Product
Tero vs Sentry: how the two tools compare for fixing production errors
Sentry tells you what broke. Tero opens the PR that fixes it. Here's how the two stack up — and where Sentry is still the better tool.
Zaid Mallik · 2026-05-13 · 9 min
If you're shipping a web app in 2026, you're probably already running Sentry — or one of its peers (Bugsnag, Rollbar, Datadog APM, Honeybadger). It's the default. So when people hear about Tero "autonomously fixing production bugs," the first question is reasonable: how is this different from what Sentry already does?
Short answer: Sentry tells you a bug happened and gives you the stack trace, breadcrumbs, and a rough idea of which release introduced it. Tero takes that information, locates the cause in your codebase, writes a patch, and opens a PR. They're operating at different layers of the same problem — and for many teams the right answer is "use both."
This is the honest version of that comparison.
What Sentry is genuinely great at
Before any of the "Tero adds X" framing, it's worth saying clearly: Sentry is one of the most engineered, mature, lovingly-built dev tools in production today. The reasons people stay on it after the trial:
- First-class error capture across every stack. SDKs for ~40 languages and frameworks, source maps that work, native exception handling on iOS/Android/Electron, browser perf monitoring, server-side performance, distributed tracing. Each one is solid. - Release health. Tying errors to a specific commit/release, computing crash-free user rates, automatically resolving issues when a release no longer reproduces them. This is genuinely hard to do well and Sentry does it. - Issue dedup and ownership. Stack-trace fingerprinting that survives minor changes in the surrounding code. Code-owners auto-assigning issues to the right person via CODEOWNERS. Sentry's been refining this for a decade. - Performance & web-vitals telemetry. Slow transactions, N+1 query detection, Web Vitals correlated to specific JS bundles. Not strictly an "errors" feature, but the same UI. - Integrations. Slack, GitHub, Jira, PagerDuty, Linear — wired up properly, not just webhooks. - The Suggested Fix feature. Sentry has shipped LLM-generated "here's what might be wrong" summaries and even drafts of code changes. They're useful for orientation and getting wrong on purpose.
If you turned off Sentry tomorrow on a 50-engineer codebase you would feel it within hours.
What we found Sentry doesn't do (and what Tero does instead)
The gap that motivated Tero isn't in error capture — it's in what happens after the alert fires. Sentry's job ends at "here's the issue, here's the trace, here's a guess at root cause." Then the human takes over: open the editor, find the file, write the fix, push the PR, wait for review.
For high-volume errors that affect real users, this loop takes hours to days. The cost is real — but it's tedious work, not interesting work. It's the kind of work where the engineer already knows what the fix is the moment they read the stack trace, but it still takes 40 minutes to write because they have to context-switch into the editor, find the file, write the patch, run the tests, push the branch.
Tero closes this gap by running an agent loop on top of the Sentry alert (or any other source — Tero ingests events from PostHog, Datadog, GA, Mixpanel, and direct JS error reporters too). The loop is:
1. Receive the error event with stack trace and breadcrumb context. 2. Locate the relevant code in your repository (via the GitHub App grant). 3. Reproduce the failing path in a sandboxed Docker preview of your repo (Tero spins up your full app in a container per blueprint). 4. Generate a candidate patch with Claude Sonnet 4.5 / Opus 4.5, run your test suite against it, iterate if it fails. 5. Open a real PR on the tero branch with the fix, a written explanation, before/after evidence, and a link back to the originating Sentry issue. 6. Wait for your approval. Merge only if you say so.
Notable: nothing in step 4 fires until tests pass. If the agent can't get a working patch in the budgeted number of turns (default 15), the loop aborts and nothing is opened — no spammy "we tried" PRs.
Where Sentry's "Suggested Fix" stops short
Sentry's Suggested Fix and similar LLM-driven hints are useful, but they're operating with only the error context — they see the stack trace, possibly some breadcrumbs, and the source code around the failing line. They don't see your test suite. They don't…