Agent Stack is a collection of five lightweight npm libraries designed to prevent AI agents from misbehaving in production environments. The libraries address specific failure modes that developers encounter when deploying AI agents at scale, providing targeted solutions for common production issues. The five libraries include agentfit for handling context window overflow, agentguard for blocking unauthorized network calls, agentsnap for managing tool call drift, agentvet for validating tool arguments, and agentcast for enforcing output schemas. Each library is intentionally kept under 200 lines of code, making them lightweight and focused on solving specific problems without unnecessary complexity. These libraries are built to be composable, allowing developers to mix and match the components they need for their specific use cases. The BYO-LLM (Bring Your Own LLM) approach means the libraries work with whatever language model you're already using, rather than locking you into a specific provider or framework. This flexibility makes them suitable for a wide range of AI agent implementations. The libraries work by intercepting and validating agent behavior at critical points in the execution pipeline. Agentfit manages context by trimming messages to fit within model limits, agentguard acts as a firewall for network requests, agentsnap captures and tracks tool usage patterns, agentvet validates inputs before they're passed to tools, and agentcast ensures outputs conform to expected schemas. Built from real production pain points, these libraries address issues that developers commonly face when scaling AI agents. The small size and focused nature of each library means they can be easily integrated into existing codebases without introducing significant overhead or complexity. This approach allows teams to incrementally add safeguards to their AI systems as needed. The target audience includes developers and teams building AI agents for production use who need reliable safeguards against common failure modes. The open-source nature and npm distribution make these tools accessible to JavaScript/Node.js developers working with AI systems, particularly those who want maintainable, composable solutions for production AI deployments.