How LangGraph and MCP Were Used to Fix a Stateless AI Support Ticket Agent

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A development team discovered their AI support-ticket triage agent was losing conversation context after just a few exchanges, causing it to deliver generic, unhelpful responses. To resolve this, they rebuilt the system using LangGraph's StateGraph to create a state machine that tracks a ticket through stages from initial report to resolution. MCP's checkpointers were integrated to save context between interactions, while conditional edges defined how the agent transitions between states such as bug reports, feature requests, and general inquiries. The team also had to address an unrelated flaw where the agent looped endlessly seeking more information, which was fixed by adding clear success criteria to each state. A key lesson from the project was the need to carefully balance memory retention — too much slowed the system, while too little caused it to lose context entirely.

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ProgrammingDEV Community ·

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