It is 2am. PagerDuty fires. You open the laptop, squint at the alert, and the next 45 minutes look like this: Datadog for metrics, GitHub for recent commits, Slack to find out who made the last deploy, Elasticsearch for logs, and then—finally—you find it. A config change from three days ago reduced the connection pool size. The fix took 90 seconds. The diagnosis took almost an hour.

Steadwing just shipped, and it wants to own that 45-minute window.

What Is Steadwing?

Steadwing is an autonomous AI on-call engineer. When an incident fires, it does the investigation you would normally do manually: correlating logs, metrics, traces, recent commits, Slack history, and past incident reports. It delivers a root cause analysis in under five minutes. Then, depending on your settings, it can file the fix itself—a PR, a rollback, a config change—or hand you a fully-staged remediation ready to approve.

It launched on Hacker News in early March 2026 with the founders laying out the problem in plain terms: “It’s 2am, PagerDuty fires, you open the laptop and start the scramble… 45 minutes later you find it was a config change that reduced the connection pool size. The fix took 2 minutes. The diagnosis took almost an hour.” The HN thread picked up traction fast, which is typically a signal that developers recognize the pain immediately.

Who Is It For?

Any engineering team that runs on-call rotations. Practically:

  • Startups with thin engineering benches where a 2am page hits a single engineer who is also the one who pushed the problematic deploy
  • Scale-ups with complex microservice stacks where incidents span five services and the logs live in three different tools
  • Enterprise SRE teams that want to reduce mean time to resolution (MTTR) and give their on-call engineers their nights back

The sweet spot is the team that already uses Datadog, PagerDuty, Slack, and GitHub—because that is exactly what Steadwing integrates with out of the box.

Key Features

Automated Root Cause Analysis

This is the core value proposition. Steadwing pulls context from across your entire observability stack—logs, metrics, traces, code history—and builds a timeline from the moment the alert fires backward through the evidence. The RCA it produces is actionable: specific service, specific change, specific line of impact.

The founders claim sub-five-minute diagnosis. That is a meaningful benchmark. The average MTTR for production incidents in mid-market engineering teams runs 30–60 minutes. If Steadwing cuts that to five, the math on engineering time saved is significant.

Learning From Your Incident History

Steadwing ingests your past incident reports and fixes. Over time, it builds a pattern library for your specific stack. The first time a certain class of error hits, the diagnosis might take four minutes. The tenth time, it knows the pattern and correlates it instantly. This is where AI agents actually compound value rather than just being expensive if-then logic.

Remediation, Not Just Diagnosis

Most observability tools stop at alerting. Some add anomaly detection. Steadwing goes further: it prepares the fix. Options include:

  • Automated PR creation with the proposed code change
  • Rollback to last stable deploy
  • Config change with before/after diff
  • Slack notification to the responsible team with full context

You can run in “approval mode” (Steadwing proposes, you click approve) or full autonomous mode for certain classes of known incidents.

Integration Surface

  • Observability: Datadog, Grafana, Prometheus (beta)
  • Alerting: PagerDuty, OpsGenie
  • Communication: Slack
  • Code: GitHub (GitLab in beta)
  • Logs: Elasticsearch, Splunk (enterprise tier)

The standard integrations cover 80% of modern engineering stacks. If you run something bespoke, the beta API access lets you pipe in your own data sources.

Pricing

Steadwing is in early access as of March 2026. Pricing is not publicly listed—you request access through the waitlist at steadwing.com. The HN thread suggests there is a free tier or trial available for early adopters, which is typical for developer tools in this launch phase. Based on comparable tools in the incident automation space (PagerDuty’s automation tier runs $800–$3,500/month at enterprise scale), Steadwing is likely positioning in the $200–$500/team/month range for growth-stage companies.

Tip: If you are reading this at launch, get on the waitlist now. Early access pricing almost always beats the GA pricing by 40–60% for the first cohort.

