Trellix innovates log syntax with Langgraph and Langsmith

According to Langchain’s blog report, Trellix reduced the log parsing time in a few days to a few minutes using Langgraph Studio and Langsmith in a significant breakthrough on cyber security. This innovation is part of Trellix’s extensive strategy to improve customer experience and operational efficiency.

Solve log syntax analysis problem

Trellix, a prominent cyber security company that provides more than 40,000 customers, has been facing the challenge with a back log of customer requests related to cyber security integration and log syntax analysis. Previously, these tasks had to be frustrated by the development time for a few days for developers to decipher and coded integration.

To solve this problem, Trellix has developed an internal application called Sidekick, designed to automate boring processes such as Log Parsing and Script Writing. By using the Langgraph tool, Trellix automated the parliamentary creation of unknown log formats, greatly reducing manual syntax analysis time and allowing engineers to focus on more complex tasks.

The role of Langgraph and Langsmith

LANGGRAPH has greatly improved the development process by providing Trellix with the tools needed to generate module and efficient agent workflows. Map-Ready Style Graph and subgraph calls facilitate the creation of structured approaches for processing log data.

Langsmith is essential for monitoring and evaluating agent performance, so Trellix has been able to experiment with various agent architecture and effectively track performance indicators. This feature allows the team to make a data -oriented decision and ensure that empirical evidence is improved.

Visualization and debugging with Langgraph Studio

Langgraph Studio played an important role in visualizing and optimizing agent workflows. By mapping the manual process and converting it to an automated workflow, Trellix was able to improve work efficiency. This visualization also promoted communication with non -technical stakeholders to provide clear insights into the AI ​​model’s decision -making process.

Influence and future prospects

The implementation of Langgraph and Langsmith has saved a considerable time for the engineering team of Trellix and improved customer satisfaction. The company not only reduced log syntax analysis time, but also accelerated customer requests, improved AI agent performance, and increased stakeholders’ confidence.

TRELLIX plans to expand Sidekick’s features to external partners to democratize access to AI -centric solutions in cyber security. The success of these tools has set the stage of continuous innovation by planning to expand automatic syntax analysis and cloud connectors to all customers in the future.

Through this development, Trellix shows the innovative potential to open the way for future development of AI -centered cyber security solutions and integrate cutting -edge technology into traditional processes.

For more information, visit the Langchain blog.

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