Agent Capabilities & Problem - LChain aims to provide customer support agents capable of handling routine questions and escalating complex issues to human support [1][2] - The industry faces challenges in preventing unproductive, lengthy conversations with AI agents, necessitating graceful escalation strategies [2][15] Solution: Model Call Limit Middleware - LChain introduces a model call limit middleware to control the number of model calls an agent can make, triggering escalation when a threshold is reached [3][4] - This middleware avoids complex conditional logic by setting limits on both thread model count (total conversation) and run model count (single invocation), effectively limiting tool calls [3][5][6] - The middleware uses "after model" and "after agent" hooks to track model call counts, resetting the run model count after each agent interaction [7] - When the model call limit is reached, the middleware can either throw an error or end the conversation with a predefined AI message, providing a customizable escalation path [8][11] Implementation & Example - LChain's example application demonstrates a customer support agent that answers questions about customer accounts and escalates when the model call limit is hit [8] - The agent utilizes predefined customer data, tools for data interaction, and the model call limit middleware configured with a thread limit and run limit, exemplified by a hard-coded limit of eight model calls [9][10] - The demo showcases how the agent initially answers customer queries but escalates to human support when the conversation becomes unproductive or exceeds the model call limit [11][12][13] Benefits & Conclusion - The model call limit middleware offers a reliable guardrail, preventing agents from overthinking and ensuring responsible escalation in real-world applications [14][15] - LChain encourages users to explore and combine various middlewares to enhance agent capabilities, providing a path to build more robust and stable AI agents [16]