Is Resolve AI or Traversal the better fit for AI-driven incident response?

Resolve AI puts teams of agents into the on-call rotation and lets you encode your runbooks as Skills. Traversal models production as a graph and searches it causally, with workers that act unprompted. Resolve AI is organised around the responder, Traversal around the system.

Are Resolve AI and Traversal direct competitors?

Yes. Both sell unattended alert triage and root cause analysis to the same buyer, and both position against the manual investigation an on-call engineer does today.

Which one is better for a large estate?

Traversal markets explicitly at petabyte scale and publishes node counts in the millions for its production model. Resolve AI does not publish a scale figure, so it is worth asking for one directly in an evaluation.

Do either of them act on production without a human?

Both describe unattended action. Traversal publishes automated remediation and workers that act unprompted. Resolve AI runs operational workflows on a schedule or a trigger. Bound that authority explicitly during a trial in either case.

What else is in this category?

Cleric, Neubird and NOFire AI sell into the same evaluations. Any shortlist that starts with two names is worth widening before pricing conversations begin.

Resolve AI vs Traversal

At a glance

	Resolve AI	Traversal
How it is framed	AI agents that run your software	An AI SRE that triages alerts, finds root cause and prevents incidents
Approach to root cause	Teams of agents investigating alongside engineers	A causal search engine over a modelled map of production
Unattended operation	Agents join every on-call rotation and work alerts and incidents	Workers described as acting unprompted, plus automated remediation
Encoding team knowledge	Skills, alongside MCP and an API	Not published as a distinct feature
Feeding development	Not published	Production context returned to development, marketed as code resilience
Stated scale	Not published	Petabyte scale, with a production model shown at over 1.7 million nodes
Deployment	SaaS	Bring your own cloud
Published outcome figure	Up to 5x faster MTTR and 75% higher productivity	Not published

How the two differ in practice

The clearest way to separate these two is to ask what each one treats as the unit of work.

For Resolve AI it is the responder. Agents join the on-call rotation, triage alerts the way a person in that rotation would, and escalate into a team of agents that investigates an incident alongside the engineers already in the channel. Skills is the mechanism that makes this specific to a company: the runbooks, the conventions and the undocumented knowledge that lives in a few people get encoded so the agents apply them consistently. Operational automation extends the same idea past incidents, running recurring workflows on a schedule or a trigger.

For Traversal it is the system. The product builds a model of production as a graph, publishes node counts in the millions for that model, and then searches it causally to trace an incident across services, dependencies and changes. Its workers are described as proactive rather than summoned, acting on what the search surfaces rather than waiting to be paged into a channel. Diagnosis feeds two directions: forward into automated remediation, and backward into development as production context that shapes the next change.

Those are different bets about where the difficulty sits. Resolve AI bets that the knowledge exists in the organisation and the problem is applying it at 2am. Traversal bets that the estate has outgrown anyone's ability to hold it in their head, and that the map has to be built and searched by machine before any knowledge is useful.

Where each one is stronger

Resolve AI is stronger where the institutional knowledge is real and the process is the failure point. A team with mature runbooks, a well-defined rotation and a recurring toil list gets a direct return from encoding that into Skills, and the on-call framing means adoption does not require rethinking how the team works. It is also the more legible model for an organisation that wants agents assisting named humans rather than operating in parallel to them.

Traversal is stronger where scale is the problem. In an estate where no single engineer knows what depends on what, a modelled graph and a causal search over it does work that no amount of encoded runbook can do, because the runbook cannot describe a dependency nobody documented. The bring-your-own-cloud deployment also matters to buyers who cannot send production telemetry to a vendor tenant, and the code resilience loop is aimed at the teams that want incidents to change what ships next rather than just close faster.

The published evidence is uneven in both directions, and that is worth naming. Resolve AI publishes an outcome figure and no scale figure. Traversal publishes a scale figure and no outcome figure. Neither has published results against a public root-cause benchmark, so an evaluation has to generate its own comparison on its own incidents.

How to choose

Run both against incidents that have already been resolved, where the true cause is known, and score the first hypothesis rather than the eventual one. That single test separates a product that reaches the right answer from one that reaches a plausible one, and it is the measurement neither vendor currently publishes. The AI SRE Benchmark sets out how that scoring is done on a public dataset if you want a method to copy.

Then decide the unattended-operation question before pricing rather than after. Both products act on production, and the bound on that authority is a requirement to write down and test, not a setting to discover later. The wider category is worth a look too: what an AI SRE actually is covers the shape of the market these two sit in.