Resolve AI — AI agents for production engineering and incident response. Best for Engineering teams with high-volume production incidents wanting faster MTTR, SRE and platform teams seeking to reduce on-call burnout, Organizations with complex microservices needing automated root cause analysis. Contact Sales pricing.
Engineering teams with high-volume production incidents wanting faster MTTRSRE and platform teams seeking to reduce on-call burnoutOrganizations with complex microservices needing automated root cause analysisEnterprises requiring SOC 2 and GDPR-compliant AI operationsTeams looking to embed tribal knowledge into automated workflows
Not ideal for
Small startups with simple monitoring and few incidentsTeams without existing incident management processes or toolsOrgs that prefer manual control and distrust AI-driven actionsBudget-constrained teams needing a free or low-cost solutionEnvironments where integration with specific tools (e.g., Slack, PagerDuty) is mandatory but unsupported
Resolve AI fills a genuine gap for mature engineering orgs drowning in alerts. If you have a complex production environment and want to reduce on-call burnout, it's worth a trial. But it's overkill for small teams or simple setups.
Resolve AI is purpose-built for engineering teams that deal with high volumes of production incidents and want to reduce the cognitive load on on-call engineers. The core value proposition is clear: AI agents take over the initial triage and investigation, so humans only step in when deep expertise is needed. If your team spends significant time in war rooms or chasing root causes, this could dramatically cut MTTR. The ability to build custom agents and plug into existing ecosystems via MCP, API, and Skills is a strong differentiator. However, there are caveats. The platform is clearly enterprise-focused, with pricing likely starting at a premium tier, and there's no free tier visible from the page content. It may not fit smaller teams or those with simple monitoring setups. Compared to alternatives like PagerDuty's AI ops or Opsgenie, Resolve AI leans more into autonomous action rather than just intelligent alerting. One missing piece is detailed integration listings – the page mentions MCP, API, and Skills, but doesn't name specific tools like Slack or Jira. Real-world usage likely requires a fair amount of setup and tuning, and the 'tribal knowledge' capture could be a double-edged sword if not properly maintained. Overall, for a mature org with a billion-dollar ads platform, it might be a game changer. For startups, start with simpler tools.
Skip Resolve AI if Skip Resolve AI if you lack established observability tools (Datadog, PagerDuty, etc.) or have a small team with minimal on-call burden.
Latest from Resolve AI
Updated 4 days ago
Across the latest 8 updates: 1 launch and 7 news mentions.
How likely is Resolve AI to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
60
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Resolve AI
Resolve AI provides AI-powered agents that help engineering teams manage production environments by handling on-call, incident response, and operational tasks. Designed for platform and SRE teams, the platform enables agents to triage alerts, investigate incidents, and run workflows autonomously, reducing the need to pull engineers into war rooms. Key features include agent-driven on-call rotations, co-working agents with engineers for root cause analysis, background agents for scheduled operational tasks, and the ability to build custom agents using MCP, API, and Skills. The platform also captures tribal knowledge and integrates with existing tools. With trusted adopters like DoorDash, Resolve AI claims up to 5x faster MTTR and 75% higher productivity. It offers enterprise-grade security with SAML SSO, RBAC, data redaction, encryption, and SOC 2 Type II compliance. Compared to traditional incident management tools, Resolve AI focuses on autonomous agent action rather than just alert routing.
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Key Features
AI agents handle on-call rotations with triage and investigation
Co-work with agents during incidents to find root cause
Automate operational tasks on schedule or trigger with background agents
Build custom agents using MCP, API, and Skills
Capture and retain tribal knowledge of your system
SAML SSO, RBAC, and admin controls
Data redaction, encryption, and retention controls
Customer isolated data per org
Activity and support access logging
SOC 2 Type II certified
GDPR and HIPAA compliant
Real-world workflow fit
Concrete scenarios for the personas Resolve AI actually fits — and what changes day-one when you adopt it.
On-call SRE
You get paged at 3am for a P1 alert. Resolve AI agents have already gathered logs, metrics, and trace data, and present a root cause hypothesis in Slack. You verify the findings and approve a fix PR drafted by the agent.
Outcome: MTTR reduced from hours to minutes; you handle the incident without waking other engineers.
Platform engineer
You create a custom agent that runs a weekly health check across your Kubernetes clusters. The agent queries Prometheus for resource usage, checks for pod failures, and posts a summary to your team's Slack channel.
Outcome: Routine monitoring automated, freeing you to focus on feature work.
Incident commander
A multi-service outage occurs. You spin up a Workbench (May 2026) where multiple agents investigate in parallel—one traces code changes, another checks telemetry, a third reviews recent deployments. Agents and engineers collaborate in the same space to build a causal timeline.
Outcome: Collaborative investigation reduces mean time to resolve from hours to under 30 minutes.
Use Cases
Delegate on-call alert triage to agents for faster initial response
Investigate production incidents collaboratively with agent-driven root cause analysis
Automate health checks and report generation on a schedule
Integrate agents with PagerDuty and Slack to reduce war-room involvement
Generate fixes or PRs automatically from incident investigations
Run background agents to proactively identify issues before they escalate
Models Under the Hood
Claude Opus 4.6Claude Sonnet 4.6
Limitations
Pricing is enterprise-only (contact required), no self-serve tiers or free trial mentioned. Platform relies on cloud connectivity and existing tool integrations; offline or air-gapped setups are not supported. Context window and rate limits were not publicly documented. Enterprise deployments may require Forward Deployed Engineering support, adding cost. The tool's value diminishes for teams with low alert volume or simple infrastructure.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Forward Deployed Engineering services may be billed separately
•Custom integrations or high agent usage may incur overage
•No public pricing—requires sales process to see costs
Where the pricing makes sense
The company stage and team size where Resolve AI's pricing actually pencils out — and where peers do it cheaper.
Resolve AI is priced as an enterprise-only product via contact sales, so it fits large organizations with budgets for premium SRE tools. Smaller teams or startups should consider PagerDuty Operations Cloud (starts at $21/user/month) or Datadog Incident Management (included in Pro plans). Resolve AI doesn't offer a free tier or self-serve option, which limits access for teams that could benefit from a trial.
Setup time & first value
How long it actually takes to get something useful out of Resolve AI — broken out by persona, not the marketing-page minute.
For teams already using PagerDuty, Slack, and Datadog, basic setup (connecting integrations and configuring on-call triage) takes under an hour. Customizing knowledge bases, running background agents, and setting up Workbench may take a few days. Full value realization (living model adapting to environment) typically occurs within 2–4 weeks of use.
Switching to or from Resolve AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From PagerDuty Operations Cloud: integrate Resolve AI via PagerDuty webhook; agents automatically take over alert triage.
→From manual runbooks: import existing runbooks and tribal knowledge into Resolve AI's knowledge base for reuse.
→From Datadog Incident Management: Resolve AI agents natively read Datadog data; configure via Datadog integration.
Migrating out
↗To another AI SRE: export custom agents and prompt libraries (MCP/API) if the target platform supports them.
↗To manual processes: Resolve AI does not lock data—you can export investigation reports and runbooks.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2026-05-21: Launched background agents, 2x root cause quality improvement, and Workbench for collaborative investigation.
•2026-04-07: Announced Resolve AI Labs to advance AI foundations for autonomous production operations.
•2026-02-11: Raised $125M Series A from Lightspeed and Greylock to scale AI for prod.
•2026-02-05: Published early impressions of Claude Opus 4.6 on long-horizon agent tasks.
