Agentic benchmarks measure how well models perform as agents in real-world workflows. Unlike general capability benchmarks, agentic benchmarks evaluate models on dimensions that matter for autonomous operation: goal persistence, tool use, multi-step execution, context retention, error recovery, outcome quality, and compliance under constraints.
These benchmarks are function-specific. A benchmark for revenue operations measures different capabilities than a benchmark for product development. The same model may perform differently across functions because each function has distinct agentic requirements.
Enterprise functions are distinct operational domains within an organization. Revenue operations include sales, customer success, and revenue optimization. Product development includes engineering, design, and product management. Each function has workflows that require specific agentic capabilities.
Functions are not defined by departments. They are defined by the nature of the work. Revenue operations require persistent goal pursuit across long-running customer relationships. Product development requires multi-step execution of complex technical tasks. Support operations require error recovery and compliance under constraints.
The intelligence requirements for each function differ. A model that excels at revenue operations may not excel at product development. This is why function-specific evaluation is necessary. Generic benchmarks cannot capture these differences.
Evaluation is continuous. New models enter the pipeline as they become available. Each model is evaluated against function-specific benchmarks. Performance data accumulates over time. Selection decisions are made based on current best performance for each function.
When a model outperforms the current selection for a function, it is selected for deployment. The deployment process updates the agent that serves that function. The agent begins using the newly selected intelligence. The system adapts to the evolving model landscape.
This process ensures that each function runs on the best-performing intelligence available. It ensures that agents are optimized for their functions. It ensures that enterprises benefit from continuous improvement in model capabilities.
The evaluation system is the foundation of the selection layer. Without rigorous, function-specific evaluation, selection would default to generic assumptions. The model fallacy would persist. Functions would run on suboptimal intelligence.
Function-specific benchmarks simulate real enterprise workflows. Revenue benchmarks test goal persistence across long-running sales cycles with multiple stakeholders. Product benchmarks test multi-step technical execution with context retention.
Benchmarks are validated against production agent performance. They measure capabilities that matter for autonomous operation, not just general language understanding.
Models are evaluated in controlled environments that simulate production conditions. Each model runs through function-specific benchmark suites. Performance is measured across all seven dimensions with weighted scoring.
Evaluation is automated and scales to assess hundreds of models. New models enter evaluation within days of release. Results are stored for comparison and trend analysis.
Selection is based on weighted performance across seven dimensions. Different functions weight dimensions differently. Revenue operations prioritize goal persistence and outcome quality. Product development prioritizes multi-step execution and context retention.
Selection decisions require statistical significance. Performance thresholds must be met. The best model for one function may not be the best for another.
The ability to maintain focus on objectives across long-running workflows and interruptions.
The ability to select and use appropriate tools to accomplish tasks.
The ability to plan and execute sequences of actions to achieve complex goals.
The ability to maintain and utilize relevant information across extended interactions.
The ability to detect, diagnose, and recover from failures and unexpected conditions.
The ability to produce results that meet function-specific quality standards.
The ability to operate within defined boundaries, policies, and safety requirements.