Resolve AI
AI agents that run production software, so engineers can build.
A strong pick for mature engineering teams drowning in on-call toil. The multi-agent approach and tribal knowledge capture are genuinely useful, but the lack of transparent pricing and requirement for existing ops tooling mean it's not for startups or small shops.
Verified 3h ago · liveness 93/100 · cite: rightaichoice.com/tools/resolve-ai
- Engineering teams reducing on-call burnout
- Organizations seeking faster MTTR
- Teams wanting AI that captures tribal knowledge
- Mid-to-large companies with complex microservices
- Startups with no structured on-call process
- Teams without existing ops tooling
- Budget-constrained buyers needing transparent pricing
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Skip Resolve AI if you lack a structured on-call process or existing ops tool stack (Datadog, PagerDuty, etc.)—the platform integrates deeply and requires that foundation.
Enterprise-only pricing means no transparent per-seat or per-alert costs; you'll need to talk to sales to get a quote.
Resolve AI is enterprise-only contact sales, so it fits mid-to-large companies with budget for premium ops tooling. Cheaper alternatives like PagerDuty AIOps or Opsgenie offer more transparent per-user pricing but lack autonomous investigation depth.
In short
Resolve AI — AI agents that run production software, so engineers can build. Best for Engineering teams reducing on-call burnout, Organizations seeking faster MTTR, Teams wanting AI that captures tribal knowledge. Contact Sales pricing.
What's new in Resolve AI
Checked 8 days agoAcross the latest 7 updates: 6 feature updates and 1 launch.
Proactively run production tasks with background agents
Resolve AI background agents watch deploys, catch drift, and run recurring engineering work that otherwise stays undone.
6 reasons DIY agents fail at investigating alerts and incidents
Identifies six gaps that separate a prototype from a production-ready incident investigation agent.
How to delegate your on-call workload to AI agents
Resolve AI agents handle alerts and backlog work, learning your systems to reduce future on-call load.
6 Pillars of an Agentic Harness needed to run and fix software
Describes six architectural layers that turn frontier-model capability into reliable enterprise outcomes for production incidents.
The Failures That Led to 2x Agent Accuracy Improvement
Founding engineer details three architectural failures that drove 2x improvement in production RCA accuracy via multi-agent council.
Run your daily engineering tasks in prod with agents
Background agents run the production engineering work that engineers never get to, reducing backlog.
Agents that run your software
Launches background agents, 2x root cause quality, and Workbench for collaborative investigation between engineers and agents.
Viability Score
How likely is Resolve AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- AI agent on-call triage and investigation
- Co-work with agents during incident resolution
- Automatic root cause analysis for complex issues
- Background agents for scheduled workflows
- Custom agents via MCP, API, and Skills
- Tribal knowledge capture from chats and runbooks
- Multi-agent council for improved RCA accuracy
- Workbench for collaborative human-agent investigation
- SAML SSO, RBAC, and admin controls
- Data redaction, encryption, and retention
- Activity and support access logging
- SOC 2 Type II certification
- HIPAA and GDPR compliance
- Severity-based vulnerability triage
- Integration with Slack and Microsoft Teams
About Resolve AI
Resolve AI deploys autonomous AI agents to handle on-call triage, incident investigation, and routine operational tasks in production environments. Built for engineering teams at companies like DoorDash and Gametime, it claims 87% faster incident investigations and up to 5x faster mean time to resolution (MTTR). The platform captures tribal knowledge from chats, runbooks, and past incidents, and uses a multi-agent council to improve root cause analysis (RCA) accuracy by 2x. Recent additions include background agents for proactive workflows (e.g., drift detection, deploy monitoring) and a Workbench for collaborative human-agent investigation. It integrates via MCP, API, and webhooks with observability, infrastructure, and CI/CD tools. Security features include SAML SSO, RBAC, encryption, SOC 2 Type II, HIPAA, and GDPR compliance. Pricing is enterprise-only via contact sales, which limits accessibility for smaller teams.
