AI Agent Governance & Safety Tools focus on controlling, monitoring, and certifying the behavior of autonomous and multi‑agent systems used in enterprises. As organizations embed agents into workflows, governance needs shift from model‑level controls to run‑time orchestration, audit trails, data access controls, and policy enforcement. By 2026 this topic is timely: widespread multi‑agent deployments, tighter regulatory expectations (risk assessments, explainability, data residency), and demand for operational observability make governance and safety tools core infrastructure. Reco AI Agent Governance is positioned as a governance layer for agent fleets; competing solutions illustrate complementary approaches. CrewAI targets developer‑built multi‑agent orchestration with an open Python framework and visual editor for building “crews” and flows. Cimba.AI emphasizes no‑code agent creation with built‑in auditability so analysts can safely ship domain agents. Model and platform vendors such as Mistral AI and Cohere supply enterprise‑grade foundation models and private deployment options that affect governance choices (privacy, fine‑tuning controls, and model provenance). Cloud platforms like Vertex AI provide end‑to‑end managed tooling for model lifecycle, monitoring, and policy enforcement, while Microsoft 365 Copilot represents integrated assistant use‑cases where governance must span apps and enterprise data ecosystems. Key considerations when comparing Reco to competitors include orchestration and policy enforcement across agents, immutable audit logs, data access and residency controls, model provenance and versioning, integration with existing MLOps and security stacks, and usability for developers and non‑technical operators. Organisations should evaluate governance tools on their ability to provide traceability, runtime safety controls, and regulatory evidence without impeding agent productivity.