When working with Agentic AI, relying on inaccurate details can quickly escalate into a significant issue since the system translates its generated output directly into actual execution. Because of this inherent risk, if you are developing an agent designed to respond to outside information, merely retrieving that data is not enough. You must carefully determine exactly which pieces of information are qualified to initiate an action.
To do this successfully, you need to implement comprehensive evaluations of your data before moving forward. Make sure to authenticate the identity and provenance behind the information, verify its source, and confirm how recent it is. Furthermore, it is essential to scan for any logical contradictions and measure the overall confidence level of the material.
You can easily construct this necessary validation layer within your own agent by consulting the guide provided by @Fetch_ai_IL at the following link.
https://innovationlab.fetch.ai/resources/docs/agent-communication/sdk-uagent-communication
To do this successfully, you need to implement comprehensive evaluations of your data before moving forward. Make sure to authenticate the identity and provenance behind the information, verify its source, and confirm how recent it is. Furthermore, it is essential to scan for any logical contradictions and measure the overall confidence level of the material.
You can easily construct this necessary validation layer within your own agent by consulting the guide provided by @Fetch_ai_IL at the following link.
https://innovationlab.fetch.ai/resources/docs/agent-communication/sdk-uagent-communication