Start with the outcome your team needs
Choose Prilog when the job is to turn an application error or supported bug report into a proposed repository change your engineers can review. Prilog connects production evidence and code, investigates the failure, prepares a patch, and supports configured PR automation. You can start with logs, traces, or a supported public GitHub issue and expand to ongoing evidence collection.
Resolve AI positions its platform around AI SRE work: alert triage, incident mitigation, remediation, and recurring operational tasks. It also describes a path from production investigation to code changes through coding-agent integrations. The two products overlap on remediation, while their published starting points emphasize different engineering workflows.
This comparison is written by Prilog and reflects linked product information checked on October 25, 2026.
What Resolve AI documents
Resolve AI's product overview describes agents for triage, mitigation, remediation, and scheduled or triggered production work. Its coding-agent announcement explains how production context and remediation plans reach Claude Code, Codex, and Cursor, and how the coding agent can implement changes and open a PR. It also describes post-deployment monitoring through background tasks.
These are capabilities to evaluate in your environment. This article does not assume that incident investigation ends at a diagnosis or that code remediation belongs exclusively to Prilog.
Compare the work you want to automate
| Decision | Prilog | Resolve AI |
|---|---|---|
| Primary evaluation | Application evidence or a supported issue through to a proposed code fix. | AI SRE workflows spanning alerts, incidents, mitigation, remediation, and operational tasks. |
| Starting a code investigation | Connected source, OTLP, selected log upload, or supported public GitHub issue import. | Production context and investigations across connected operational tools. |
| Coding-agent access | CLI and shared skill, or authorized hosted MCP for existing projects. | Plugin-based production context and remediation plans for coding agents. |
| Review | Inspect a generated diff or a configured auto-created PR; repository review still applies. | The coding-agent workflow describes reviewed write actions and PRs. |
For Prilog setup, use automatic bug fixing, the plugin guide, or hosted MCP. Directory submissions remain subject to approval; CLI and supported direct MCP access are separate routes.
What at-scale bug fixing should mean
Adding an agent to every alert is only useful if it reduces the team's work. For Prilog, the operating loop connects recurring evidence to a project, repository, investigation outcome, and proposed change. Analysis and publication settings determine how eligible work proceeds, and usage controls help bound the workload.
Start with one service and track accepted fixes, additional edits, review time, and repeated failures. Expand only when the output helps the engineers responsible for the code. This evaluates the recurring process instead of counting generated patches alone.
Five-minute average and inspectable quality
Prilog reports a five-minute average fix time in its product information. Actual duration depends on the issue, environment, and verification work. Measure time to a reviewable patch separately from incident mitigation, merge, and deployment when comparing these products.
The reported 91.2% SWE-bench Verified result (456/500) measures a separate issue-solving evaluation. It retains 413 previously passing patches and regenerates 87 cases using evaluation feedback. The submission remains open as of October 25, 2026; it is self-reported, not pass@1, and not a head-to-head comparison with Resolve AI. Inspect the methodology and public artifacts.
Community contributions you can read
We contribute open-source bug fixes free of charge to give back to the projects engineers rely on. The merged Keycloak realm-migration contribution and Prefect Teams webhook contribution provide public code and review records. Our merged-fix guide explains what these examples establish.
Public contributions and a benchmark serve different purposes. Use the PRs to inspect concrete changes and the benchmark to inspect a disclosed evaluation; use your own incidents to decide fit.
Put Prilog to work on a real bug
Start with three free publishable fixes or follow GitHub issue to pull request. You can also compare Sentry Seer, Datadog Bits Code, and Superlog.
