According to a recent LinkedIn post from Deepchecks, the company is emphasizing a workflow for improving AI agents that goes beyond simple aggregate accuracy metrics. The post describes an evaluation loop integrated into the developer’s IDE, where Deepchecks identifies specific failure categories and links them to individual agent sessions.
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The post highlights an iterative process in which developers use Deepchecks’ evaluation and a Claude Code skill, backed by the Deepchecks SDK, to apply targeted fixes directly into their applications. It suggests that this approach allows developers to address distinct issues such as clarification avoidance, hallucinations, and formatting errors in successive versions.
According to the description, the tooling aims to make each iteration traceable to a named prior failure, potentially reducing trial-and-error in tuning AI agents. For investors, this positions Deepchecks as focused on practical agent evaluation and debugging workflows, which may appeal to teams deploying complex AI agents in production environments.
If adopted widely, such a workflow could deepen customer reliance on Deepchecks’ platform and SDK, potentially supporting higher retention and expansion revenue. It may also help differentiate the company within the AI tooling and evaluation segment, where demand is growing for solutions that turn qualitative failure analysis into faster, more systematic model improvement cycles.