The trust loop — hear, read, and judge every call, then tune the agent until it does the job you wanted.

An agent only earns trust if you can see what it's doing. A call log always existed, but it was hard to navigate and didn't connect the dots.

Outcomes — didn't pick up, picked up, booked a meeting, transferred — are handy labels, but a label isn't proof the agent handled the conversation well, and a single call never showed a contact's whole journey.

Evaluation isn't the outcome; it's the conversation behind the outcome — and increasingly the history behind the contact.

To trust an AI agent calling their customers, users need to hear and read what actually happened, see it in context, and have an obvious next step when it's not right.

A call log that finally ties the experience together: every call in one place with its outcome, full transcript, and audio recording, plus much better filtering to find what matters.

From a call's detail drawer, a new button surfaces the contact behind it — a threaded view of all their calls (and, soon, texts), every workflow they've been in, and what that history looks like. Did they stall in one workflow but finish another, and why? That's the new activity log.

Users listen, read, and see the result, then decide whether the agent is doing its job. When it isn't, they edit the prompting and watch the next calls improve — a build → observe → refine loop.

This is where the trilogy closes — create the agent, put it to work in a workflow, then evaluate and refine it — turning a one-time setup into something users can trust and steadily improve.

Hard numbers can be added once cleared.