Why your team should own its agent analytics

Imagine your dashboard reports that an agent completed a task. The transcript shows the user asking for the same thing twice, then leaving. Before celebrating the success rate, you need to understand how the dashboard reached its conclusion.

Owning your agent analytics means having a practical way to answer that question. It includes access to the evidence, an understanding of the rules applied to it, and control over the system's operation. Open-source software helps make those choices available.

Keep a path from the finding to the evidence

A label such as "failed task" is useful when a reviewer can connect it to what the user asked, what the agent did, and what happened afterward.

Take a cancellation request. The agent might explain the cancellation policy correctly without actually cancelling anything. If your product promises to perform the cancellation, a correct explanation is an incomplete outcome.

Write that distinction into your quality rules. Review the resulting findings against a small set of conversations where the outcome is known. When a finding looks wrong, investigate both the agent's behavior and the analysis that judged it.

Currai brings conversations, intent detection, and violation findings into the same product. Its open-source code gives developers a way to inspect the implementation behind that workflow.

Make changes that reflect your product

Your definition of quality will change as the agent gains responsibilities. An assistant that once answered questions might later be allowed to update an account or initiate a transaction. The evidence needed to judge success changes with those permissions.

Start with explicit rules and concrete examples. If a finding depends on a tool result, preserve that result in the evidence you send. If a user must confirm an action, make the confirmation visible in the conversation.

When configuration is insufficient, access to the source lets your developers evaluate a change rather than wait for a vendor to support your exact workflow. That flexibility comes with maintenance responsibility: document local changes and check them when updating your instance.

Choose where the evidence lives

Self-hosting lets your team operate its own Currai instance and choose how access, retention, and deletion are managed. These are operating decisions that need to be made deliberately.

Review the complete data path. The application, database, authentication service, and classification provider can each have a role. Hosting the application yourself does not automatically keep every processing step inside your network. Use the repository's configuration guide to understand which services your deployment requires.

Assign someone to own updates, credentials, backups, and deletion procedures. If your team cannot support that work, include those costs when evaluating self-hosting against a hosted service.

Give yourself a small, measurable starting point

You do not need to migrate all production conversations to evaluate an open-source analytics tool. Start with a test agent and a few synthetic sessions:

• A task the agent completes correctly.
• A task it explains but does not complete.
• A conversation where the user corrects the agent.
• A response that violates one of your product's explicit rules.

Define the expected findings before sending the sessions. Then review what the system captures, what its analysis gets right, and where you need a clearer rule or better evidence.

This gives your team a concrete basis for deciding whether to adopt the tool and how much operating work it requires. It also creates useful examples for future regression checks.

Read why we open-sourced Currai, or explore the repository and setup instructions to try the self-hosted edition.