Thought leadership

January 6, 2026

Live Keynote: Ensuring Trust Through Governed Agentic AI

min read

Eleanor Treharne-Jones

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This talk was recorded live at The AI Summit New York, where enterprise leaders gathered to discuss what it really takes to put AI into production. The conversation focuses on a challenge we hear consistently: how to move fast with AI agents without exposing organizations to unacceptable data risk. The themes explored here directly informed the launch of AI Guardian, Bigeye’s approach to governing agentic AI at runtime.

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Resource

Monthly cost ($)

Number of resources

Time (months)

Total cost ($)

Software/Data engineer

$15,000

$540,000

Data analyst

$12,000

$144,000

Business analyst

$10,000

$30,000

Data/product manager

$20,000

$240,000

Total cost

$954,000

Role

Goals

Common needs

Data engineers

Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.

Freshness + volume
Monitoring
Schema change detection
Lineage monitoring

Data scientists

Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.

Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing

Analytics engineers

Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.

Lineage monitoringETL blue/green testing

Business intelligence analysts

The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.

Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing

Other stakeholders

Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.

Integration with analytics toolsReporting and insights

about the author

Eleanor Treharne-Jones

Chief Executive Officer

Eleanor Treharne-Jones is Bigeye’s Chief Operating Officer, bringing over 20 years of global leadership experience across the SaaS and data industries. A seasoned executive with a proven track record of driving innovation and scaling high-growth organizations, Eleanor is passionate about empowering teams and advancing the field of data observability.

Before joining Bigeye, Eleanor served as CEO of Kensu, where she led the company’s mission to revolutionize how organizations monitor and ensure the reliability of their data. Her leadership helped accelerate adoption of data observability solutions that reduce issue resolution times and improve data trust.

Previously, Eleanor held senior leadership roles at Odaseva, as Chief Revenue Officer, and at TrustArc, as Senior Vice President, where she built and led high-performing teams across Europe, the U.S., and APAC. Her career reflects her deep expertise in data governance, data privacy, and operational excellence.

about the author

Kyle Kirwan

Chief Product Officer, Bigeye

Kyle Kirwan is Bigeye’s Co-founder and Chief Product Officer. Before launching Bigeye, he worked on Uber’s data platform, where he and fellow Co-founder Egor built the systems used to power analytics and experimentation across the company. Their experience with data quality challenges at that scale inspired them to create Bigeye, a data observability platform designed to make data more trustworthy and easier to manage. Today, Kyle leads product and strategy, focused on building tools that data teams actually love to use.

about the author