Risks and limitations

Multi-agent dependencies

Certain complex tasks require the knowledge of multiple AI agents. When implementing these multi-agent frameworks, there is a risk of malfunction. Multi-agent systems built on the same foundation models (opens in a new tab) may experience shared pitfalls. Such weaknesses could cause a system-wide failure of all involved agents or expose vulnerability to adverse attacks. This highlights the importance of data governance in building foundation models and thorough training and testing processes.

Infinite feedback loops

The convenience of the hands-off reasoning for human users using AI agents also comes with its risks. Agents that are unable to create a comprehensive plan or reflect on their findings, may find themselves repeatedly calling the same tools, invoking infinite feedback loops. To avoid these redundancies, some level of real-time human monitoring may be used.

Computational complexity

Building AI agents from scratch is both time-consuming and can also be very computationally expensive. The resources required for training a high-performance agent can be extensive. Additionally, depending on the complexity of the task, agents can take several days to complete tasks.