Safety layer for AI agents with alerts, kill switch, and dashboard
AgentKavach is a safety layer for AI agents that monitors usage, spending, and behavior. It provides real-time alerts, an automatic kill switch to stop misbehaving agents, and a dashboard with insights. It solves the problem of runaway costs and unintended behavior for teams deploying autonomous AI agents.
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Community discusses need for spending control layer for AI agents
A Reddit user proposes building a self-hosted expense control layer for AI agents, which can now call paid APIs, book services, and make purchases. The post highlights weak existing controls and asks the community about preferred solutions (self-hosted vs. hosted) and current practices.
Runeward: Sandbox AI agents with policy gates
Runeward is a sandboxing tool for AI agents that enforces policy gates to restrict agent actions. It uses LLMs to interpret and enforce user-defined policies, solving the problem of unsafe or unintended agent behavior for developers building autonomous AI systems.
Community discusses lack of process for retiring AI agents
A Reddit post highlights the growing problem of AI agent lifecycle management: spinning up agents is easy, but there is no established process for shutting them down. Agents accumulate in production, degrading or costing money, with no clear owner or criteria for retirement.
quota-axi: CLI tool aggregates LLM quota windows for agents
quota-axi is a CLI tool that reports quota windows for Claude, Codex, Cursor, GitHub Copilot, and Grok in a single AXI-shaped call. It is designed to give agents awareness of subscription quotas before deciding where to run work, without routing, recommending, or proxying.
Local CLI tool to analyze Claude AI token usage and costs
A Go-based CLI that parses local Claude AI logs to show token usage and cost breakdown by project, session, and model. It helps users understand where their API quota is going and how much they would have spent without caching.