Private Benchmark
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A private benchmark is a company-specific evaluation suite used to reliably assess, iterate on, and release AI agents against realistic task scenarios. Unlike public benchmarks, it is purpose-built around an organization's actual production environment and is considered a core engineering discipline for any team shipping agents.
Why Every Company Needs One
Rustem Feyzkhanov (Snorkel AI) argues that a private benchmark is the foundational prerequisite for serious agent development: "every company needs a benchmark. It's the only way to reliably evaluate, release, and improve your agents." (0:47) He draws an explicit contrast with public benchmarks, which he regards as useful for "orienting and building priors" but insufficient for production: "your private benchmark is useful to ship." (5:41)
Core Design Principle: Production Fidelity
The benchmark must mirror the real deployment environment as closely as possible—including actual tools, API services, policies, and workflows: "It has to be as close to production as possible." (0:56) This fidelity requirement is what distinguishes a private benchmark from academic or public alternatives.
Components
A private benchmark is composed of several interconnected parts:
- Oracle Solution: Provides the reference or "gold" answer for each task. Feyzkhanov describes it as "an important part of the task." (7:08)
- Verifier: Evaluates agent outputs by analyzing "final state, trace, and artifacts." (9:57)
Part of the Agentic Lifecycle
Private benchmarks are not a one-time artifact but an ongoing operational concern. Feyzkhanov situates them explicitly within Agent Ops: "it has to be part of your agentic life cycle." (1:47) Building and maintaining the benchmark is framed as an "engineering discipline that needs to be built in each engineering team that needs to ship AI agents to production." (13:17)
Anti-Patterns
Feyzkhanov identifies a common failure mode where teams attempt to fix agent issues by adding more instructions to the prompt rather than addressing problems at the correct architectural layer (the harness, skills, or structured outputs). He calls this "a bit of an anti-pattern in the industry," implying that a rigorous private benchmark exposes these issues and forces fixes to be placed appropriately. (14:34)
Points of Disagreement
No speakers in the available material dispute the value of private benchmarks. The implicit contrast is with over-reliance on public benchmarks, which Feyzkhanov positions as complementary orientation tools rather than substitutes for company-specific evals.