From Manual Data Prep to Minutes-Long Automation
AI agents in enterprise software are specialized systems that autonomously perform complex data preparation, quality checks, and workflow automation steps that previously required long, repetitive manual effort by data and operations teams. Instead of analysts writing scripts, reconciling spreadsheets, and stitching together feeds for weeks, AI agents monitor data flows, detect issues, and execute corrections in near real time. This shift is reshaping data preparation automation in mission-critical platforms, turning once-static back-office processes into adaptive, always-on systems. For large organizations, the impact is direct: faster time-to-insight, fewer manual handoffs, and workflow automation software that can respond instantly to new data or edge cases. As these agents are embedded closer to the core of enterprise data platforms, data prep moves from being a project with an end date to a continuous, autonomous capability.
Eon’s Cloud Backup AI Agent: Months to Minutes
Backup and archive environments have long been some of the hardest places to unlock value, because data is scattered across systems and formats. Eon’s AI Agent for cloud backup solutions tackles this by sitting directly on top of existing backup data and enabling natural-language queries without moving information into separate analytics stores. According to Eon, customers using its cloud backup solutions reduce backup costs by 30 to 50 percent while keeping production systems unaffected. SoFi’s experience shows how far data preparation automation has progressed: the company has accelerated data preparation time by more than 90 percent on the platform, turning multi-month extraction and cleanup cycles into workflows that complete in minutes. By resolving the “data access problem” inside backups, AI agents turn what used to be passive insurance into an active, high-value data source.
Addepar’s AI Agents in Financial Data Operations
In financial services, data quality and timeliness drive investment decisions, but the underlying preparation work is complex and error-prone. Addepar is extending AI agents across its platform to address these challenges in daily operations. The company previewed a data operations agent that helps teams identify and resolve data issues more efficiently, cutting down time spent on manual investigation and reconciliation while improving data quality at scale. These agents sit alongside Addison, Addepar’s native AI experience, which now brings together expanded alternatives data, enhanced visualizations, and partner integrations for richer portfolio insights. This combination shows how AI agents enterprise platforms use are moving beyond chat-style experiences into autonomous data management. Rather than waiting for periodic data clean-up projects, firms can rely on agents continuously monitoring feeds, reconciling discrepancies, and keeping analytics-ready data available for portfolio managers and client teams.
Workflow Automation Software Meets Mission-Critical Data
Both Eon and Addepar point to a wider trend: AI agents are being embedded into core workflow automation software, not treated as add-ons at the edge. Addepar’s new connectivity capabilities, APIs, and integrations with CRM, cloud data, and business intelligence platforms show how data agents can coordinate across entire technology stacks, powering analytics and AI at scale. At the same time, Eon’s AI Agent runs queries with built-in security and access controls, so sensitive backups remain governed while becoming more accessible. These advances signal a shift toward autonomous data management in mission-critical systems, where AI agents orchestrate data movement, quality, and access under human oversight. As data preparation automation becomes an always-on service layer, organizations gain faster, safer routes from raw data to decisions, rather than relying on slow project-based integration efforts.
Faster Data Prep, Faster Decisions
Compressing data preparation from months to minutes has a compounding effect on enterprise performance. When AI agents clean, reconcile, and expose data on demand, investment professionals, risk teams, and operations leaders can run more scenarios, respond faster to market changes, and adapt client workflows without waiting for IT backlogs. Addepar’s focus on eliminating friction across the investment lifecycle, combined with Eon’s emphasis on making backup data “instantly usable,” illustrates how AI agents are now core to competitive differentiation. Time-to-insight shrinks as data pipelines become self-monitoring and self-correcting, and operational efficiency rises as manual, repetitive work is replaced with autonomous tasks. For large organizations managing complex portfolios and sprawling data estates, AI agents enterprise platforms provide a practical path from static, siloed data toward dynamic, continuously prepared information ready for action.