Tricentis Acquires Tabnine to Gain Knowledge Graph for AI Testing Agents

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Tricentis today revealed it is acquiring Tabnine to gain access to a knowledge graph that will be used to provide AI agents that have been trained to automate a range of testing tasks. Once the acquisition is complete, Tricentis plans to integrate the knowledge graph developed by Tabnine, dubbed the Enterprise Context Engine, within the company’s Agentic Quality Engineering Platform. Coupled with a vector model, that knowledge graph makes it possible to extract entities, relationships, dependencies, and architectural patterns from repositories, documentation, tickets, application programming interfaces (APIs) and infrastructure metadata in a way that is much easier for AI agents to consume and understand. The end result will be a platform that continuously ingests code, documentation, tickets, and APIs to provide organizational intelligence in real time that enables multiple AI agents to share memory and context to both verify outputs and better understand the potential downstream impact of an action. David Colwell, vice president of AI and machine learning for Tricentis, said that, just as importantly, the capability makes it possible for AI agents to more efficiently test code in a way that serves to reduce the total number of tokens that might otherwise be required. It’s still early days when it comes to incorporating AI agents into DevOps workflows, but the one thing that is already clear is there is now a greater need to rely on an independent set of AI agents to verify the output of AI coding agents. In some cases, the AI agents performing testing tasks may rely on …

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