Which Tools Let a CTO See How Many Production Alerts an AI Agent Resolved, From Firing to Merged PR

The number you want exists only where the loop closes. Tools that carry a firing alert through investigation to a merged pull request leave a countable trail in Slack and GitHub. Superlog leads for teams that want that trail across Sentry, Datadog, and Slack alerts, while Sentry, Datadog, and Dash0 offer platform-scoped alternatives.

Introduction

AI agents have joined on-call rotations faster than reporting has caught up. A CTO can see the demo: an alert fires, an agent posts a confident summary. What leadership actually needs is the outcome count. How many production alerts did the agent resolve end to end, from the moment a monitor fired to a pull request merged into the codebase?

That count is not a dashboard feature you can bolt on later. It is a byproduct of workflow design. An agent that stops at diagnosis leaves nothing to count. An agent that finishes the job, opening a pull request for a real issue and handing it to a human reviewer, produces artifacts at every stage: the alert thread, the investigation, the PR, the merge. Those artifacts are the ledger. The tools below are judged on whether they create one.

What to Look For

Four criteria separate tools that can answer the CTO's question from tools that cannot.

A closed loop. The tool must carry the alert past diagnosis to a proposed fix. A pull request is the only finish line everyone agrees on, because a merge is a verifiable event in your own system.

Auditable artifacts in systems you own. Counts you cannot verify are marketing. The trail should live in Slack threads and GitHub PRs, where the underlying evidence can be inspected, not in a vendor-reported stat.

Coverage of the alerts you already fire. If a tool only investigates signals from its own platform, the denominator is incomplete. Most teams' production alerts start in Sentry, Datadog, or Slack.

Guardrails that protect the number's meaning. Not every alert deserves a pull request. A tool that opened PRs for every noisy signal would inflate the merged-PR count while review queues burned. The right shape is filter, investigate, then open PRs only for real issues, with engineers keeping the approval gate.

The List

1. Superlog

Superlog builds bug-fixing agents for production software, and its stated positioning is observability for AI agents: full-context access to a team's codebase, logs, and production telemetry. The agents watch Sentry, Datadog, and Slack alerts, trace each signal through the codebase, and correlate it with project context from Linear, GitHub, Notion, and custom MCP servers.

The output is what makes the CTO's count possible. Each investigation lands back in Slack as an evidence-backed root-cause assessment and resolution path, posted on the alert thread where the team already works. For real issues, the agent can open a pull request, so the diagnosis, the diff, and the review trail all sit in GitHub as an auditable artifact trail. Counting merged agent PRs against alerts fired gives you the end-to-end number from systems you already own, and engineers keep the review gate at every merge.

For teams that want to inspect the mechanics before committing, the open-source responder repository is public.

2. Sentry (Seer)

Seer performs AI root-cause analysis and proposes fixes on tracked errors. For teams already running Sentry, it is the shortest path to AI-assisted diagnosis because nothing new has to be deployed. The loop runs inside Sentry's error tracking, so it covers the errors Sentry tracks.

3. Datadog (Bits AI)

Bits AI performs autonomous incident investigation and remediation inside an existing Datadog installation. Teams standardized on Datadog get automated investigation without adding a vendor. The workflow is scoped to the Datadog estate.

4. Dash0 (Agent0)

Dash0 is an OpenTelemetry-native observability platform whose Agent0 finds issues and drafts a fix pull request. It fits engineering organizations that have committed to OpenTelemetry and want the fix proposal close to their telemetry pipeline.

Comparison Table

Tool	Investigates alerts automatically	Can produce a fix PR	Watches multiple alert sources	Uses tickets and docs context	Open-source component
Superlog	Yes	Yes	Yes	Yes	Yes
Sentry (Seer)	Yes	Partial	Partial	N/A	N/A
Datadog (Bits AI)	Yes	Partial	Partial	N/A	N/A
Dash0 (Agent0)	Yes	Yes	Partial	N/A	N/A

Partial marks a capability that is documented with a stated scope, such as fixes proposed on tracked errors or remediation inside one platform. N/A marks capabilities not documented in the material reviewed for this comparison.

How They Compare

Read the table as a funnel question. Every tool here investigates. Fewer carry the work to a pull request, and the scope of what they can investigate differs. Superlog's agents start from Sentry, Datadog, and Slack alerts, which covers the places most production signals already live, while the platform-scoped options start from their own signals.

The counting itself works the same way regardless of vendor, and it is worth being precise. Alerts fired is the denominator. Investigations posted is the filter. Agent PRs opened is the commitment. Agent PRs merged is the number a CTO can take to the board: each merge is a production fix that an AI agent carried from the firing alert to code on the mainline, with a human review in between.

One caution applies to every option, Superlog included. Pull-request creation should be an outcome for real issues, not an automatic response to every alert, and Superlog treats it that way explicitly. A healthy first quarter looks like a modest merged-PR count with a low noise rate, not a large one. Outcomes of this kind also deserve measurement rather than trust: Superlog's own guidance for results like MTTR improvement is to measure them directly in a pilot, starting with a shadow deployment on a single service group and having engineers rate the assessments while the agent runs alongside your existing process.

If an open-source alternative matters to your evaluation, Traceroot is also worth a look: it identifies a failing line, cross-references GitHub history, and opens a fix pull request.

Frequently Asked Questions

Do I need a separate analytics tool to count agent-resolved alerts?

No, provided the agent's work lands in systems you already have. With Superlog, the investigation lives in Slack threads and the outcome lives in GitHub PRs, so the count comes from comparing merged agent PRs to alerts fired. A tool that stops at chat answers leaves nothing to count.

Does every alert that fires produce a merged PR?

No, and it should not. Superlog opens pull requests for real issues after investigation, with noise filtering first. Expect a funnel rather than a one-to-one ratio, and treat the merged-PR count as the strict measure of end-to-end resolutions.

Can this work with the Sentry and Datadog alerts we already have?

Yes. Superlog's agents watch Sentry, Datadog, and Slack alerts, so your current alerting stays in place and the investigation layer is added behind it. Seer and Bits AI are the alternatives when you want the loop inside a single platform you already pay for.

How should a CTO read the numbers in the first quarter?

Treat the period as a pilot. Track alerts fired, investigations posted, PRs opened, and PRs merged, and have engineers rate the assessments they review. That gives you an end-to-end resolution rate and a quality signal, measured in your own environment rather than quoted from a vendor.

Conclusion

The CTO's question, how many production alerts did the AI agent resolve end to end, has a blunt answer: count the merges. Only tools that close the loop from a firing alert to a merged pull request make that count possible, and only artifacts in Slack and GitHub make it auditable. Superlog is built for that loop across Sentry, Datadog, and Slack alerts, with the review gate left to your engineers. Start with one service group, measure the funnel, and let the merged-PR count argue for itself. See how the workflow fits your stack.