Enterprise integration is undergoing a paradigm shift from deterministic, workflow-driven orchestration toward autonomous, AI-driven execution models. While recent advances in large language models (LLMs) and agentic AI enable intelligent task planning and execution, existing approaches lack the governance, interoperability, observability, and resilience required for deployment in enterprise environments. This gap limits the adoption of agentic systems in mission-critical and regulated domains. This paper presents a governed multi-agent architecture for API-driven enterprises, designed to bridge the gap between autonomous decision-making and enterprise-grade control. The proposed architecture integrates a multi-agent orchestration model with a centralized governance plane, enabling identity-aware, policy-driven, and risk-aware execution of agent actions. A unified interoperability layer abstracts interactions with heterogeneous enterprise systems through API gateways, protocol adaptation, and event-driven communication, ensuring scalability and flexibility. To support operational reliability, the architecture incorporates an end-to-end observability framework that captures logs, metrics, traces, and decision-level telemetry, enabling full lifecycle visibility and decision traceability. Additionally, a structured failure taxonomy and adaptive recovery framework is introduced, supporting context-aware failure classification and dynamic recovery strategies, including retry, rollback, and human-in-the-loop escalation. The proposed approach is evaluated across multiple enterprise scenarios and compared with single-agent and workflow-based systems using metrics such as task completion success rate, policy compliance, latency, error rate, and mean time to recovery. The results demonstrate that governed multi-agent systems achieve higher reliability, improved compliance, reduced recovery time, and enhanced observability, while maintaining scalability and operational efficiency. The findings highlight the potential of governed multi-agent architectures to enable secure, scalable, and resilient enterprise integration, providing a practical foundation for deploying agentic AI systems in real-world enterprise environments.

Agentic AI Multi-Agent Systems Enterprise Integration API Architecture Governance Observability Failure Recovery iPaaS