ArticleThe Cognition TeamResearch
As enterprise AI token spend surges, engineering leaders must prove return on investment rather than merely tracking consumption. Traditional productivity metrics fail when applied to autonomous agents. Automating the evaluation of equivalent human engineering hours offers a scalable, defensible way to justify and optimize agent deployments.
Cognition tackles a fundamental problem in software analytics: evaluating autonomous agents without relying on flawed proxies like commit counts or token burn. By anchoring the baseline prompt to an engineer with assumed relevant expertise and ignoring agent-specific overhead (like environment recovery and automated summaries), the methodology deliberately yields conservative estimates. While single-session predictions remain noisy (often off by a factor of 2 to 3), the unbiased distribution makes it viable for aggregate reporting across large engineering organizations.
Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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