Enterprises should run on general intelligence. This is the premise that drives EGI. The question is not whether intelligence will power enterprise operations, but how to select and deploy the right intelligence for each function.
The model fallacy is the assumption that one model fits every function. This fallacy persists because evaluation is often superficial. Models are compared on generic benchmarks that measure general capability, not function-specific performance. A model that excels at reasoning may fail at persistent goal pursuit. A model that handles context well may struggle with tool use under constraints.
Enterprise functions are distinct. Revenue operations require agents that maintain goal persistence across long-running workflows. Product development requires agents that execute multi-step plans with high context retention. Each function has unique agentic requirements that generic benchmarks cannot capture.
EGI operates as an intelligence selection layer. We continuously evaluate hundreds of frontier and open models against enterprise-function-specific agentic benchmarks. Evaluation is not a one-time assessment. New models enter the pipeline continuously. Performance is measured on function-specific criteria. The best-performing intelligence is selected for deployment.
Our evaluation system measures seven dimensions of agentic capability: goal persistence, tool use, multi-step execution, context retention, error recovery, outcome quality, and compliance and safety under constraints. These dimensions are not weighted equally. Different enterprise functions require different capability profiles. Revenue operations prioritize goal persistence and outcome quality. Product development prioritizes multi-step execution and context retention.
The selection process is continuous. Models are evaluated as they become available. Performance data accumulates over time. Selection decisions are made based on current best performance for each function. When a new model outperforms the current selection, deployment is updated. The system adapts to the evolving model landscape.
Selected intelligence deploys into production workflows via flagship agents. Agents are outputs of the selection system. They are not static implementations. They evolve as the selection system identifies better-performing models. Bruce deploys intelligence selected for revenue operations. Alfred deploys intelligence selected for product development. Each agent is optimized for its function.
General intelligence for enterprise is rooted in neuro-symbolic architectures. Neural layers understand natural language, extract intent, and maintain context. Symbolic layers translate understanding into precise code and structured execution. We have refined neuro-symbolic execution architecture for key enterprise workflows. The combination enables reliable execution, full auditability, and compliance with enterprise requirements. This architecture is fundamental to enabling enterprises to run in a generally intelligent way.
The system is the product. The agents are manifestations of the system. Continuous evaluation drives continuous improvement. Function-specific benchmarks ensure that the right intelligence is selected for each function. Neuro-symbolic architectures ensure that intelligence executes reliably in enterprise environments. The model fallacy is avoided through rigorous, function-specific evaluation.
This approach requires discipline. It requires resisting the temptation to default to the latest frontier model. It requires maintaining evaluation infrastructure that can assess hundreds of models continuously. It requires making selection decisions based on data, not hype. It requires accepting that the best model for one function may not be the best model for another.
The result is a system that selects intelligence based on function-specific performance. The result is agents that are optimized for their functions. The result is enterprises that run on general intelligence selected for each function. This is the doctrine of Enterprise General Intelligence.