Behind the Verdict
Resolve AI is one of the most focused AI-for-ops tools we've seen. It doesn't try to be a general chatbot or code generator; it lives inside your incident management workflow and does the grunt work that burns out engineers. The multi-agent council that improved RCA accuracy by 2x is a real architectural differentiator — most competitors rely on a single agent that can hallucinate on complex root causes. Background agents are a nice touch for proactive maintenance. That said, the pricing model is a barrier. There's no self-serve tier, no public numbers, and the website funnels you into a sales call. For bootstrapped startups or teams with fewer than 10 engineers, it's hard to justify. You also need a mature ops stack — if you're still using spreadsheets and Slack alerts, Resolve AI will feel overkill. Compared to alternative AI ops tools, Resolve AI is more about autonomous investigation than just alert correlation. If you want a simpler slackbot that just summarizes logs, look elsewhere. But if you're at a mid-to-large company with complex microservices and on-call burnout, this is worth the sales conversation.
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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.
Receiving a PagerDuty alert for high latency in production.
Outcome: Resolve AI agent immediately triages the alert, pulls metrics from Datadog and logs from Splunk, identifies a recent code change in GitHub, and surfaces a root cause hypothesis in Slack before the engineer even acknowledges. Engineer reviews and applies the fix.
Wants to reduce burn rate and improve MTTR across the team.
Outcome: Deploys background agents to run nightly health checks and generate reports. The agents capture tribal knowledge from postmortems and runbooks, making each investigation faster. Over a quarter, MTTR drops by 5x, and on-call hours reduce by 75%.
Integrates Resolve AI with custom internal tools via MCP and API.
Outcome: Builds custom agents that automate deployment monitoring and cross-service dependency mapping. The platform learns from team-specific runbooks and chats, enabling autonomous resolution of recurring issues without manual intervention.
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 and report generation on a schedule
- 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
- Run background agents to proactively identify issues before they escalate
- Use Workbench for human-agent collaborative investigation of complex issues
Limitations
- Pricing is enterprise-only (contact required), no self-serve tiers or free trial available.
- Platform relies on cloud connectivity and existing tool integrations; offline or air-gapped setups are not supported.
- Context window and rate limits are not publicly documented.
- Enterprise deployments may require Forward Deployed Engineering support, adding cost.
- Value diminishes for teams with low alert volume or simple infrastructure.
as of 2026-06-28
Verification history
We have re-verified Resolve AI 14 times since . Each pass re-reads the vendor's own pages and updates only what actually changed.
- — 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
Showing the 6 most recent of 14 verification passes.
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 enterprise-only contact sales, so it fits mid-to-large companies with budget for premium ops tooling. Cheaper alternatives like PagerDuty AIOps or Opsgenie offer more transparent per-user pricing but lack autonomous investigation depth.
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 with existing ops tooling (Datadog, PagerDuty, Slack), you can connect Resolve AI in under an hour and see initial investigation results within the first alert. Full tribal knowledge capture and custom agent tuning may take a few weeks of active 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.
- →From PagerDuty AIOps: Resolve AI integrates directly with PagerDuty, allowing you to layer autonomous investigation on top of existing alerts without replacing your current setup.
- →From Opsgenie: Use Resolve AI's API and webhook integrations to add agent-driven root cause analysis to your existing alert workflow.
- →From manual runbooks: Resolve AI's platform can ingest existing runbooks and chat history to bootstrap its tribal knowledge, automating steps that were previously manual.
- ↗To PagerDuty: Export your incident data and runbooks from Resolve AI; PagerDuty's API can ingest historical alerts.
- ↗To Datadog Incident Management: Resolve AI's integrations log all actions; you can manually reconfigure alert routing in Datadog.
Integrations
Resources & Guides
- Resourcedocs.resolve.ai
Home · Resolve AI
Helpful link from docs.resolve.ai
- Resourcedocs.resolve.ai
About Resolve Ai · Resolve AI
Helpful link from docs.resolve.ai
- Resourcedocs.resolve.ai
Get Started · Resolve AI
Helpful link from docs.resolve.ai
- Resourcedocs.resolve.ai
Connect Integrations · Resolve AI
Helpful link from docs.resolve.ai
- Resourcedocs.resolve.ai
Security · Resolve AI
Helpful link from docs.resolve.ai
Tutorials & Learning
Official links
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