Enterprise AI agents are composable systems that combine large language models, connectors, memory, and orchestration to automate tasks across business applications. As of May 2026, organizations are evaluating platforms that balance developer control, operational observability, and governance—driven by production deployments, tighter compliance expectations, and a shift from single-model apps to multi-agent workflows. Key categories include AI Agent Marketplaces (no-code/low-code apps and prebuilt agents), Agent Frameworks (developer SDKs and orchestration layers), and AI Automation Platforms (end-to-end deployment, monitoring, and governance). Representative tools illustrate the spectrum: Kore.ai focuses on enterprise multi-agent workflows with governance and observability; LangChain provides open-source SDKs and commercial runtimes for building, testing, and deploying reliable agents; Xilos positions itself as infrastructure for “agentic” activity with visibility across connected services; GPTConsole offers developer-first APIs, CLI and lifecycle/memory features for production-ready agents; JetBrains AI Assistant embeds context-aware copilots inside IDEs; and Anakin.ai targets business users with 1,000+ ready-made no-code AI apps for content, search and automation. Platform selection depends on requirements for security and identity (where vendors such as SailPoint play a role), model sourcing and safety (providers like Anthropic and Claude), and enterprise integration (large vendors including Microsoft). Current trends favor hybrid approaches that combine no-code marketplaces for rapid adoption, open frameworks for customization, and infrastructure that provides auditing, observability, and access controls—ensuring agents are reliable, auditable, and maintainable in production.