AI production engineer for alerts, on-call, and incidents
Resolve AI is an AI agent platform for production engineering and incident response teams.
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6 AI reviews
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AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.Resolve AI connects to a team's existing production stack — monitoring tools, alerting systems, and infrastructure — and uses agents to handle the operational work that would otherwise fall to on-call engineers. When an alert fires, the agent triages it, investigates the underlying systems, and can perform root cause analysis and troubleshooting before escalating to a human, aiming to resolve or narrow down incidents within minutes rather than hours.
The platform is organized around four product areas: On-Call, which manages alert triage and escalation; Incidents, which covers autonomous detection, response, and resolution; Operational Tasks, for automating repetitive engineering work; and Custom Agents, which let teams build agents tailored to their own workflows. Resolve AI integrates with the tools engineering teams already use rather than requiring a new toolchain, and the company publishes documentation on how its agentic AI is built and reasons through production issues.
Resolve AI is aimed at engineering and platform teams responsible for production reliability — SRE, DevOps, and on-call engineers at software companies. It sits in the incident management and AIOps category alongside tools like PagerDuty, Rootly, and incident.io, differentiating itself through autonomous agent action rather than notification and workflow orchestration alone. Pricing is not publicly listed on a self-serve basis; the company directs prospective customers to book a demo.
The product connects to existing infrastructure and third-party tools via integrations rather than requiring a specific deployment model, and Resolve AI publishes a security page detailing its practices and compliance posture.
Underlying agentic AI architecture that determines how agents reason and act across systems.
Handles and triages alerts automatically to reduce manual on-call burden.
Autonomously detects, investigates, and resolves production incidents.
AI-driven on-call handling that triages alerts and manages escalation automatically.
Performs root cause analysis on incidents to identify underlying causes quickly.
Automates repetitive operational work with AI agents connected to production systems.
Lets teams build custom AI agents tailored to workflows specific to their own tech stack.
Connects directly to production systems, monitoring tools, and infrastructure to take autonomous action.
Integrates with existing engineering tools and infrastructure that teams already use.
Implements security practices and compliance measures for safely operating within production environments.
Provides curated prompts for common production engineering workflows to guide agent usage.
Resolve AI does not publish list pricing; enterprises can request a custom quote for enterprise plans and integrations covering autonomous production operations.
Coinbase and DoorDash already run this in production, so the board question is autonomy, not survival.
“Resolve AI is a $1B-valuation company with agents running inside Coinbase, DoorDash and Salesforce. The outcome numbers behind that are all vendor-published, so the pilot has to produce your own.”
Coinbase says Resolve AI surfaces root causes 72% faster than its own teams. Zscaler reports 75% less investigation time. Those aren't claims a notification-and-workflow tool like Rootly can make, and they're the reason this gets a board conversation at all.
Lightspeed led a $125M Series A at a $1B valuation in February 2026, and the customer wall carries Salesforce, Snowflake and MongoDB. SOC 2 Type II, GDPR and HIPAA are already done. Vendor survival isn't the risk here — Custom Agents holding write access to production is.
The catch is that every one of those numbers is vendor-published, with no independent benchmark and no changelog to check what shipped when. Scope the first ninety days to read-only investigation on one service, then measure your own MTTR. Widen the write grant after that, not before.
Autonomous action plus 40+ integrations separates it from the notification tier occupied by PagerDuty and Rootly.
Granting an agent write access to production is a real governance exposure, and unpublished pricing means terms are negotiated blind.
Coinbase cites 72% faster root cause and Zscaler 75% less investigation time, but there is no trial or self-serve path to test it.
Autonomous resolution rather than alert routing, and named enterprises report investigation time dropping from hours to minutes.
Docs, a REST API and an MCP server are deep, but there is no changelog and the pricing page carries no numbers.
Platform teams who can supervise an agent on live production alerts.
Teams who need an independent benchmark before trusting an autonomous agent.
Autonomous incident response with real integration depth, but pricing opacity and no self-serve trial slow evaluation.
