Providing the right feedback to an AI agent is crucial in making sure it provides value in your product ecosystem—and works well with your team members.
Find out how to gain visibility to your AI agent's flows so your teams can share their feedback and improve the agents they work with.
Your employees know your business inside and out—and their expertise can retrain poorly performing agents. It's a fast and accurate way to help your AI agent work alongside existing teams. We reveal the behavior of your agents so that your teams can provide useful feedback, and better understand these new tools.
Case Study: Improving AI AgentsOnce your agents have been reviewed, you get more than a list of errors. You get a dataset you can use to improve your AI agent in the tasks it’s struggling with the most—meaning it can better assist the teams it was designed to assist.
AI agents have the potential to revolutionize how businesses work and operate. For this to happen, AI agents need to collaborate successfully with teams and integrate well to your workflows, so that they can provide value to the departments they are supporting.
No matter how advanced your agents are, there will be edge cases where only an expert can identify the flow it should have taken. Having a human identify and correct these edge cases is key.
Being able to review the steps taken by an agent can help build trust with the workforce they will operate with. It also allows you to proactively correct model hallucinations, biases, and other errors.
In case of errors, having the audit tool in-hand can help non-data experts understand what went wrong in the flow, make corrections in-context, and flag them to the engineering teams.
In order to create successful hybrid teams with humans and AI agents, the two need to operate together—on a shared language. Our tools help agents be more nuanced for your use case.
With our platform, we can ingest your agent log and make it visible for annotation, step by step.
We simplify the output to make it more readable, so that all types of users can give feedback—even if they don’t have data science profile.
Auditing the flow of your agents to uncover the break down of the planning and actions taken across every step.
Users add feedback to any step. You guide the feedback flow by adding existing error tags and correction paths, so your users can be granular and consistent in their feedback.
The annotated output of your AI agent log contains detailed analysis of what steps went wrong. You can investigate the data to understand trends about where your AI agents fail most.
Retrain your models with the additional rich feedback. This dataset can also become a benchmark for future projects.
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