. Organisations are moving beyond isolated chatbots towards coordinated, multi-agent systems that operate across real business workflows, infrastructure, and databases.

Based on insights drawn from more than 20,000 Databricks customers worldwide, including over 60% of the Fortune 500, the report highlights how AI agents are being scaled into production environments rather than confined to pilots.

Multi-agent systems emerge as the enterprise model

One of the most significant shifts outlined in the Databricks State of AI Agents report is the move from single-purpose chatbots to multi-agent systems built on domain intelligence.

According to the findings, the use of multi-agent systems grew by 327% in just four months. These systems are increasingly designed to collaborate, automate decisions, and manage workflows that were previously handled through manual or rule-based processes.

This change reflects a broader evolution in how enterprises view AI—not as an add-on interface, but as an operating layer embedded across functions.

Real-time AI becomes the default

Real-time processing has become central to enterprise AI workloads. The report shows that 96% of AI requests are now processed in real time, supporting use cases such as copilots, customer support, and personalisation.

In Asia Pacific, real-time adoption is especially pronounced, with organisations processing 82% of AI requests in real time. This shift underlines the need for infrastructure capable of supporting low-latency, autonomous AI operations at scale.

AI agents reshape database development

The Databricks State of AI Agents report also highlights how AI agents are increasingly responsible for core database activities.

Key findings include:

• 80% of databases are now built by AI agents.

• 97% of database testing and development environments are built by AI agents.

This trend is driving demand for AI-ready databases designed to handle autonomous, real-time AI workloads rather than traditional query-based operations.

AI moves deeper into critical workflows

The report indicates that most generative AI use cases are now focused on automating routine but essential tasks. Around 40% of these use cases are tied to customer experiences.

In the Asia Pacific region, market intelligence and strategic analytics have emerged as the leading AI-driven applications, reflecting a shift towards data-led decision-making at the leadership level.

At the same time, organisations are adopting a more flexible approach to AI models. The report shows that 78% of organisations use two or more AI model families, while nearly 60% use three or more.

Governance and evaluation drive production success

As AI systems scale, governance and evaluation are becoming critical enablers of production deployment.

According to the report:

• Organisations using AI evaluation tools put nearly six times more AI projects into production.

• Organisations with AI governance frameworks deploy over twelve times more AI projects into production.

• Investment in AI governance has grown sevenfold in nine months.

These findings suggest that quality controls and oversight are no longer optional but foundational to enterprise AI maturity.

Industry perspective

Commenting on the findings, Nick Eayrs, Vice President, Field Engineering, Asia Pacific and Japan, Databricks, said, “Across Asia, we’re already seeing a decisive shift from pilots to production, with organisations embedding AI agents across critical workflows, infrastructure and databases. Governance and evaluation are emerging as early signals of those scaling with confidence.”

He added that long-term success will depend on strong data foundations, clear ownership, and a disciplined focus on scaling proven AI use cases.

Conclusion

The Databricks State of AI Agents report captures an inflection point in enterprise AI adoption. Multi-agent systems, real-time processing, AI-built databases, and governance-led scaling are no longer emerging trends—they are becoming the baseline for organisations serious about operationalising AI at scale.

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