incident.io vs. Resolve AI: Do You Need Better Coordination or Faster Root Cause?

On-call and incidents take up a real share of your engineering capacity, and most orgs never explicitly budget for them. Each alert takes an engineer away from product work, while each serious incident can pull the entire team away for an afternoon. Engineering leaders have to work out how to budget for this within their headcount, while also shipping the roadmap.

AI agents now promise to give some of that time back, and incident.io and Resolve AI are two of the most credible products making the promise. But they work in fundamentally different ways: incident.io is an incident management platform, and its agents orchestrate on-call and incident processes. Resolve AI is built for the investigation itself, helping engineers find the root cause and fix the issue.

Deciding whether you need one, the other, or both starts with being precise about what the work actually is.

What happens during on-call and incidents?

Every alert or incident comprises the same four clusters of work. In a pure, human-led on-call scenario, it looks like this:

1. Triage. An alert fires and the on-call (SRE or an engineer) decides whether it's real, how bad it is, and whether it deserves an incident or just a quick fix and a note.
2. Investigate. The same on-caller (and anyone else they have initially pulled in) works out what's affected, what changed, and which change caused this symptom through which path. In practice this means combing through dashboards, logs, traces, deploy history, and the memory of whoever knows that part of the system.
3. Remediate. Someone decides on a fix, gets it approved, executes it, and verifies the problem is actually gone.
4. Coordinate and close. People are assembled, stakeholders are updated, the record is maintained, and a postmortem is written.

Those aren't equal work. Triage is frequent but brief: most alerts are assessed and dismissed within minutes. The real cost there is the interruption. Remediation is usually quick once the cause is known; when it drags, it's mostly because the investigation was wrong. And coordination is steady, predictable work that tooling has been automating for a decade.

Investigation is where the time goes. It can take twenty minutes or a day; nobody knows which at the start, and the longer it runs, the more senior engineers get pulled in, because they're the ones who hold enough of the system in their heads to reason about it. Allocating an entire day from multiple senior engineers on your team is a massive cost.

The time distribution matters when AI enters the picture. Coordination was largely automated before agents arrived. Investigation is where the hours are, and it's the hardest cluster for AI, because it requires understanding your system rather than your process.

So when a vendor says "agents," ask the same three questions about each cluster:

• Coverage: how many of your alerts and incidents does the agent actually work on?
• Autonomy: how much of the work does it do without a human driving?
• Quality: is it right often enough that you'd act on its output?

Using agents for orchestration vs investigation

You can think about the above problem in terms of where you want the automation to land.

Orchestration agents land on the process around an alert or incident. The earlier generation of this tooling was workflow automation: paging rules, escalation policies, and channel creation. The agentic version goes further and takes over the human coordination work itself:

• When an alert fires, the agent pulls up context before anyone opens a laptop: similar past incidents, recent changes to the affected service, and a suggested severity.
• During the incident, it takes notes on the call, drafts and posts status updates, keeps stakeholders and the status page up to date, and answers questions in the channel so responders don't have to.
• Anyone joining late gets a summary of the incident so far, instead of having to scroll back through the channel.
• Afterward, it assembles the timeline, drafts the postmortem, and chases the follow-ups.

Measured against the coverage, autonomy, and quality, this is strong. It covers every incident, runs without a human driver, and maintains quality because the output is easy to verify at the moment: a wrong update or a bad summary gets caught immediately. What it leaves open is the middle of the work, investigating why the system broke.

Investigation agents land on the system behind the alert. These tools do the investigation themselves, taking a current picture of production: the code, the deploys, the infrastructure, the telemetry, and how the services depend on each other. Working from that picture:

• When an alert fires, the agent pulls what changed around the affected service: recent deploys, commits, config changes, and infrastructure events.
• It investigates the way an engineer would, querying logs, metrics, and traces and following the failure through the dependency chain rather than stopping at the service that alerted.
• It tests several explanations at once and drops the ones the evidence rules out, instead of committing to the first plausible one.
• It arrives with a conclusion an engineer can check: the change, the path from that change to the symptom, and the evidence for each step.

Coverage is complete because the agent sits on the alert stream. It can work every alert, not only the ones that become incidents. Autonomy runs throughout the investigation and ends at the fix, which then waits for human approval. Quality is dependent on the model and harness of the investigation agent. A model trained on production data, with a harness capable of diving through the system to find the root cause, will identify the exact issue and provide evidence to an engineer for remediation.

incident.io is the orchestration platform, and it's built around the incident. It maps services to teams so the right people get paged, and when an investigation runs, it assembles context for that incident by querying the sources you've connected. That design is exactly why it's strong at declaring, coordinating, and communicating an incident, and why responders see a first hypothesis within minutes.

Resolve AI is the investigation platform, and it's built around a model of the production system. A context graph holds how services actually depend on each other, built from the telemetry, code, and cloud accounts you connect rather than declared by hand, and kept current as the system changes. An investigation runs as a query against that model, so the gathering is already done before anything breaks.

