Seven Autonomous AI Agents
The definitive reference catalog for every Contivra AI Agent. Understand what each agent does, what it consumes and what it produces.
Discovery Agent
Always-on asset discovery across your entire data estate
Purpose
The Discovery Agent continuously crawls every connected data source to identify new assets, detect schema changes and surface previously unknown datasets. It operates on configurable schedules and triggers, ensuring the Enterprise Context Layer is always current—without requiring manual cataloging effort from any team.
Responsibilities
- Crawl all connected data sources on configurable schedules and event triggers
- Detect net-new assets and register them in the Enterprise Context Layer
- Identify schema changes and surface change events to downstream agents
- Generate initial classification and domain tagging suggestions
- Monitor connector health and alert on connectivity failures
- Maintain discovery lineage—recording when and how each asset was found
Inputs
Outputs
Key Capabilities
Classification Agent
Automated sensitivity, PII detection and domain tagging at enterprise scale
Purpose
The Classification Agent applies PII detection, sensitivity labelling and domain classification to every asset discovered by the Discovery Agent. It uses a combination of pattern matching, policy rules and LLM reasoning to classify at column level—and routes exceptions requiring human judgment to the steward queue.
Responsibilities
- Detect PII and sensitive data patterns at column level across every discovered asset
- Apply sensitivity labels (Public, Internal, Confidential, Restricted) based on policy definitions
- Assign domain classification using semantic taxonomy and LLM inference
- Re-classify assets automatically when policy definitions are updated
- Route classification exceptions and low-confidence results to steward review queues
- Maintain classification history and version audit trails
Inputs
Outputs
Key Capabilities
Documentation Agent
Business-friendly asset descriptions and usage guidance, generated at scale
Purpose
The Documentation Agent eliminates the documentation debt that accumulates in every enterprise data estate. It generates plain-language descriptions, usage guidance and example queries for every asset—drawing on schema, sample data and domain context from the knowledge graph to produce documentation that is accurate, relevant and immediately useful.
Responsibilities
- Generate plain-language descriptions for every table, view, column and dataset
- Create persona-appropriate usage guidance for analysts, scientists and architects
- Suggest example queries and data access patterns based on schema and usage history
- Enrich assets with domain context and related concept references from the knowledge graph
- Flag assets with insufficient or low-quality documentation for steward attention
- Regenerate documentation automatically when schemas change
Inputs
Outputs
Key Capabilities
Governance Agent
Real-time policy compliance monitoring, enforcement and violation alerting
Purpose
The Governance Agent makes governance a continuous, automated process rather than a periodic manual audit. It monitors every data access event against defined policies, flags violations in real time and produces the audit trails that regulators, compliance teams and external auditors require.
Responsibilities
- Monitor every data access event against defined access and classification policies in real time
- Detect and alert on policy violations with severity scoring and escalation paths
- Enforce data retention, classification and quality policies automatically
- Generate GDPR, HIPAA, SOX and CCPA compliance audit trails
- Propose remediation steps for flagged violations with priority guidance
- Notify asset owners and stewards when governance events require attention
Inputs
Outputs
Key Capabilities
Quality Agent
Continuous data quality monitoring across five dimensions
Purpose
The Quality Agent profiles datasets, scores quality across five dimensions and monitors for statistical drift—ensuring that every data product consumer always knows the trustworthiness of what they are consuming. It surfaces quality issues before they impact downstream AI models, dashboards or business decisions.
Responsibilities
- Profile datasets for completeness, accuracy, freshness, uniqueness and validity
- Score quality against defined SLAs and flag breaches with owner notification
- Establish statistical baselines and monitor for data drift against them
- Surface quality trends and identify assets at risk of SLA breach
- Alert data product owners when quality scores approach breach thresholds
- Generate quality improvement recommendations with prioritised action lists
Inputs
Outputs
Key Capabilities
Build Agent
Automated data product assembly, documentation and publication
Purpose
The Build Agent reduces the engineering effort required to publish governed, certified data products. Given a product specification, it assembles the product from governed source assets, validates quality requirements, generates documentation and registers the product in the Data Product Registry—following defined contracts and SLAs throughout.
Responsibilities
- Assemble data products from governed source assets following product specifications
- Validate quality requirements and SLA thresholds before publication
- Generate product documentation, schema definitions and data contracts
- Register published products in the Data Product Registry with version metadata
- Monitor published products against their contracted SLAs continuously
- Notify product owners and subscribers of version changes and quality events
Inputs
Outputs
Key Capabilities
Impact Agent
Downstream blast-radius analysis before any change is applied
Purpose
The Impact Agent analyses the downstream blast radius of any proposed schema change, policy update or classification revision before it is applied. By traversing the full lineage graph, it identifies every affected asset, data product, AI model and dashboard—enabling teams to make informed decisions about changes that could otherwise cause silent, cascading failures.
Responsibilities
- Traverse the full lineage graph to identify all downstream dependants of a proposed change
- Score impact severity based on asset type, consumer criticality and dependency depth
- Generate a prioritised impact report with affected asset inventory
- Alert owners of affected assets, data products and AI models
- Suggest mitigation paths for high-severity impacts
- Maintain a history of impact assessments for governance and audit purposes
Inputs
Outputs
Key Capabilities
Get Started
Ready to Build Trusted
Enterprise AI?
See how Contivra transforms your fragmented enterprise data into a foundation for AI you can actually trust.