Enterprise agent frameworks for finance and accounting (agentic AI) refer to platforms and toolchains that orchestrate LLM-powered agents to automate FP&A, reconciliation, invoicing, audit trails, and compliance workflows. As of 2026-05-14 this topic is timely because widespread adoption in finance increases regulatory scrutiny, demand for auditable decisioning, and the need for governed, private deployments that integrate with ERP and accounting systems. Key categories include developer-first SDKs and platforms (e.g., LangChain) for building, testing, and deploying agent workflows; no-code/low-code visual builders (e.g., MindStudio) to design and operate agents rapidly with enterprise controls; in‑IDE and CI/CD assistants (JetBrains AI Assistant, GitHub Copilot, Windsurf) that keep engineering workflows context-aware; and quality/security-focused code and SDLC governance tools (Matter AI, Qodo, CodeRabbit, Bito, Tabnine) that add code review, test generation, SAST, and policy enforcement. Together these tools address the full stack: model orchestration, observability, testable logic, secure code generation, and enterprise governance. Practical considerations for finance teams include data privacy (self-hosting or private LLMs), auditability (explainable chains of reasoning and immutable logs), validation and simulation in sandboxed environments, multi-model support for specialized tasks, and integration with financial controls. Trends to watch are the proliferation of reasoning‑first agents, automated test generation for agent workflows, and tighter links between code-quality agents and regulatory evidence workflows. Choosing a framework means balancing developer flexibility (LangChain, Windsurf) against productized governance and low-code operational controls (MindStudio, Tabnine, Qodo/Bito) to meet finance’s reliability, compliance, and security requirements.