A Practical Checklist for Evaluating AI Vendors

AI agents are best understood by what they add on top of traditional automation: the ability to make judgment calls in workflows that involve ambiguity rather than only executing fixed, predictable steps. Traditional automation tools work well for fixed, predictable steps, but struggle when a workflow requires judgment calls, like deciding how to categorise an ambiguous customer request.

AI agents can read unstructured input, decide which of several tools or systems to use, and adapt their next step based on what they find, handling variability that rigid automation cannot.

We typically pair agents with traditional automation, using the agent for the judgment-heavy parts of a workflow and simple automation for the predictable parts, which keeps the system easier to debug.

A useful mental model is to break any business workflow into its predictable and ambiguous segments. Steps like "update the record" or "send the confirmation email" are deterministic and best left to simple, reliable automation. Steps like "figure out what this customer actually wants" or "decide which of these three systems needs updating based on this request" are where an agent's ability to reason and choose a next action adds real value.

A common pitfall is giving an agent too much unscoped autonomy too early, letting it call any available tool without clear boundaries. This makes debugging painful when something goes wrong, since it is hard to reconstruct why the agent chose a particular path, and it increases the risk of the agent taking an action it should not have, such as sending an incorrect message or modifying the wrong record.

When Techomaxx designs an agent for a client, we start by mapping the exact tools it needs access to and the guardrails around each action, since an agent that can act autonomously also needs clear boundaries on what it is allowed to do. We also log every tool call an agent makes, which turns debugging from guesswork into simply reading a clear execution trace, and lets us tighten guardrails based on real behaviour rather than speculation.