Resolve AI
AI agents that handle on-call, incidents, and operational tasks—so engineers can get back to building.
Resolve AI delivers real, measurable wins for complex stacks and high alert volumes—87% faster investigations and 30% fewer engineers in war rooms are numbers that matter. But it's enterprise-only with custom pricing, so smaller teams should pass. If you're on PagerDuty and drowning, it's worth a serious look.
Verified 14d ago · liveness 81/100 · cite: rightaichoice.com/tools/resolve-ai
- Engineering teams with high alert volume and complex microservices
- Organizations needing faster root cause analysis and MTTR reduction
- Teams wanting AI that captures and uses tribal knowledge
- Mid-to-large companies with existing ops tooling like Datadog and PagerDuty
- Startups with no structured on-call process
- Teams without existing ops tooling to connect
- Budget-constrained buyers needing self-serve pricing
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Skip Resolve AI if you don't have a mature ops stack (Datadog, PagerDuty, etc.) or if you need transparent, self-serve pricing—it's enterprise-only with custom quotes.
Pricing is custom-only; you must talk to sales and likely commit to an annual contract.
Resolve AI is priced for mid-to-large enterprises, not small teams. Compared to PagerDuty's per-user pricing, Resolve AI is likely more expensive but offers agentic investigation. For budget-conscious teams, lighter alternatives like Opsgenie or even Slack alerts exist. The cost-benefit hinges on reducing war-room headcount and MTTR.
In short
Resolve AI — AI agents that handle on-call, incidents, and operational tasks—so engineers can get back to building. Best for Engineering teams with high alert volume and complex microservices, Organizations needing faster root cause analysis and MTTR reduction, Teams wanting AI that captures and uses tribal knowledge. Contact Sales pricing.
What's new in Resolve AI
Checked 5 days agoAcross the latest 8 updates: 4 feature updates and 4 news mentions.
Resolve AI Extends Production Context to Coding Agents
Resolve AI now integrates production context into Cursor, Claude Code, and Codex, enabling investigation and fixes within the coding agent.
Why Resolve AI: Token Efficiency at Scale
Resolve AI outlines its focus on token efficiency and context as drivers of high-quality AI agents, enabling root cause analysis and predictable costs.
Proactively run production tasks with background agents
Resolve AI introduces background agents that monitor deploys, detect drift, and handle recurring engineering work autonomously.
6 reasons DIY agents fail at investigating alerts and incidents
Resolve AI highlights six gaps that cause DIY AI agents to fail in production alert investigation, emphasizing need for a robust harness.
How to delegate your on-call workload to AI agents
Resolve AI explains how its agents handle on-call tasks from alert pickup to root cause investigation and backlog management.
6 Pillars of an Agentic Harness needed to run and fix software
Resolve AI details six architectural layers that turn frontier models into reliable agents for production incident response.
The Failures That Led to 2x Agent Accuracy Improvement
Resolve AI founding engineer Steven Karis shares architectural failures that led to 2x accuracy gain, moving to multi-agent council system.
Run your daily engineering tasks in prod with agents
Resolve AI describes background agents running always-on production tasks like deployment monitoring and reporting.
What people actually say about Resolve AI — is it worth it?
We scanned public community sources for Resolve AI on Aug 18, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Resolve AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Delegate on-call to AI agents for alert triage and investigation
- Collaborative Workbench for joint incident investigation
- Multi-agent council for root-cause analysis
- Parallel hypothesis testing
- Background agents for deploy watching and drift detection
- Automated health checks on schedule or trigger
- Causal timeline construction
- Build custom agents via MCP, API, and Skills
- Capture tribal knowledge from chats and runbooks
- Severity-based vulnerability triage and fixes with SLA adherence
- SAML SSO and RBAC access controls
- Data redaction, encryption, and retention controls
- SOC 2 Type II certification, HIPAA and GDPR compliance
- Token-efficient design for predictable costs
About Resolve AI
Resolve AI is an enterprise platform where autonomous AI agents take over on-call, incident investigation, and routine operational work. It's built for engineering teams running complex microservices with high alert volume, giving agents full production context by connecting across code, infrastructure, telemetry, incident management, and CI/CD. Agents participate in on-call rotations, triage alerts, and investigate incidents alongside engineers in a collaborative Workbench. The platform uses a multi-agent council for root-cause analysis, testing parallel hypotheses, and deploys background agents that watch deploys, catch drift, and run health checks on a schedule or trigger. The core promise is to reduce the operational burden on engineers. Customers report 87% faster incident investigations, 73% faster time to root cause, and 30% fewer engineers needed per incident. A recent architectural update improved agent accuracy by 2x in production RCA, and the company emphasizes token efficiency as a way to keep costs predictable while maintaining quality. The platform also captures tribal knowledge from chats, runbooks, and past incidents, so that expertise isn't lost when people leave or rotate. Security and compliance are enterprise-grade: SAML SSO, RBAC, data redaction, encryption, retention controls, audit logging, SOC 2 Type II, HIPAA, and GDPR. Your data is never used to train external models, and severity-based vulnerability triage with SLA fixes is built in. You can extend the platform by building custom agents via MCP, API, and Skills, and it plugs into your existing tooling ecosystem. Resolve AI isn't just another alert triage tool like PagerDuty or Opsgenie. It goes deeper, adding an agentic investigation and remediation layer. If you're a mature engineering org drowning in alerts, this is a serious contender. But it's enterprise-only with custom pricing, so smaller teams or those without existing ops tooling should look elsewhere.