“Resolve AI is betting agents can absorb the on-call toil that PagerDuty and Rootly still route to humans as notifications. The architecture is right, the buying process is the friction point.”
Four product areas — On-Call, Incidents, Operational Tasks, Custom Agents — map cleanly to how platform teams actually organize reliability work. That's not a marketing taxonomy, it's an org chart. SOC 2 Type II plus GDPR/HIPAA compliance signals they've thought about running inside production, not just adjacent to it.
My concern is evaluation cost. No self-serve pricing, no free trial, just a contact-sales form. For a tool that wants write access to production systems, I need a POC against our actual alert volume before I trust an agent to triage, not a demo deck.
Three-year view: if the Agentic Reasoning Engine and RCA quality hold up under real incident load, this displaces a chunk of on-call toil and becomes infrastructure we can't easily unwind. If it's triage theater that still escalates everything, we've added another vendor with production access for no reduction in pages. MCP and API-based custom agents are the right extensibility model regardless.
Positions against PagerDuty and incident.io on autonomous action rather than notification/orchestration, a real differentiation if it holds.
On-Call/Incidents/Operational Tasks/Custom Agents maps directly onto SRE team structure and on-call rotation reality.
MCP, API, and Skills-based custom agents integrate into existing toolchains rather than forcing a new one.
Granting agents production write access is a durable architectural commitment, not a reversible trial.
RCA and autonomous investigation go beyond alert routing; the published customer stories give outcome numbers but no independent benchmark.
Platform and SRE teams with enough alert volume to justify a sales-cycle evaluation of autonomous incident response.
Avoid if you need self-serve pricing or a fast trial before committing engineering time to a POC.
No price on the page. Zero benchmarks for a bill that touches production infrastructure.
“Contact-sales only, zero published tiers. For a tool with write access to prod, that's a bigger number to underwrite than the invoice.”
A pricing page with no prices on it. One tier: Contact Sales. Free to ask, unknown to buy.
That's the whole story here. PagerDuty and incident.io publish per-seat tiers you can model in five minutes. Resolve AI makes you book a demo just to get a number. For a team of 50 SREs, that's weeks of procurement before finance sees a line item.
TCO math is a black box. SOC 2 Type II, GDPR, HIPAA — compliance box is checked, which helps security review move faster. But autonomous agents touching production systems usually carry usage-based components: per-incident, per-agent, per-integration. None of that is visible. Year 3 cost could be flat enterprise fee or could scale with alert volume. No way to model it from public materials. Budget owners should assume premium pricing — this category doesn't sell cheap.
SOC 2 Type II, GDPR, and HIPAA are already checked, which speeds security review even if pricing stalls procurement.
Nothing published on terms, renewal, or cancellation — everything starts in a demo.
The pricing page carries no numbers — one path, Contact Sales, zero published tiers.
Incident-response automation is measurable in principle, but zero public benchmarks for a tool that touches production.
Year-3 cost could be flat enterprise fee or scale with alert volume; no way to model it from public materials.
Well-funded SRE orgs ready for a premium enterprise procurement cycle with security review already half-done.
You need to model year-3 cost before booking a demo.
An agent that pages you at 3am and might actually fix it before you're awake — if you trust it to.
“Resolve AI wants to sit in the on-call rotation and do the triage work itself, not just route the page. The autonomy is the pitch and the risk at the same time.”
Pager fatigue is the real disease here, and Resolve AI is going after root cause, not just notification — that's the actual delta from PagerDuty or incident.io. Alert Triage plus Root Cause Analysis running unattended in prod is a bigger ask than a Slack bot with runbooks. Week one, I'm not letting it touch anything write-path. I'm watching its investigation traces against what my team already knows, comparing verdicts by hand.
No published pricing, no free trial, demo-gated — normal for enterprise infra sales, but it means no sandbox to break things in before a contract's signed. SOC 2 Type II, GDPR, HIPAA compliance is table stakes covered, good. MCP/API/Skills for custom agents is the right shape technically.
