Cleric alternatives, 2026

At a glance

	Cleric	NOFire AI	Traversal	Resolve AI
Default authority	Read-only by default, acting not offered	Read-only collectors, acting under an enforced bound	Read-only and agentless, actions proposed for approval	Integration permissions, SSO and RBAC
Where agent code runs	Not applicable. The agent does not act	Read-only collectors. Coding agents run in a microVM via brig, our open-source sandbox	Nowhere. Agentless and read-only	In your own environment, scoped by permissions, SSO and RBAC
Learning across incidents	Operational memory, a resolved incident informs the next	Not published as a distinct mechanism	A Production World Model updated from each incident	Skills, encoding your runbooks
Auditability	Every investigation auditable, and it leads on this	Evidence path attached to each answer	An audit trail of data accessed, not only actions taken	Audit-ready records kept automatically
Published accuracy	92% actionable findings, self-reported	89% Top-1 on RCAEval, a public benchmark	82% RCA accuracy, self-reported	Up to 5x faster MTTR, self-reported
Published outcome claim	92% actionable findings over 200,000 investigations, self-reported	Not the primary claim	82% root-cause accuracy and MTTR down 32 to 70%, self-reported	Up to 5x faster MTTR, self-reported
When work starts	On the incident	On the alert, and before a change ships	Unprompted workers	In the on-call rotation
Compliance posture	SOC 2 Type II, no training on customer data	In-VPC processing, BYOC	Bring your own cloud	SAML SSO and RBAC

Why teams look for an alternative

Almost everyone arriving here likes Cleric. The reasons for looking are about reaching the edge of what it is designed to do rather than about it doing that badly.

The first and largest is that read-only is a ceiling as well as a safeguard. It is the property that gets the rollout approved, and then twelve months later the organisation is comfortable with agents diagnosing and starts asking whether they can remediate the routine cases. Cleric is built never to answer yes to that. This is a coherent product decision rather than a gap, and it means a team whose ambition grows past diagnosis has to change vendor rather than change a setting.

The second is evidence. Cleric leads on auditability and memory, which are design arguments, and does not publish accuracy against a public benchmark. For a buyer whose deciding question is how often the first hypothesis is right, a design argument is not the answer, and only some of this category publishes a checkable one.

The third is volume. Operational memory compounds with incident frequency, so a team having incidents quarterly gets least from the feature that most distinguishes the product. That mismatch is usually discovered after purchase rather than before, because a short trial understates memory in either direction.

Where the current tool still wins

Read-only by default remains the strongest thing about Cleric, and it wins evaluations against more capable products for a reason that has nothing to do with capability. In an organisation with a change advisory process for humans, the question that stalls an AI rollout is authority rather than accuracy. A product that structurally cannot write to production retires that question before it is asked, where a bounded-action model has to argue its way through a review. If your blocker is a security team rather than an engineering one, that is worth more than any feature.

Operational memory is the second, and it is the capability least likely to transfer. Most alternatives investigate each incident from the same cold start. A product that gets better on your specific estate over time is doing something the others are not, and the benefit grows rather than plateaus.

Auditability is the third and it pairs with the first. Where somebody has to review what the agent did, an investigation trail designed to be read is a different proposition from one reconstructed from logs afterwards.

The honest summary is that Cleric optimises for getting deployed and staying trusted, and it is unusually good at both. If those were your constraints, moving trades a solved problem for an unsolved one.

How to switch

Establish what you actually need before shortlisting, because the reasons above lead to different places. If the driver is remediation, the requirement to write down is not permission but the bound: how much may a single action affect, enforced before it runs. What governing an AI agent in production requires covers what a testable version of that looks like, and a product that cannot express it will not acquire the ability during a pilot.

If the driver is evidence, score the first hypothesis rather than the eventual one, on incidents whose true cause you already know. The AI SRE Benchmark gives a method on a public dataset worth copying rather than inventing.

Take the security conversation early, not late. If read-only was what got Cleric approved, assume anything that can act will face the same review, and find out whether a bound satisfies your reviewers before you invest in a trial.

Run both in parallel for a fortnight on the same alert stream. Expect the connectors to be the work rather than the migration, since every product here reads the telemetry, deploy history and incident records you already keep.