Two things follow from that.

Investigation usually means understanding dependencies well beyond the service that raised the alert. When context gets assembled fresh for each incident, the third hop is much harder than the first, because nothing in the alert points at a service three dependencies away, and the relationship that would point there was never indexed. When the topology already exists, the third hop costs about the same as the first.

And for depth, incident.io leans on the engineer. Its desktop app can pull a live investigation into Claude Code or Cursor so you can steer it when you spot something it missed, which helps when you know the system well enough to spot it. A coding agent also works from your repository rather than your production state, so it can tell you what the code should do. Working out what the 14:32 deploy did to a connection pool three services downstream still comes back to a person.

Life with incident.io

A team that runs everything on incident.io usually has excellent process hygiene, because the platform makes the process nearly free:

• Triage is mostly handled by the platform. An incident is declared with one Slack command; severity, roles, and channels are set up automatically, and the agent checks whether a third-party provider, such as a cloud or payments vendor, is experiencing its own outage before anyone starts digging.
• Investigation runs against the incident. The agent reads the incident's context, past incidents, and change events alongside code, docs,and telemetry, then posts a root-cause hypothesis with supporting evidence and keeps reassessing while the incident remains open. It's a useful head start.
• Remediation stops at recommendations by design. The agent suggests next steps and can draft a fix as a pull request for review, and by incident.io's own description, a reviewed pull request is the only change it can make to your systems.
• Coordination and close-out barely need the engineers at all. Updates, status pages, timelines, follow-ups, and postmortems mostly run themselves.

So with incident.io, you get the process side of incidents handled about as well as it can be, from paging through postmortem, with very little engineer effort spent on any of it. On investigation, the agent hands responders a head start when an incident is declared.What stays with your engineers is the investigation that runs past the first plausible explanation, into the services the alert never named.

Life with Resolve AI

The same team on Resolve AI keeps whatever paging and incident tooling it already has:

• Triage happens on the alert stream itself. The on-call agent assesses every alert with adjustable depth per alert type, from a quick check to a full investigation, so the long tail of alerts that never become incidents still gets worked.
• Investigation is the center of the product. Specialized agents using post-trained models pursue competing hypotheses in parallel, a verifier checks the leading explanation against production evidence, and the output is a causal timeline: which change caused which symptom through which dependency path, with the evidence attached.
• Remediation follows from the findings. Resolve AI proposes actions such as silencing the alert, reverting the suspect commit, opening a remediation PR, or running a GitHub Action. Every action requires human approval, and the models never directly call write APIs.
• Coordination and close-out are supported. Resolve AI posts findings and summaries in Slack or Teams channels and can draft postmortem material, but paging, escalation policies, status pages, and the incident record live in whatever platform the team already uses.

Coinbase cut investigation time by 72% and reaches a likely root cause in under ten minutes, Zscaler made root-cause investigation 75% faster with over 30% fewer engineers pulled into each incident, and Salesforce reports roughly 60% lower MTTR.

For teams that can't hand credentials to a vendor cloud, Resolve AI's Satellite component runs inside their own infrastructure and applies redaction before anything leaves it.

Life with both

There's no out-of-the-box integration between incident.io and Resolve AI today. Both expose MCP servers and APIs, so connecting them is eminently feasible, but it's an integration project rather than a toggle.

The pattern that works splits the clusters by strength:

• incident.io remains the incident command system of record, owning declarations, roles, escalations, comms, status pages, and postmortems.
• Resolve AI covers the alert stream and the investigation, with findings posted into the incident channel where the response is running.
• Remediation ownership gets decided up front, so one system proposes fixes and responders aren't reviewing two agents' recommendations in the same channel.

Run this way, a team gets high coverage across all four clusters. Run carelessly, with both products auto-investigating the same alerts, and you get duplicate investigation threads and two agent voices talking over each other.

The trade-offs in each approach

With incident.io alone, you get a first-rate coordination layer and a capable investigator inside it. The trade-off is depth: the investigation is anchored to the incident record, so it is strongest on incident-local context and thins out as the cause moves further from the service that alerted. Remediation stops at drafted PRs by design.

With Resolve AI alone, you get depth and coverage on the investigation layer that consumes the most engineer time, and you keep your existing incident stack, because Resolve AI integrates with it rather than replacing it. The trade-off is that the orchestration layer is still yours to run, and pricing requires a sales conversation rather than a public page.

With both, you get the widest coverage and the most governance overhead: two systems with deep access to observability, code, docs, and chat, duplicated credentials and permissions, and an integration to build and maintain.

So, do you need better coordination or faster root cause? For most teams, coordination is already the solved half. The hours you're losing are in the investigation, and that's the problem Resolve AI was built around.

Book a demo to see how Resolve AI investigates an alert in your own production environment.