According to a recent LinkedIn post from Glean, the company is emphasizing the importance of treating agent “harnesses” as distributed context management systems to support more complex, longer-running AI workflows. The post explains that as AI agents invoke more tools, skills, searches, and executions, maintaining a focused and efficient context window becomes increasingly challenging.
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The post suggests Glean has rebuilt its internal harness architecture to progressively load instructions into skills rather than relying on a large static system prompt, reportedly reducing its own system prompt size by more than 45%. This design is portrayed as delivering only the necessary instructions to the agent at the right time, with the goal of improving accuracy and task completion for enterprise use cases.
For investors, the update points to ongoing product-level innovation aimed at scaling AI agents reliably in real-world, high-complexity environments. If effective, this type of infrastructure could strengthen Glean’s competitive position in enterprise AI and knowledge management by enabling more robust automation without significantly increasing model costs or error rates.
The post also directs readers to additional technical material on Glean’s “third-gen harness,” suggesting a continued focus on platform depth rather than purely front-end features. This emphasis on core infrastructure may help Glean appeal to sophisticated enterprise buyers that prioritize reliability, control, and scalability in AI deployments, potentially supporting longer-term customer retention and expansion.