Turn conversation patterns into agent improvements

Agent Evolution helps teams learn from the conversations their bots already handle. It looks across live traffic for recurring patterns and brings proposals into a review workflow rather than asking an operator to read every conversation individually.

From signal to proposal

The workspace groups related observations, shows supporting examples, and can propose learned rules for how an agent should respond next time. The operator can inspect the reasoning and evidence before approving a change. This keeps improvement accountable to a person with context about the business.

Evolve with control

Approved changes remain visible in the bot's operating history, with a path to roll back when needed. Episodic recall and per-user adaptation give the agent additional context while keeping the review process explicit.

Teams can use Agent Evolution as an ongoing quality loop: observe real conversations, review a concrete proposal, apply it deliberately, and check its effect in future traffic.