Multi‑AI agent orchestration and integration frameworks center on running, coordinating and instrumenting multiple AI agents so they can safely and reliably act on real systems. As of 2026, adoption is converging on standardized interfaces—most notably the Model Context Protocol (MCP)—that let agent kernels mount modular MCP Servers to access databases, SaaS, version control, browsers and cloud services without bespoke plumbing. This ecosystem includes hosted integrators like Pipedream (connects thousands of APIs and prebuilt tools for event‑driven automations), cloud‑native MCP Server offerings from AWS (production patterns and MCP servers tuned to AWS environments), and vendor solutions such as Fujitsu’s multi‑AI agent initiatives and Unicorne on AWS for scalable, cloud‑deployed agent runtimes. Specialized MCP Servers expose concrete capabilities: GitHub’s MCP Server for repository and issue management, Atlassian’s server for Confluence/Jira, Supabase and the MCP Toolbox for Databases for data access and pooling, Playwright MCP Server for browser automation, and Agent TARS/Browser MCP for multimodal agent kernels that operate across terminals and browsers. Kiln integrates MCP into task orchestration to chain tools and workflows. The practical payoff is modularity—teams can mix agent kernels, policy layers, observability and existing SaaS via standard connectors—and faster tool development because servers handle auth, pooling and semantics. Key challenges remain: identity and permissioning across MCP Servers, latency and state management for long‑running agents, and operational observability. For organizations building production agent fleets, these frameworks offer an interoperable layer for integrating AI action with cloud platform integrations and tool integrations while forcing attention to security, governance and runtime economics.