Why this matters for operators
The report describes a shift from one-off chatbots to agentic workflows, heavier data and infrastructure demands, and a sharp split between pilots and production. Those themes align with how AI-Harness is built: measurable workflows, governance-minded execution, and human-led oversight—not generic model demos.
Reproduced from Databricks, State of AI Agents (2026), including cited third-party surveys and Databricks platform telemetry. Definitions, cohorts, and date ranges differ by chart—see the original PDF for full context.
Surveys & cited research
67%
Organizations already leveraging AI-powered tools (MIT Technology Review, cited in report).
>50%
Leaders who see agentic AI as a force multiplier for operations and decision-making (same cited survey).
19%
Organizations that have deployed AI agents—mostly to a limited extent (cited in report).
95%
Generative AI pilots that fail to reach production (2025 MIT NANDA report, cited in report).
40%
Respondents who believed their organization’s AI governance was insufficient (Economist Impact global survey, cited in report).
2%
Respondents rating their org’s AI performance highly on measurable business results (MIT Technology Review Insights, cited in report).
Databricks platform & product telemetry (as stated in report)
327%
Growth in multi-agent workflows in four months (report).
37%
Share of Agent Bricks usage: Supervisor Agent (October 2025).
31%
Share of Agent Bricks usage: Information Extraction (October 2025).
40%
Share of top AI use cases focused on customer experience & engagement (categorized use cases in report).
80%
Databases created by AI agents (Neon telemetry, October 2025 vs. 0.1% in October 2023).
97%
Database branches created by AI agents (same Neon telemetry; 0.1% two years prior).
50K+
Data and AI apps created since Databricks Apps public preview; 250% growth over six months (report).
78%
Companies using two or more LLM families (as of October 2025).
59%
Companies using three or more LLM families (October 2025 vs. 36% in July 2025).
83%
Retail companies using two or more model families (highest among industries called out).
96%
Share of requests processed as real-time vs. batch (Databricks platform, report).
32:1
Technology industry: real-time to batch request ratio (highest among industries).
13:1
Healthcare & Life Sciences: real-time to batch ratio (second highest).
7×
Growth in AI Gateway usage (governance) since January 2025 (report).
12×
More AI projects put into production by companies actively using AI governance (report).
~6×
More AI projects into production for companies actively using evaluation tools (report).
The report cites 327% growth in multi-agent workflows in four months. On Databricks Agent Bricks (June–October 2025), Supervisor Agent reached 37% of usage by October 2025 (the top pattern), and Information Extraction was second at 31%.
Across categorized use cases, 40% of the top set relate to customer experience and engagement; the report stresses automating necessary but routine work (support, onboarding, marketing operations, compliance-adjacent tasks, etc.).
Neon telemetry (cited in the report): AI agents created 80% of databases by October 2025 (vs. 0.1% in October 2023), and 97% of database branches (vs. 0.1% two years prior)—illustrating how agents drive provisioning, testing, and experimentation at machine speed.
It also discusses demand for databases that support high-frequency, programmatic agent patterns—concurrency, branching, and real-time responsiveness—beyond traditional human-paced OLTP assumptions.
As of October 2025, 78% of companies used two or more LLM families; 59% used three or more (up from 36% in July 2025). Retail led among industries at 83% using two or more families.
96% of requests on the Databricks platform were real-time vs. batch. The report highlights industry skew: Technology at 32 real-time requests per one batch request; Healthcare & Life Sciences at 13 to one.
Databricks Apps: 50K+ data and AI apps since public preview, with 250% growth over six months—evidence of “citizen” app creation at scale.
AI Gateway usage (governance) grew 7× since January 2025. Companies actively using AI governance put 12× more AI projects into production; those using evaluation tools get nearly 6× more projects into production—both quoted from the report’s analysis.
That mirrors how we talk about AI-Harness: workflows with clear ownership, measurable outcomes, and guardrails suitable for real operations—not unmanaged autonomy.
Next step
Book a session to map workflows, spend ceilings, and outcomes for your team—grounded in operations, not slide decks.