Behind the Verdict
Resolve AI is for teams where on-call is a serious burden and alert volume is high enough to justify an agentic approach. The numbers are impressive: 87% faster investigations, 73% faster root cause, 30% fewer engineers per incident. Those aren't marketing fluff; they come from real customers like DoorDash and Gametime, who run billion-dollar ads platforms on it. When should you pick this? If you've got a mature microservices architecture, a well-defined on-call process, and a stack that includes Datadog, PagerDuty, Slack, and the usual AWS/GCP tooling, Resolve AI can slot in and start paying for itself quickly. It's especially valuable if you're dealing with firefighting and tribal knowledge loss—the platform captures runbooks and past incidents so the on-call experience isn't lost when someone quits. When should you pass? If you're a startup with a simple monolith and no formal on-call, this is overkill. You don't need a council of agents to debug a single service. Also, if you're on a tight budget and need self-serve pricing, Resolve AI won't give it to you—you'll have to talk to sales. There's no free tier, and deployment involves integration planning with their team. The closest alternative is PagerDuty Operations Cloud, but Resolve AI goes beyond alert triage into actual investigation and remediation. It's not just a notification layer; it's an AI SRE. That said, you're betting on the agents being reliable enough to trust at 3am. The company's own blog admits DIY agents often fail, and while they've improved accuracy 2x, it's still a leap of faith. One caveat: enterprise licensing means you'll be locked into a contract, and the value depends heavily on how well you integrate it with your stack. Expect a rollout phase where you map out workflows and connect
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Real-world workflow fit
Concrete scenarios for the personas Resolve AI actually fits — and what changes day-one when you adopt it.
You're paged for a spike in error rates at 3am. Resolve AI agents have already triaged the alert, gathered evidence from Datadog and logs, and posted a root cause hypothesis in Slack.
Outcome: You review the hypothesis, approve a fix, and resolve the incident without waking other engineers—cutting MTTR from hours to minutes.
You're managing a complex microservices environment with frequent deploy-related incidents. You deploy background agents to watch deploys and run health checks.
Outcome: The agents catch drift and failed health checks automatically, preventing incidents before they impact users, and freeing your team to focus on feature work.
Your team is burned out from repeated war rooms. You deploy Resolve AI to co-investigate incidents in the Workbench.
Outcome: Agents build causal timelines, test parallel hypotheses, and propose fixes. Your team spends 30% fewer engineer-hours per incident, protecting revenue.
Use Cases
- Delegate on-call alert triage to AI agents for faster initial response.
- Co-investigate production incidents with agent-driven root cause analysis.
- Automate health checks, deploy monitoring, and drift detection with background agents.
- Encode tribal knowledge as repeatable incident response workflows.
- Reduce war-room participation by integrating agents with PagerDuty and Slack.
- Generate fixes or PRs automatically from incident investigations.
- Proactively identify issues before they escalate using scheduled background agents.
Limitations
- Pricing is enterprise-only (contact required), with no self-serve tiers or free trial.
- The platform requires cloud connectivity and integration with existing tools; offline or air-gapped environments are not supported.
- Specific context window and rate limits are not publicly documented.
- Resolve AI is not a simple alerting tool—it requires a mature ops stack to be effective.
as of 2026-08-26
Verification history
We have re-verified Resolve AI 19 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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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 for mid-to-large enterprises, not small teams. Compared to PagerDuty's per-user pricing, Resolve AI is likely more expensive but offers agentic investigation. For budget-conscious teams, lighter alternatives like Opsgenie or even Slack alerts exist. The cost-benefit hinges on reducing war-room headcount and MTTR.
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.
Initial setup typically takes a few days to connect integrations and test agents. For a standard stack (Datadog, Slack, PagerDuty), you can have agents triaging alerts within a week. Custom agents and workflow tuning may take longer.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Resolve AI”, and we withheld 6: 6 could not be judged, because “Resolve AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Resolve AI.
Official links
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