Resolve.ai

On-Call, Delegated

• Resolve.ai is a platform of AI agents that perform software operations work — triaging alerts, investigating incidents and running operational tasks, positioned as participants in the rotation rather than as a dashboard.
• Three modes: agents on every on-call rotation for triage, collaborative incident resolution alongside engineers, and scheduled or triggered background automation.
• The customer list is the substance — Coinbase, DoorDash, Expedia Group, Snowflake, Salesforce, MongoDB, Robinhood, Cisco and Autodesk among more than twenty logos.
• Stated results are up to 5x faster MTTR and 75% higher productivity, both vendor figures published without a baseline.
• It is contact-sales only, with integration via MCP, API and Skills rather than a published connector list.

Scope, Customers And Claims

Why On-Call Is The Right Thing To Attack

Framing this as a debugging tool undersells what it is aimed at. The target is a staffing and wellbeing problem that most engineering organisations have and few solve.

On-call is a genuine cost centre that rarely appears in any budget. Engineers carry a pager outside working hours, sleep badly during rotations, lose the following day, and in many organisations cite it as a leading reason for leaving. The cost is real and shows up as attrition rather than as a line item.

The work itself splits unevenly. A large share of pages are noise — a threshold crossed briefly, a known flaky dependency, an alert nobody has tuned since it was written. Somebody still has to wake up, look, and decide it was nothing.

Dependency-graph approaches like Traversal attack the same night from the diagnosis end. That triage layer is what Resolve.ai is offering to absorb, and it is the correct target. It is high-volume, largely mechanical, and it is what makes on-call miserable rather than the genuinely hard incidents, which engineers mostly find interesting.

The framing of agents participating in every rotation rather than assisting one is a deliberate distinction. It positions the product as reducing how often a human is woken rather than helping a woken human work faster — and those are different value propositions.

The DoorDash testimonial about fewer engineers pulled into war rooms points at the same thing. The saving is not only speed; it is how many people an incident consumes.

Most pages are noise, and somebody still has to wake up to establish that. The triage layer is what makes on-call miserable, not the interesting incidents.

The Customer List Is The Real Evidence

In a category full of unverifiable claims, the logo wall here is doing more work than the metrics, and it is worth explaining why.

Coinbase, DoorDash, Snowflake, Robinhood, MongoDB, Salesforce and Cisco are organisations with mature platform engineering functions, strict change control and, in several cases, regulatory oversight of their production systems. Coinbase and Robinhood in particular operate under financial regulation where uptime and incident handling are examined.

Companies at that level do not put an unproven agent into an on-call rotation as a trial. Getting through their security review, their change management and their reliability engineering leadership is a substantial filter, and passing it repeatedly says something a benchmark cannot.

The published figures deserve much more scepticism. Up to 5x faster MTTR and 75% higher productivity both carry the qualifier doing the heavy lifting — up to — and neither states a baseline. Faster than what? A team with mature runbooks or one without?

MTTR is also an average dominated by outliers, so a large improvement can come from resolving a handful of very long incidents rather than from everything getting faster. That is genuinely valuable and it is a different claim.

The honest reading: treat the logos as evidence and the percentages as marketing. Then run your own measurement during a pilot, because your baseline is the only one that matters.

What To Govern Before An Agent Joins The Rotation

Putting an agent into on-call is an operational decision with consequences, and the questions are the same ones you would ask about a new engineer with unusual access.

What can it actually do? There is an enormous difference between an agent that investigates and reports, and one that takes action on production systems. Establish where that line sits and who can move it.

What happens when it is wrong? Any automated triage will occasionally classify a real incident as noise. That is the failure that matters, because it delays human attention on something genuine. Ask how confidence is expressed and what threshold escalates to a person regardless.

Who is accountable? If an agent triages an alert as benign and it was not, the post-mortem needs an owner. Agreeing that in advance is much easier than agreeing it afterwards.

A narrower agent such as Sentry Seer only ever reads your error telemetry. What does it see? Production access is broad by nature. An agent investigating incidents reads logs, and logs contain customer data more often than teams assume.

How does it fit change management? In regulated environments, automated changes to production sit inside controls that were written assuming a human. Reconciling that is a compliance conversation, not a configuration one.

None of these are reasons not to adopt it. They are the work that adoption requires, and the organisations on that logo wall will all have done them.

1. Start in report-only mode, with the agent investigating but taking no action.
2. Measure your current MTTR and page volume before anything changes — you cannot claim an improvement without a baseline.
3. Track how often it classifies something as noise that was not, because that is the risk, not slowness.
4. Define which actions may run unattended and which escalate, in writing.
5. Confirm what production data the agent reads, and clear it with whoever owns customer data.

You Will Be Talking To Sales

This site does not publish plan prices, and Resolve.ai does not publish anything at all — there is no self-serve entry point, only a contact route.

That is consistent with the product and the customer list. An agent with production access is not something a company sells by credit card, and the deployment involves security review, integration work and access decisions that need a conversation.

In this category the quote is normally driven by estate size — hosts, services or monitored resources — sometimes combined with incident volume or engineering headcount. Those are the variables to ask about early, because per-resource metering behaves badly for organisations running many small services.

The comparison worth running is not against another licence. It is against the fully loaded cost of on-call: the engineering hours consumed by incidents, plus the attrition cost of a rotation people dislike, plus whatever an hour of downtime costs your business.

One practical point specific to this product: because integration is described as MCP, API and Skills rather than as a list of named connectors, the implementation effort is genuinely unknown from outside. Ask what a deployment requires against your specific observability stack, and how long before the agent is useful rather than merely installed.

What The Site Does Not Tell You

Being straightforward about the gaps is more useful than filling them with assumptions, and there are several here.

No founders, funding or headquarters appear on the pages reviewed. For an enterprise purchase involving production access, company stability is a legitimate diligence question, and the absence means you will have to ask.

No named observability integrations. MCP, API and Skills describes an approach rather than a compatibility list. Whether it works cleanly with your specific monitoring stack is the first thing to establish.

No detail on the agent's action boundary. The material describes agents resolving incidents without setting out what resolving involves — investigation, recommendation, or execution.

Testing platforms like Functionize publish considerably more at a similar deal size. No published security posture on the pages reviewed, which is unusual for a product marketed to Coinbase and Robinhood. Those organisations will have received it under NDA; you should ask for the same.

The reasonable interpretation is that this is a sales-led enterprise product where the website exists to start a conversation rather than to inform a decision. That is a legitimate model and it means an evaluation here is a process, not an afternoon.

• Founders, funding and headquarters are not published
• Integrations are described as an approach, not a named compatibility list
• The boundary between investigating and acting is not specified publicly
• Outcome percentages are published without any stated baseline