White Paper

Evals in Production

Lessons from Building AI Agent Evaluation Frameworks

Drawing on agentic AI deployments across financial services, life sciences, investment management, and technology sectors.

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Why this paper exists
Most enterprises are not failing at AI because of the technology. They are failing because nobody is measuring whether it works.
This paper is the product of eight real deployments and the kind of operational detail that only comes from having actually shipped AI in production, in banking, pharma, investment management, and beyond.
Inside the paper
01

The evaluation landscape

Three phases of production evaluation, output-type complexity, and the closed-loop model.
02

Case studies in enterprise evaluation

Proxy voting intelligence, pharma-grade platforms, cybersecurity remediation, NL2SQL, and RBAC-aware RAG.
03

Tooling decisions and trade-offs

Build vs. buy analysis, LLM-as-judge strengths and failure modes, and adversarial red teaming.
04

Emerging principles for enterprise AI evaluation

Six cross-cutting patterns distilled from all eight engagements.
05

Sample evaluation report

Anonymized Agentic AI Evaluation Dashboard covering 5,090 test cases across three platforms.
Industries covered
●
Healthcare, Life Sciences and Pharma
●
Banking and Financial Services
●
Technology and Hitech
Who should read it
Senior leaders past the pilot phase asking harder questions. Why is adoption stalling, where is the value, and what does it take to run AI at enterprise scale in a regulated environment.
Who wrote this
Author

Shivkumar Krishnan

AI Strategy & Innovation Leader, Altimetrik
Contributing Architects and Technical Leads
Martin Gaida
Sathiyanarayanan Gopal
Atharva Joshi
Konrad Jackowski
Sandeep Kalra
Pavan Muthozu
Yasmeen Sultana
Mahesh Varavooru

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