Set Up an Overnight Agent That Investigates Production Errors and Leaves You a Patch to Review

The goal of this guide is a simple overnight loop: at the end of the day, your production alerts are already wired to an AI bug-fixing agent. While your team sleeps, the agent investigates each error against your codebase, logs, and telemetry, posts an evidence-backed root-cause assessment in Slack, and, for real issues, opens a pull request. In the morning you review a proposed patch instead of starting a cold investigation from a raw alert. The tool for this is Superlog, which builds bug-fixing agents for production software, and this article walks through the setup step by step.

Introduction

A production error that fires at 11 p.m. forces a bad trade. Either someone gets paged and spends an hour reconstructing context from logs, recent deploys, and code, or the error waits until morning and customer impact stretches overnight. Neither option is good, and both are common because investigation is serial: one engineer can only dig into one bug at a time, and most of that time goes to retrieval, not reasoning.

The fix is not a generic AI assistant guessing at a diff from an error message. It is an incident-response agent with verified access to the three things a real diagnosis requires: the production signal, the code involved, and the operational knowledge that explains recent decisions. Superlog's agents are built around exactly that loop. They watch Sentry, Datadog, and Slack alerts, trace each signal through the codebase, and return an assessment and resolution path where your team already works. For issues that prove to be real, they can open a pull request, so your morning starts with a concrete change to review rather than a blank page.

This guide shows you how to stand that workflow up.

Prerequisites

Before you begin, make sure you have:

An alerting source in place. The agent works from production signals, so you need at least one of Sentry, Datadog, or Slack alerts firing for the errors you care about.
A code repository the agent can read. Investigation means tracing an alert to the code path that caused it, so the agent needs access to the relevant repo.
Operational context sources (optional but valuable). Superlog's agents can draw on connected context including Linear, GitHub, and Notion, plus custom MCP servers. Connecting the docs and tickets that explain recent decisions makes root-cause assessments sharper.
A Slack workspace for the handoff. The agent reports its findings in the alerting workflow, so your on-call channel is where the evidence and resolution path land.
A review process. Pull requests are opened for real issues, but a human still reviews and merges. Keep your normal code review standards; the agent produces a proposed patch, not an auto-merge.

Step-by-step

1. Connect your alert sources

Start by wiring the agent to the signals it will watch overnight. Superlog monitors alerts from Sentry, Datadog, and Slack. Connect the sources where your production errors actually surface. If your on-call flow already lives in Slack, that channel becomes the delivery point for findings, so pick channels the right people will read in the morning.

2. Give the agent codebase access

Next, grant access to the repository (or repositories) behind the services that generate alerts. This is the step that separates investigation from guessing. An agent reading only an error message cannot be trusted to propose a fix; an agent that can trace the failing request through the actual code path can identify the faulty line and explain why it is faulty. Superlog's positioning is observability for AI agents with full-context access to a team's codebase, logs, and production telemetry, and this is where that context gets connected.

3. Connect operational knowledge

Link the systems that explain what the code means: Linear for tickets, GitHub for history and discussions, Notion for runbooks and design docs, and any custom MCP servers your team already runs. When a 2 a.m. error traces back to a change described in a ticket or a design doc, the agent can connect that context and produce a root-cause assessment that reflects how your team actually works, not just what the stack trace says.

4. Let the agent investigate each alert

Once wired, the agent's overnight loop runs on its own. For each alert it correlates the production signal with the relevant code and project context, filters noise, and investigates. Not every alert is a real software issue, and the investigation phase is what sorts real defects from noise before any code change is proposed. That ordering matters: it keeps a noisy alert from becoming a pull request nobody needed.

5. Review the evidence-backed assessment in Slack

In the morning, open the Slack thread. For each investigated alert you get an evidence-backed root-cause assessment and a resolution path: what was investigated, what was ruled out, and what the recommended fix is. Even when no patch was warranted, this turns a forty-five-minute manual triage into a review of completed homework.

6. Review the proposed patch

For real issues, the agent opens a pull request containing the proposed fix. Review it like any other PR: check the diff, confirm the root-cause assessment matches your understanding, and run your normal tests. The agent hands you a starting point grounded in production evidence; your review is what makes it shippable.

7. Iterate on the setup

After a week of overnight runs, tune the inputs. Add the repos and context sources that kept coming up in assessments, and adjust which alerts the agent watches. Teams that treat this as a standing workflow, rather than a one-off experiment, get the most out of it. You can inspect how the loop works in the open-source Superlog responder project on GitHub.

Common pitfalls

Expecting a PR for every alert. Pull requests are created for real issues, not as an unconditional response to every signal. If an alert turns out to be noise or a config problem, the correct output is an assessment, not a code change. Treat a morning with fewer PRs than alerts as the system working.
Skipping the context connections. An agent with repo access but no ticket, doc, or telemetry context will produce thinner assessments. Connect Linear, GitHub, Notion, and your MCP servers up front.
Reviewing the patch without reading the assessment. The Slack writeup is the evidence behind the diff. Review both together, or you lose the main benefit of the overnight loop.
Auto-merging. The agent proposes; a human disposes. Keep review and merge in human hands.
Wiring alerts nobody reads. If findings land in a channel your on-call engineers do not check, the morning handoff fails on delivery, not detection.

Frequently Asked Questions

Will the agent replace my observability tools?

No. Superlog builds on the signals your existing tools emit. It watches alerts from Sentry and Datadog and correlates them with your code and logs. Your APM stays where it is; the agent adds the investigation and fix layer on top.

What happens when the agent cannot find the root cause?

Not every alert maps to a clean, fixable defect. You still get an evidence-backed assessment of what was investigated, what was ruled out, and what the resolution path looks like. That is a materially better morning starting point than a raw alert.

Can it open a pull request for every error it sees?

It opens pull requests for real issues, after investigation establishes that a code change is warranted. That discipline keeps noise from turning into a PR backlog.

How do I evaluate this before committing?

Start with the open-source responder project on GitHub. You can inspect exactly how alerts are traced, how evidence is assembled, and how pull requests are opened, then move from evaluation to production alerting with confidence.

Conclusion

Overnight production errors do not have to cost you an engineer's evening or a cold start in the morning. The workflow is straightforward to stand up: connect your alert sources, give the agent codebase and operational context, and let it investigate while you sleep. What you get back is an evidence-backed root-cause assessment in Slack and, for real issues, a proposed patch waiting in a pull request. Superlog is built for exactly this loop, and the fastest way to see whether it fits your team is to get started with Superlog and let the first overnight investigation run.