Bigeye Launches Cost Anomaly Detection to Catch AI Agent Cost Spikes and Silent Failures on a Daily Schedule

New: Agent Trust Hub automatically flags AI agent spend by agent, user, and workspace based on daily evaluations with a direct line into conversations

SAN FRANCISCO, CA, UNITED STATES, August 19, 2026 /EINPresswire.com/ — Bigeye, the Enterprise AI Trust company, today announced the launch of Cost Anomaly Detection, a new capability within its Agent Trust Hub that automatically flags when an AI agent, user, or workspace starts spending unusually more on AI usage— or unusually less — than its own history says it should. The feature closes a gap that has persisted even for organizations with full cost attribution: knowing not just where AI spend went, but when it changed, and why.

As organizations move AI agents into production faster than they can govern them, cost is often the first place the strain shows. According to the EY US AI Pulse Survey (Wave 5, July 2026), 82% of senior leaders whose organizations are investing in AI are concerned about token usage and related costs, highlighting the urgent need for robust monitoring and budget guardrails.

Bigeye already gives customers one centralized, estimated-cost view of every AI agent conversation it observes, across every platform in use. Until now, no one had an automated way to catch when that trend changed. Cost Anomaly Detection evaluates daily AI cost activity for every agent, user, and workspace against an expected range derived automatically from that entity’s own history — with nothing to configure and no budget model to build first. When spend moves outside that range in either direction, it surfaces as an insight card on the homepage and the Agent Trust Registry, linking directly to the conversations behind the change.

Because detection runs in both directions, it catches two very different failure modes: an agent that slips into a retry loop or an unexpectedly chatty conversation pattern and starts costing well outside its normal range, and an agent whose cost quietly drops to zero because a credential expired, a scheduler stalled, or an integration silently failed — a change that can look like a savings for days before anyone notices the work stopped.

The feature is built on Bigeye’s expertise in anomaly detection for data observability, extending the same trusted logic used to monitor massive enterprise datasets to the world of AI spending. Anomalies inherit the same workspace- and agent-level permissions already configured in the platform and live alongside the Agent Trust Registry, so a flagged anomaly opens into the same agent view teams already use: the data an agent accessed, and the quality and freshness signals already tracked on those tables. Teams can also ask Bigeye’s bigAI chat plain-language questions, such as “which agent cost the most this week?” or “what did we spend querying the Orders table?”, and get a ranked answer immediately, in the same place they already work.

By tracing spend down to specific table queries through access-decision records, Bigeye provides unique dataset-level cost visibility across both modern cloud warehouses and legacy systems. This matters because it allows organizations to identify exactly which data assets are driving AI costs, enabling precise budget optimization and ensuring that high-value agents are running on the most cost-effective infrastructure.

“Agents are moving into production faster than anyone can govern them, and cost is usually the first place that shows,” said Eleanor Treharne-Jones, CEO of Bigeye. “It’s the difference between finding out about a runaway or a broken agent from next month’s invoice, and finding out from Bigeye — in time to do something about it.”

Gartner has reported that when one large enterprise moved from simply publishing cost recommendations to an automated system that detected and routed them, adoption jumped from a historical 20%–38% to 88% (G00852511) — evidence that pairing detection with a direct path to action, not visibility alone, is what changes behavior.

Cost Anomaly Detection began rolling out to Bigeye’s existing Agent Trust Hub customers on Aug. 18, 2026. Organizations that want to see the solution running against their own workspace can book a demo at bigeye.com.
Read more about why cost visibility alone isn’t complete AI governance on the Bigeye blog: [https://www.bigeye.com/blog/ai-cost-anomaly-detection]. For a walkthrough of how detection works and what an insight card means, see the Bigeye Help Center: https://docs.bigeye.com/.
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About Bigeye

Bigeye is the AI Trust Platform for the enterprise. It traces the data AI agents actually touch and connects it with critical context, including classification, lineage, quality, and ownership, so companies can understand whether their AI is operating on trusted, appropriately governed data. Building on deep expertise mapping and monitoring data across the enterprise, Bigeye gives organizations visibility into what each agent accesses and automatically enforces policy to keep agents within approved data boundaries.
Bigeye works across the broadest range of modern and legacy systems and serves some of the world’s largest enterprises, including USAA, Zoom, Hertz, and Cisco. Every day, it monitors 3 million customer datasets, maps 100 million lineage connections, and scans 3 trillion rows of data.

Patricia Miron
Bigeye
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