How We Evaluate AI Agents Before Recommending Them to Clients

We get asked which AI agent platform to use at least a dozen times a week. Our answer is always the same: it depends on the workflow, not the tool. We have shipped over 350 products, many of them AI-powered, across 20+ industries. The evaluation framework below is what we actually use when a client comes to us with an agent build in scope. It is not a tool comparison. It is a decision framework built from production experience. Key Takeaways Reliability under real inputs matters more than benchmark performance: an agent that scores well on evals but fails on your actual data is not a good agent for your use case. Tool-calling quality is the most underexamined criterion: the ability to call the right tool at the right time with the right parameters separates production-ready agents from demo-ready ones. Context window behavior determines viability for long workflows: agents that lose track of earlier steps in multi-step workflows create errors that compound and are difficult to trace. C