This topic examines leading enterprise AI agent platforms and their role in regulated, data-sensitive domains—particularly finance and healthcare—as of 2026-01-30. Enterprise agents combine conversational LLMs (for example, Anthropic’s Claude family) with engineering frameworks, data integrations, and operational controls to automate workflows, assist in meetings, and support contact centers while meeting governance and compliance requirements. Key platform categories include AI automation platforms and low-code/no-code builders (StackAI, MindStudio), agent engineering frameworks and libraries (LangChain), agent marketplaces and managed services (Crescendo.ai), and specialized contact-center/voice platforms (Observe.AI, Yellow.ai). Emerging technical patterns include neural + symbolic stacks (Tektonic AI) to improve explainability and rule compliance, and centralized data-plane integrations—most notably Snowflake—for secure, governed access to enterprise data that agents use to reason about transactions, patient records, or risk models. Microsoft Teams agents represent a practical delivery layer for meeting assistants and workflow bots that surface real-time summaries, action items, and structured handoffs into downstream systems. The market is shifting from proof-of-concept pilots to production deployments that emphasize evaluation, QA, human-in-the-loop escalation, and role-based data access. Decision factors for finance and healthcare adopters now center on data governance, auditability, latency, integration breadth (Snowflake, CRM, EHR), and the availability of domain-specific validation (managed services or expert-in-the-loop models). This overview aims to help technical and procurement teams compare tradeoffs between turnkey agent services, engineering-first frameworks, and hybrid managed models when selecting platforms for regulated enterprise use cases.