Pros and Cons

Pros:

  • Genuinely solves the most painful part of on-call: the diagnosis phase
  • Integrates with the stack most teams already run (Datadog, PagerDuty, Slack, GitHub)
  • Learning loop means accuracy improves over time
  • Remediation options span from human-in-the-loop to fully autonomous
  • Strong positioning for teams where MTTR reduction has direct business impact

Cons:

  • Early access means limited public pricing transparency
  • Dependent on the quality of your existing observability infrastructure (if your logs are a mess, the AI is working from incomplete data)
  • Autonomous remediation requires careful guardrails—most teams will need time to trust it at 2am
  • GitLab and Prometheus support still in beta
  • No public case studies yet—it is very new

How Does It Compare?

The space Steadwing is entering has a few partial competitors:

  • PagerDuty’s Operations Cloud: Mature alert routing with some automation, but the AI layer is tacked on rather than core. Much higher price point.
  • Incident.io: Excellent for incident management and retrospectives. Does not do autonomous RCA or remediation—focused on process orchestration, not diagnosis.
  • Blameless: SRE-focused. Strong on reliability scoring and SLO tracking. Not an autonomous agent.
  • AWS DevOps Guru: Amazon’s AI ops layer. Good for AWS-native stacks. Very limited outside the AWS ecosystem.

None of them are doing what Steadwing is doing: true autonomous diagnosis and remediation generation in a single product. That is the differentiated bet.

The Real Question: Will Engineers Trust It?

The adoption blocker for any autonomous remediation tool is trust. An engineer who just got paged at 2am is not in the mood to let an AI push a config change to production without double-checking everything.

Steadwing’s approval mode is the right answer to this. Let the AI do the investigation and draft the fix. Let the human approve. That builds trust over time. Then, for the class of incidents where the pattern is clear and the fix is low-risk (scaling a connection pool, rolling back a dependency version), the team can flip to autonomous mode.

This is the same arc every workflow automation tool goes through. The question is whether Steadwing’s RCA quality is good enough to earn that trust quickly.

Should You Try Steadwing?

If your team runs on-call rotations and MTTR is a metric you actually track, yes, get on the waitlist now. The problem it solves is real, the market for incident automation is underserved at the AI-native layer, and early-access tools in this space tend to offer generous trial terms to build case studies.

If you are a solo developer or a team without formal on-call processes, this is probably not the right tool yet.

The bet with Steadwing is a simple one: engineering time is expensive, incidents are expensive, and anything that cuts the diagnosis window from 45 minutes to 5 minutes pays for itself very quickly.

→ Get early access at Steadwing

FAQ

What does Steadwing do? Steadwing is an autonomous on-call engineer. It monitors your production stack, and when an incident fires it automatically investigates root cause across logs, metrics, traces, and code history. It delivers a root cause analysis in under five minutes and can also prepare or execute remediation.

How does Steadwing integrate with PagerDuty? Steadwing receives the alert from PagerDuty and immediately begins investigation. It correlates the alert with Datadog metrics, GitHub commit history, and Slack context to build a root cause analysis before you would normally have finished opening your laptop.

Is Steadwing free? As of March 2026, Steadwing is in early access with pricing available on request. Get on the waitlist at steadwing.com for early-adopter pricing.

What integrations does Steadwing support? Out of the box: Datadog, PagerDuty, OpsGenie, Slack, GitHub, Elasticsearch, and Splunk. Grafana and Prometheus support is in beta.

Can Steadwing automatically fix production issues? Yes, in autonomous mode Steadwing can push PRs, trigger rollbacks, and make config changes. In approval mode (recommended for most teams starting out), it prepares the fix and waits for human approval.

Is Steadwing better than PagerDuty? Steadwing is not a replacement for PagerDuty—it works alongside it. PagerDuty handles alert routing. Steadwing handles the investigation and remediation that comes after the alert fires.