AgentShelf is an AI operations platform designed to help businesses integrate AI without code or uncontrolled LLM spending. It provides a centralized solution for building, deploying, reusing, and governing specialized AI agents. The platform offers features such as an AI operations workflow, a no-code agent builder, approved context and tools, and usage and cost governance. AgentShelf aims to turn AI sprawl into reusable business AI operations by addressing fragmented tools, unmanaged model access, and trapped workflows. The platform includes an AI Front Desk for customer-facing interactions and individual agent workspaces for internal use. It also features a control plane for shared services, a builder platform for launching agentic workflows, and robust cost governance with budget policies, LLM gateway, and value attribution. AgentShelf is built for public-facing and private business AI boundaries, offering features like public/private boundary separation, observability into request traces and token usage, and admin controls for model access and budgets.

• Build: Define agent instructions and approved context.

• Deploy: Launch agents on websites, workspaces, and via API.

• Govern: Manage budgets, access, and usage traces.

• Reuse: Utilize a shared workflow library.

Key features include:

• No-code agent builder

• Approved context and tools

• Usage and cost governance

• Website, workspace, and embedded experiences

• Cost governance with budget policies, LLM gateway, and value attribution

• Public/private boundary separation

• Observability and admin control