Zavora: An AI-Powered Agentic ERP System with Commitment-Driven, Event-Sourced Architecture for Governed Autonomous Business Execution

Enterprise Resource Planning (ERP) systems integrate finance, sales, inventory, procurement, human resources and reporting, yet most of them remain passive data-entry platforms in which every workflow step still depends on a human operator reading a screen and clicking a button. This makes execution slow, inconsistent and dependent on staff availability, and it leaves weak, fragmented evidence of why a particular decision was made. Recent advances in Large Language Models (LLMs) allow software agents to reason over business data and call systems directly, but granting autonomous agents control over real business and financial actions is risky unless the platform enforces policy, traceability and auditable commitments at every step. This paper presents Zavora, an AI-powered agentic ERP system in which agents are treated as first-class operators rather than as chat assistants bolted onto a legacy system. Zavora combines a Coordinator Agent and role-restricted specialised agents (HR, Finance, Sales, Inventory, Analytics, Reporting and Notification) with a commitment-driven, event-sourced core in which every significant action is stored as an immutable commitment, obligation, proof or settlement event. Entity state is derived by replaying event streams, which supports financial determinism and a fully replayable audit trail. Governance is enforced through approval thresholds, escalation rules, policy validation and the ability to freeze autonomous actions. The operational cost of AI autonomy itself (tokens, cloud infrastructure and subscriptions) is tracked as a first-class accounting concern through agent payroll and cost allocation. The prototype is realised as microservices over PostgreSQL and Redis and covers the autonomous lead-to-opportunity-to-quote-to-order-to-fulfilment-to-finance-to-settlement lifecycle. The paper describes the requirements, architecture, data and event model, agent design, governance and security model, implementation plan with code-level design, testing and evaluation strategy, risk analysis and ethical considerations, and offers a reference architecture for policy-controlled autonomous ERP execution.