AI agent governance and trust platforms address the practical need to manage, verify and monitor autonomous or agentic software that uses LLMs and connected services. As enterprises deploy multi-agent workflows, sales assistants, and embedded LLMs, governance shifts from one-time audits to continuous runtime controls: verified skills and attested capabilities, automated risk scanning for data exfiltration and misbehavior, and standardized trust metadata for provenance and auditability. This topic sits at the intersection of AI Governance Tools and AI Security Governance, combining policy enforcement, observability, and secure model infrastructure. Key platforms illustrate how the stack is evolving: Kore.ai focuses on building, deploying and orchestrating multi-agent workflows with embedded governance and observability; Xilos positions itself as enterprise agentic infrastructure offering high visibility into connected services and agent activity; Cohere provides private, customizable LLMs, embeddings and retrieval primitives that underpin verifiable skills and controlled inference; Notion represents how workspace and knowledge layers integrate trust metadata and automations; specialist agents like Pria AI (sales dialer) exemplify domain-specific workloads that require fine-grained attestations and runtime risk monitoring. Trends through 2026 emphasize continuous risk scanning, machine-readable trust metadata attached to skills and connectors, cryptographic or policy attestations for provenance, and tighter integration between model providers, orchestration platforms and enterprise knowledge stores. Effective governance implementations combine registries of verified skills, dynamic policy engines, telemetry-led observability, and incident-ready controls—making agent deployments auditable, interoperable and safer without relying on ad-hoc manual reviews.