Enterprises should run on general intelligence—but intelligence must arrive as agents that are pre-trained, benchmarked, and ready from Day 1. The question is not whether agents will power enterprise operations, but whether those agents can perform standardized functional work before they enter production.
The customization fallacy is the assumption that every deployment starts from a blank slate. Teams spend months tuning generic agents on proprietary data, hoping they will eventually handle revenue, finance, or operations workflows. Without standardized jobs-to-be-done and benchmark scores, there is no objective measure of readiness.
Enterprise functions are distinct. Revenue operations require persistent goal pursuit across long-running customer relationships. Product development requires multi-step execution with high context retention. Each function has standardized jobs-to-be-done—measurable outcomes that define what the work is and what success looks like.
EGI benchmarks are empirical: agents must successfully run the same functional job expectations at multiple businesses in diverse verticals. The method is subjective—each job is judged against a defined expectation—but the signal is cross-context replication, not a single demo. Benchmarks are function-specific—a revenue job is not a product job.
Pre-training means the agent arrives ready for functional work. Benchmarking means you know its performance before deployment. Day 1 readiness means you deploy in days—not the months-long implementation cycle typical of generic enterprise AI. These three properties define what EGI delivers.
We started with Bruce for revenue and Alfred for product. Today, the same pre-trained, benchmarked agent model runs across operational functions—finance, purchasing, warehouse, operations, retail, and more. Each agent is scored on standardized JTBD and built to run from Day 1.
Under the hood, neuro-symbolic architectures combine neural inference with symbolic code generation. Neural layers understand intent and maintain context. Symbolic layers translate understanding into precise, auditable execution. This architecture enables reliable Day 1 operation in enterprise environments with full traceability and compliance.
The benchmark is the product. The agent is the manifestation. Standardized JTBD define the work. Pre-training and benchmarking define readiness. Day 1 execution is the objective. This is the doctrine of Enterprise General Intelligence.