What is the best alternative to Resolve AI for AI-driven incident response?

Teams leaving Resolve AI are usually after one of two things: a root cause tied to a named change rather than an agent's narrative, or a hard bound on what an agent may do in production. NOFire AI, Traversal and Cleric each answer one of those differently.

Is there a like-for-like replacement for Resolve AI?

No single product matches its shape. Traversal is closest on unattended investigation, NOFire AI on causal accuracy and enforced action bounds, and Cleric on the on-call assistant framing. The right one depends on which half you valued.

What happens to Skills if we move?

Skills are Resolve AI specific, so the encoded knowledge does not transfer directly. The underlying runbooks do, and most alternatives read the same sources those runbooks point at.

Can we run two of these side by side during an evaluation?

Yes, and it is the only way to compare them honestly. Point both at the same alerts, score the first hypothesis on incidents whose true cause you already know, and compare on that.

Resolve AI alternatives

Q: How long does a switch take?

The integrations are the work, not the migration. Every product in this category reads the telemetry, deploy history and incident records you already have, so the connectors take days rather than the quarter a catalog migration would.

At a glance

	Resolve AI	NOFire AI	Traversal
Core idea	Agents in the on-call rotation, carrying encoded runbooks	A causal model of production, answers tied to a named change	A modelled graph of production, searched causally
Published accuracy	Up to 5x faster MTTR, self-reported	89% Top-1 on RCAEval, a public benchmark	Not published
Bounding agent actions	Integration permissions, SSO and RBAC	Blast radius enforced as a policy bound before an action runs	Not published
Before a change ships	Not published	Deployment risk analysis on the proposed change	Marketed as incident prevention
Encoding team knowledge	Skills, plus MCP and an API	MCP server and API	Not published as a distinct feature
Deployment	SaaS	Read-only collectors, in-VPC processing, BYOC	Bring your own cloud

Why teams look for an alternative

Resolve AI is a coherent product and the teams that fit it stay. The evaluations that end elsewhere tend to turn on one of three things, and none of them is a complaint about the agents themselves.

The first is evidence. Resolve AI publishes an outcome figure, up to 5x faster MTTR, and no accuracy figure against a public benchmark. That is normal for the category, but a buyer who has been burned by a tool that produced confident wrong answers usually wants a number they can check rather than an outcome they have to reproduce. When that becomes the deciding question, the shortlist narrows to whoever has published a method.

The second is authority. Agents that triage alerts, work incidents and run operational workflows on a trigger are acting on production. Resolve AI scopes that through integration permissions, SAML SSO and RBAC, which answers who may act. It does not publish an answer to how much a single action is allowed to affect. In organisations where a change advisory process exists for humans, that gap is usually what stalls the rollout rather than anything about accuracy.

The third is timing. All three of Resolve AI's published use cases begin after something has fired. Teams whose expensive incidents are the ones that should never have shipped end up looking for a product that reads the same model before the deploy rather than after the alert.

Where the current tool still wins

If a team has real runbooks and a real rotation, Skills is hard to beat, and that is worth saying plainly before recommending a move. Encoding institutional knowledge so an agent applies it consistently at 2am solves a problem that no amount of causal modelling addresses: the knowledge already existed, and the failure was that nobody applied it under pressure.

The operational automation piece is also genuinely differentiated. Running recurring workflows on a schedule or a trigger takes on the toil that sits entirely outside incidents, and most products in this category do not touch it at all. A team whose real cost is the weekly manual chore rather than the quarterly outage is solving the wrong problem by switching.

The on-call framing matters too. Agents that join an existing rotation are easy to adopt, easy to explain to a sceptical team, and easy to withdraw if they do not work out. A product that operates in parallel to the humans rather than inside their process asks more of the organisation.

How to switch

Do not migrate first. Run the alternative alongside Resolve AI for a fortnight on the same alert stream, and score the first hypothesis rather than the eventual one on incidents whose true cause you already know. That single measurement is the whole decision, and it is the one neither vendor's marketing can settle for you. The AI SRE Benchmark sets out how that scoring works on a public dataset if you want a method to copy.

Write down the authority bound before the trial, not after. If the requirement is that no single agent action may affect more than a defined share of traffic, that is a testable requirement, and what blast radius analysis is covers what it consists of. Products that cannot express it will not acquire the ability during a pilot.

Expect the connectors to be the work. Every product here reads the telemetry, deploy history and incident records you already keep, so the switching cost is integration time rather than a migration project. Skills do not transfer, but the runbooks they were built from do.