The Anomaly Detection Tool
Detect outliers and anomalies in a dataset or time series so an agent can surface fraud, faults, and surprises automatically — pointing a human at the few points that need attention.
The data holds the answer — nobody has time to dig
Spreadsheets, databases, and documents are full of answers that stay locked because pulling them out is slow, manual, and skill-bound. And the data is exactly what can’t be handed to a hosted assistant.
Manual analysis
Profiling and querying data by hand doesn’t scale.
Skill bottleneck
Answers wait on the few people who can write the query.
Locked in documents
Tables trapped in files stay out of reach.
Sensitive data
Business data can’t be sent to a third-party service.
Anomaly Detection, without the risk
Capability
What it does
Flag the data points that don’t belong.
it detects outliers and anomalies in a dataset or time series and returns the flagged points.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
detection runs statistical and model-based methods inside your perimeter, returning scored anomalies, so an agent surfaces the exceptions worth investigating rather than combing all the data.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
Analysis runs inside your perimeter, scoped per tenant with audit logging, so an agent can profile, query, and transform sensitive business data without any of it leaving your environment.
Per-tenant, logged
Parameters
The anomaly_detect tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: auto Optional Detection method. autozscoreiqrisolation_forest
default: 0.95 Optional Threshold controlling how aggressive detection is.
How the Anomaly Detection tool works in practice
Anomaly Detection is a data & analytics tool you assign to a VDF AI agent. It detects outliers and anomalies in a dataset or time series and returns the flagged points. Its hallmarks — Anomaly, Outliers, Scored — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, detection runs statistical and model-based methods inside your perimeter, returning scored anomalies, so an agent surfaces the exceptions worth investigating rather than combing all the data. It expects data as required input, so calls are explicit and easy to audit. Every call is scoped to the requesting tenant and written to an audit log, so the capability is safe to run inside a regulated, on-premise environment — the same governance model behind every VDF AI tool.
Teams reach for Anomaly Detection when they need to handle fraud signals, ops monitoring, and quality control. It rarely works alone — pair it with Statistics Tool, Data Profiler, and Read-Only SQL Query to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Anomaly Detection pays back
Fraud signals
Flag transactions that look abnormal.
Ops monitoring
Catch a metric spiking out of range.
Quality control
Surface defects in a batch of readings.
Triage
Point a human at the few points that matter.
Assigned to agents, orchestrated as networks
On VDF AI, an industry’s use cases map to agents, and you assign tools like this one to those agents. Compose multiple agents into a governed, on-premise network.
What changes after you assign it
Questions about the Anomaly Detection tool
What is the Anomaly Detection tool?
It detects outliers and anomalies in a dataset or time series and returns the flagged points. Assigned to a VDF AI agent, it runs under role-based policy with full audit logging so the capability is safe to use in production.
What methods does it use?
Statistical and model-based approaches such as z-score, IQR, and isolation forest, or auto-selection.
Can I tune how aggressive it is?
Yes. The sensitivity parameter controls how readily points are flagged.
What inputs does the Anomaly Detection tool need?
It requires data, and optionally accepts method and sensitivity. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Anomaly Detection?
Anomaly Detection is commonly assigned alongside Statistics Tool, Data Profiler, and Read-Only SQL Query. On VDF AI you compose several tools and agents into a single governed, on-premise network.
Does it run on-premise?
Yes. Like every VDF AI tool, it can run on-premise or in your sovereign cloud, scoped per user and audit-logged, so your data never leaves your perimeter.
How do agents use it?
You assign the tool to an agent under a role-based policy; the agent calls it as one step in a task, and several agents and tools can be orchestrated together as a governed VDF AI Network.
Assign Anomaly Detection to these agents
These VDF AI agents can be assigned this tool. Open an agent to see the full toolkit it can run.
Tools that work well alongside this one
Where this tool delivers value
Put Anomaly Detection to work
See the Anomaly Detection tool assigned to an agent and orchestrated in a governed, on-premise network.