Most people who only use chat AI and are now watching AI coding tools will make the same first mistake: they will shop for a smarter model before they can replay why the last run failed. My read on kenn-io / agentsview [C001] is that AI coding needs replay before a new model [C002].
That mistake is expensive in a boring way. You burn time, budget, and attention on model shopping when the real problem is that your failures leave no trail. A product update is worth reading only if it changes your next decision, not because it ships a longer feature list.
On AgentsView's public Session Intelligence page, the emphasis is on failure clues: health score, outcome, tool failures, retries, repeated edits, and context compaction. That reads less like a dashboard and more like an incident archive for stuck jobs. The useful part is not the score by itself. The useful part is saving the trail of a bad run so you can inspect it later.
Its public Stats page pushes the same idea into code history: commit count, lines added or deleted, and files changed. That is a harder test than "the assistant sounded smart." Did it finish useful work, or did it just produce a convincing chat?
Boundary: this take comes only from AgentsView's public Session Intelligence and Stats pages, not from a local install, benchmark, or hands-on run. So I would treat the health score as a clue, not a KPI. If you know someone pricing a new model before they can inspect a failed run, share this with them.