Resolve AI
An AI on-call engineer that investigates production incidents by reasoning across your logs, traces and code.
Highlights
- Connects to observability, logs, traces, code, and cloud infrastructure for full-stack context
- Autonomously triages alerts and correlates signals across services to surface a likely root cause
- Recommends or executes remediations: rollbacks, config changes, capacity and infrastructure actions
- Provides an agentic chat interface to query production systems in natural language
- Builds a living model of your environment, services, and dependencies over time
- Guided code-change suggestions tied to the incident's root cause
- Created by the team behind OpenTelemetry, with deep observability heritage
- Enterprise controls for human approval before production actions
External link — opens resolve.ai in a new tab. Resolve AI is a third-party product; we are not affiliated with it.
About Resolve AI
What it is
Resolve AI connects to observability data, logs, traces, source code and cloud infrastructure, then works through them when an alert fires — correlating signals across services to propose a likely root cause, and recommending or executing remediations such as rollbacks and config changes. There is an agentic chat interface for querying production in plain language. It was founded in 2024 by ex-Splunk leaders who were involved in creating OpenTelemetry.
Why it's different
Most incident tooling shows you data and leaves the reasoning to whoever was unlucky enough to be paged. Resolve does the first pass of the investigation itself, which is the expensive part at three in the morning. The founders' background in OpenTelemetry is a real signal about whether the integrations will work. What to weigh: it needs broad read access across your production estate to be useful, which is a significant trust and security decision, and letting it execute remediations rather than suggest them is a step most teams should not take early. It is enterprise-priced with no self-serve tier.
How people use it
The realistic deployment is as a first responder that has already assembled the context by the time a human opens the laptop — which service degraded first, what changed recently, which traces show the error. Teams generally start with it in read-only advisory mode, compare its conclusions against what the on-call engineer actually found for a few months, and only then consider letting it act. The chat interface gets used far more than expected for ordinary questions about production, not just during incidents.
Written by the n3os team. We are not affiliated with Resolve AI.
This listing was written from public information, without Resolve AI’s involvement. If you own it and something here is wrong — or you would rather not be listed at all — email us and we will correct or remove it.
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