Foundation models should be replaceable inference components. Whether a swap preserves execution is an empirical question. The hard problem is translating reasoning into correct actions across heterogeneous systems—ERP transactions, supply-chain state transitions, and governed treasury operations.
Enterprises should run on general intelligence—but intelligence must arrive as agents that are pre-trained, benchmarked, and ready from Day 1. Enterprise RAG and long-context copilots do not qualify: they retrieve or paste, they do not complete jobs. The question is not whether agents will power enterprise operations, but whether those agents can perform standardized functional work before they enter production—and whether that work remains correct in a paired evaluation when the model underneath is swapped.
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 post POs, journals, or close. Without standardized jobs-to-be-done and benchmark scores, there is no objective measure of readiness. Without an execution graph, there is no product independent of the current inference API.
Enterprise functions are distinct. Purchasing requires signal-to-PO completion under legal entity and policy. Finance requires close and reconciliation that other teams will treat as the books. Warehouse requires inventory that matches what purchasing posted. 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 purchasing job is not a close job. They score actions, not answers.
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 as the enterprise execution agent. The serious work is ERP: posting, receiving, and closing on unlike stacks—NetSuite is not SAP is not Dynamics. Revenue is a production surface on the same layer, not the exam that proves seriousness. Today the same pre-trained, benchmarked execution runs across purchasing, finance, warehouse, operations, retail, and more. Each job 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. The context graph, execution graph, evaluation layer, reusable workflow patterns, and control plane remain stable as the inference model changes. EGI isolates inference behind a versioned execution layer, then reruns the same job contracts when the model changes. Operational interchangeability is established by paired completion, safety, and recovery results—not by a shared API shape.
Standardized jobs define the expected outcome. Pre-trained workflow patterns provide a starting point, and production benchmarks establish readiness across unlike tenant environments. Bruce is the enterprise execution agent that applies those controls to live ERP work while keeping the inference model replaceable.