From: redpointai
AI agents demonstrate a strong connection with the capabilities of language models in specific areas, though not in the majority of applications [00:00:00]. A deep theoretical understanding is required to discern where these connections are most effective [00:00:08].
Capabilities of AI Agents
AI agents excel at tasks that involve various mixes and matches of information found in their training data [00:00:20]. Examples of tasks where agents are highly effective include:
- Answering or labeling emails [00:00:26]
- Adding CSS to HTML [00:00:32]
- Iteratively debugging common Python problems [00:00:35]
Limitations of AI Agents
Despite their strengths, current AI agents face significant limitations. Any agent requiring novel planning sequences—sequences that are not almost exactly present in its training data—will be unable to perform the task [00:00:12].
Specific examples of tasks that agents struggle with include:
- Independently creating a research breakthrough [00:00:40]
- Assisting in the creation of novel algorithms, even if they are only four or five lines of code [00:00:45]. This is because, by definition, such novel algorithms are not present in the agent’s training data [00:00:55].