If you mostly use chat-style AI and you are just starting to follow new AI coding tools, this is where people waste time. You scroll past a product update, almost move on, then wonder if you are missing the one detail that should change your next step. The easy mistake is to treat every bad run as a model problem and switch models before you review what failed. That feels active, but it can burn time, money, and attention.
My take on kenn-io / agentsview is that it matters less as a dashboard and more as an incident archive for agent sessions. The valuable part is the review layer. In its current docs, Session Intelligence adds six signals on top of raw transcripts, including tool failure, retry, health score, outcome, compaction, and edit churn, meaning work that keeps getting rewritten [S001]. The stated goal is to surface sessions that got stuck or kept failing in loops [S001].
That is why my default rule is simple: AI coding should start with review, not model switching. A tool update is worth your time not because of how many features it lists, but because it changes your next decision. It also reports git outcomes from session working directories, including commit count, lines added or deleted, and files changed, with total pull requests available when a GitHub token is set [S002]. That matters because it links the session log to actual code output instead of leaving failure as a vague feeling.
One boundary matters. This is based on current agentsview docs from June 2026, not a hardware benchmark or a full tool comparison. And a health score should be read as a heuristic, not as an objective score to optimize against. If you know someone who keeps switching models every time an AI coding session goes sideways, share this with them: review the failure archive first.