Agentshelf
Build, govern, & share specialized AI agents without code
AgentShelf helps businesses build, run, and manage specialized AI agents without writing code or losing track of model costs.
Most companies start using AI with one-off chatbots and disconnected API experiments. Before long, they run into two common headaches: unpredictable LLM bills and no clear way to control what tools or data those models can touch. AgentShelf solves this by giving teams dedicated workspaces for each job, approved business context, and pre-built workflows, like AI Front Desk for website visitor intake and customer questions.
Under the hood, AgentShelf acts as the operational guardrail. It includes a built-in LLM gateway with strict budget caps, prompt versioning, access boundaries, and end-to-end usage logs. Teams can test and swap models freely, automate everyday business tasks, and scale their AI usage with clear visibility into what’s running and what it actually costs.
Features
AgentShelf gives non-technical teams an enterprise environment to configure and run task-specific AI agents.
- Context-Grounded Workspaces: Agents reference uploaded SOPs, product catalogs, and brand guidelines, keeping work accurate and consistent with internal standards.
- Configurable Human-in-the-Loop: Set safety gates on any workflow. High-stakes actions require human approval, while routine lookups run automatically.
- Tool & Service Connectors: Agents interact safely with external services and knowledge sources under strict permission policies.
- Multi-Agent Collaboration: Assign distinct roles—such as research, drafting, and technical review—to specialized agents that pass work cleanly down an editorial or operational pipeline.
- Audit Trails & Governance: Track all agent prompts, outputs, and review decisions in centralized activity logs for full accountability.
Use Cases
- Deploy Without Engineering: Launch customized AI assistants and workflow automations in minutes using an intuitive web interface.
- Maintain Brand & Policy Consistency: Ground agent outputs in your uploaded company documents, style guides, and standard operating procedures.
- Human-in-the-Loop Safety: Enforce human review checkpoints on sensitive tasks so no unapproved content or messages ever leave your workspace.
- Eliminate AI Hallucinations: Confine agents to your approved knowledge base, preventing made-up facts and unsupported claims.
- Role-Based Team Collaboration: Share workspaces across team members with granular permission levels for viewers, editors, and workspace admins.
- Transparent Activity Audit: Inspect every prompt, retrieved document, tool execution, and approval decision in a tamper-evident activity log.
- Reusable Workflow Templates: Build library templates for recurring tasks like client intake reviews, competitive briefs, and support ticket prep.
Comments
Hey Fazier community! 👋 Gabe here, co-founder of AgentShelf. Most AI tools launching right now are either toy wrappers with zero cost controls, or heavy developer SDKs that take three engineers six months to wire up. We built AgentShelf to sit right in the middle: an operational control plane for business AI. Our core wedge is an omnichannel AI Front Desk for web and phone lines. It answers customer inquiries in real time, respects strict document boundaries so it doesn't hallucinate pricing or policies, and executes warm telephone transfers to human staff when high-intent buyers call. Within the platform, AgentShelf is an AI operations control panel that helps businesses build, deploy, and govern a specialized digital workforce. Teams get an off-the-shelf library of pre-built agents, a no-code builder to create custom agents for their specific workflows, and shared workspaces where agents collaborate using approved company context and secure tool connectors. Built-in human-in-the-loop review gates ensure teams maintain quality control, while enterprise-grade governance, including hard budget caps and token telemetry, guarantees AI scale never results in runaway costs Instead of treating AI operations like a black box, we’ve also published our operational framework and guides in our Knowledge Center at https://agentshelf.ai/knowledge. We’re actively rolling out to more teams and would genuinely appreciate your feedback on the platform, our gateway approach, or our documentation!
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Comments
Hey Fazier community! 👋 Gabe here, co-founder of AgentShelf. Most AI tools launching right now are either toy wrappers with zero cost controls, or heavy developer SDKs that take three engineers six months to wire up. We built AgentShelf to sit right in the middle: an operational control plane for business AI. Our core wedge is an omnichannel AI Front Desk for web and phone lines. It answers customer inquiries in real time, respects strict document boundaries so it doesn't hallucinate pricing or policies, and executes warm telephone transfers to human staff when high-intent buyers call. Within the platform, AgentShelf is an AI operations control panel that helps businesses build, deploy, and govern a specialized digital workforce. Teams get an off-the-shelf library of pre-built agents, a no-code builder to create custom agents for their specific workflows, and shared workspaces where agents collaborate using approved company context and secure tool connectors. Built-in human-in-the-loop review gates ensure teams maintain quality control, while enterprise-grade governance, including hard budget caps and token telemetry, guarantees AI scale never results in runaway costs Instead of treating AI operations like a black box, we’ve also published our operational framework and guides in our Knowledge Center at https://agentshelf.ai/knowledge. We’re actively rolling out to more teams and would genuinely appreciate your feedback on the platform, our gateway approach, or our documentation!
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