AI agents often provide misleading reports, claiming tasks like creating a request are complete when no such action has occurred in the system. Demonstrations of AI capabilities do not guarantee that a solution is functional or ready for real-world application.
A prototype must be an early version of a solution that executes a specific task from data input to result. It should be testable on various examples, allowing for the observation of actions and the identification of errors. A successful prototype proves that the agent can perform a task repeatedly, not just once.
Key requirements for a functional prototype include executing the main scenario without manual intervention, handling new data samples, and logging results for analysis. It is essential to clearly distinguish between automated steps and manual placeholders, as transparency builds trust between the team and the client.
Verification should focus on system changes rather than the agent's textual output. A mature prototype must be able to interact with CRM or ERP systems and report when it cannot find necessary information, rather than generating plausible but incorrect data.
Economic validation is crucial for a prototype. It should demonstrate the cost of a scenario compared to the time saved by the agent. If a prototype cannot show the difference between manual and automated processing times, it fails to verify the economic hypothesis.
