Essential Insights
- The current evaluation framework measures agent accuracy and correctness but ignores whether deploying the agent is economically viable, i.e., whether it costs less per successful outcome than hiring humans.
- The key metric for assessing AI agent viability is cost per successful outcome, which includes the total costs (attempts, retries, escalations) divided by the number of successful resolutions — not just cost per call or token.
- Measuring cost per successful outcome is complex due to challenges like attributing failed attempts, double payments during escalations, and defining the business value of outcomes, but it is essential for true ROI assessment.
- Teams that succeed in deploying AI agents are those whose cost per successful outcome stays below the value of that outcome; focusing solely on quality metrics can lead to continued investments in unprofitable models or unnecessary shutdowns.
The Limitations of Quality Metrics in AI Evaluation
Many teams focus on a set of quality metrics to judge AI agents. These metrics include task accuracy, retrieval precision, and hallucination rates. They are useful for understanding if the AI is functioning correctly. However, they do not tell the full story. For example, an agent can perform well on these metrics but still cost more than it saves. Quality alone does not reveal if an AI is financially sustainable. This gap led to the shutdown of an AI tool, despite passing all quality checks. It highlights the need for a broader perspective that considers economics, not just correctness.
The Missing Piece: Cost per Successful Outcome
The key to truly evaluating an AI agent lies in measuring its cost per successful outcome. Unlike cost per call or token, this metric considers the total investment divided by successful results. It captures the true operational cost of delivering value. For instance, if an AI attempts ten tickets at $3.40 each and resolves only seven, the average cost per success rises significantly. When failures and escalations are factored in, the total cost often exceeds the value of the outcome. This metric reveals whether the AI workflow is economically justified, not just technically effective.
How to Use Cost Metrics to Improve AI Deployment
Measuring cost per successful outcome is complex but essential. Teams can improve this metric by reducing the cost of each attempt—like trimming unnecessary reasoning or caching results—or by increasing the resolution rate. They might also narrow deployment to workflows where the AI is already cost-effective. Importantly, scaling an agent does not automatically reduce costs if the cost per success remains above the value of the work. Therefore, ongoing measurement guides teams to optimize AI use, ensuring they avoid deploying costly agents that do not pay for themselves in the long run.
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