Should I use NOFire AI or Resolve AI for incident root cause analysis?

Both put AI agents on call. Resolve AI is built around agents that work an on-call rotation and carry a team's tribal knowledge through Skills. NOFire AI is built around a causal model of production, so a root cause comes back attached to a named change and a measured blast radius.

Do NOFire AI and Resolve AI replace each other, or can they run together?

They overlap on alert triage and investigation, so most teams pick one. Both expose an MCP server, so a team already invested in one can query the other rather than migrating everything at once.

How do the accuracy claims compare?

NOFire AI publishes 89% Top-1 accuracy on RCAEval, a public fault-injection benchmark. Resolve AI publishes an outcome figure of up to 5x faster MTTR. The two are not measuring the same thing, so they cannot be ranked against each other.

Which one works without an existing observability stack?

Neither. Both read from the telemetry, deploy history and incident records a team already has. A thinner stack means a thinner model in both products.

Can either run inside our own cloud account?

NOFire AI publishes read-only collectors, in-VPC processing and a BYOC option. Resolve AI publishes a SaaS deployment with SAML SSO, RBAC and a commitment that customer data is not used to train models for others.

NOFire AI vs Resolve AI

At a glance

	NOFire AI	Resolve AI
Approach to root cause	A causal model of the live system, with each answer tied to a named change	Teams of agents that investigate alerts and incidents alongside engineers
Published accuracy figure	89% Top-1 on RCAEval, a public benchmark of 735 injected faults	Up to 5x faster MTTR and 75% higher productivity, self-reported
Bounding agent actions	Blast radius computed from the dependency graph and enforced as a policy bound before an action runs	Not published
Before a change ships	Deployment risk analysis on the proposed change	Not published
Extensibility	MCP server and API	MCP, API and Skills, used to encode tribal knowledge
Data handling	Read-only collectors, in-VPC processing, no PII egress, BYOC	SaaS, with SAML SSO, RBAC, and customer data not used to train models for others
Pricing	On request	On request

How the two differ in practice

Resolve AI describes its product as AI agents that run your software. Agents join every on-call rotation to triage and investigate alerts, teams of agents work incidents with engineers to reach a root cause and a fix, and operational workflows run on a schedule or on a trigger. The extensibility story is Skills: the knowledge a team keeps in runbooks and in people's heads gets encoded so the agents apply it the way a senior engineer would.

NOFire AI starts one layer lower, at the model of the system rather than the agents on top of it. It builds a live causal model of production from telemetry, deploy history, dependencies and incident records, and answers an incident by tracing the symptom back through that model to the change that caused it. The output is not a ranked list of suspicious signals. It is a specific deploy, config change or resource event, with the dependency path that carried the effect.

That difference shows up most clearly in what each product does with an answer once it has one. Because NOFire AI holds a dependency graph, it can compute the blast radius of a proposed action before the action runs, and refuse or escalate anything that exceeds an approved bound. Resolve AI does not publish an equivalent runtime bound, and scopes agent authority through integration permissions, SSO and RBAC instead.

The second practical difference is timing. NOFire AI applies the same model before a change ships, scoring deployment risk on the proposed change rather than waiting for the alert. Resolve AI publishes no equivalent pre-deploy capability, and its three stated use cases all begin once something has already fired.

Where each one is stronger

Resolve AI is the stronger fit where the bottleneck is human process rather than diagnosis. If a team has good runbooks that nobody has time to follow at 2am, Skills is a direct answer to that: the knowledge is already written down, and encoding it puts an agent in the rotation that applies it consistently. The operational automation piece is genuinely useful for the recurring toil that sits outside incidents entirely, and the on-call framing is easy for an existing rotation to adopt without rethinking how the team works.

NOFire AI is the stronger fit where the bottleneck is diagnosis, or where unattended action is the thing holding adoption back. If postmortems keep landing on the wrong cause, or if the objection to agents is that nobody will let one touch production, the causal model and the enforced blast radius are aimed exactly at that. The published benchmark exists for the same reason: an accuracy figure a buyer can check against a public dataset is a different kind of claim from an outcome figure a buyer has to take on trust.

Neither product removes the requirement to have telemetry worth reading. Both build their picture from what a team already collects, and a sparse stack produces a sparse model in both.

How to choose

Ask which half of the incident is actually costing the time. If most of the cost is the handover, the escalation and the runbook nobody ran, the agent-and-Skills model is the closer fit. If most of the cost is the hour spent between the alert and the first correct hypothesis, or the change that should never have shipped, then a causal model and pre-deploy risk analysis is the closer fit.

Then ask what has to be true before an agent is allowed to act. If the answer involves a hard bound rather than a permission scope, that is a specific requirement to test in both products rather than a preference. See what blast radius analysis is for what that bound consists of, and the AI SRE Benchmark for how the accuracy figure above was produced.