The real test is postmortems: does it explain its reasoning well enough that I'd defend its RCA to a VP at 2am. Docs exist, but I want incident replay logs, not prompt libraries.
Autonomous action on prod systems means trust-building overhead before you stop double-checking every triage.
Published docs on agent reasoning exist, but a prompt library reads more like enablement collateral than incident-replay depth.
No self-serve pricing or trial means onboarding friction starts before you even see the product work.
Custom Agents via MCP, API, and Skills gives real extensibility for teams with specific stacks, not just canned playbooks.
Connects to existing monitoring/alerting stack rather than forcing a new toolchain, per the integration model described.
Platform teams with mature monitoring already in place who want agents doing triage work before paging a human.
Avoid if you need transparent self-serve pricing or a sandbox before committing prod access.
An AI on-call engineer that sounds great in the pitch, but you can't try it before you buy it.
“Resolve AI wants to replace the 3am pager grab with an agent that triages and root-causes for you. No pricing, no free trial, no self-serve — you're booking a demo like it's 2019 enterprise software.”
Four product areas: On-Call, Incidents, Operational Tasks, Custom Agents. That's a real structure, not just a chatbot bolted to PagerDuty. Root cause analysis and autonomous triage are the pitch, and if it actually works, on-call gets a lot less miserable at 3am.
But there's zero self-serve here. No pricing page with numbers, no free trial, just 'fill out a form with your work email.' Compare that to incident.io or Rootly, where you can at least see tiers before committing. For a tool asking to touch production infrastructure, that's a big ask on faith alone.
Month three is the real test — can it learn your stack's quirks or does it stay generic? SOC 2 Type II, GDPR, HIPAA compliance are all listed, which matters when an agent has hands on your prod systems. Custom Agents via MCP and API is the most interesting bit, if it's discoverable without a consultant walking you through it.
No changelog and no product tour — screenshots turn up only inside blog posts, so daily craft is hard to judge.
Custom Agents via MCP, API, and Skills gives depth but requires engineering effort to configure per stack.
Platform is listed as web-only, no evidence of mobile support for on-call engineers away from a desk.
No free trial, no self-serve pricing — onboarding starts with a sales form, not a product.
SOC 2 Type II, GDPR, HIPAA compliance and data isolation controls suggest real operational maturity.
SRE and platform teams at larger companies ready to commit to a demo-led enterprise sales cycle.
You want to self-serve, see pricing upfront, or trial the product before looping in security review.
Autonomous incident response, no published prices, no changelog. Book a demo.
“Resolve AI wants agents inside your production stack making judgment calls at 3am. The docs and SOC 2 claim are real signals — the missing pricing, missing changelog, and demo-gated everything are not.”
Three tells before the pitch even lands. One: 'AI for prod' as the entire H1 — the kind of superlative that ages poorly if an agent misfires root cause analysis during a real outage. Two: no changelog, so shipping cadence is invisible. Three: pricing is 'fill out a form with your work email' — fine for enterprise motion, but it means no self-serve signal on where this actually sits against PagerDuty or Rootly.
The good part: SOC 2 Type II, GDPR, HIPAA claims are specific and checkable, not vague trust-badge decoration. That's real for a company touching production infrastructure.
Exit portability is the open question. Custom Agents built via MCP and API sound portable in theory. In practice, once an agent has tribal knowledge encoded and is running your on-call rotation, unwinding that in 18 months is not a config export. incident.io and PagerDuty at least leave you with structured data, not learned behavior.
Autonomous RCA and triage vs. PagerDuty/incident.io's notification-and-orchestration model is a real, named gap, not a copycat feature set.
Custom Agents via MCP/API sound portable, but agent-encoded tribal knowledge isn't a clean export like a rules-based escalation policy.
SOC 2 Type II plus GDPR/HIPAA compliance is concrete, and a $125M Series A at a $1B valuation removes near-term funding risk; headcount is still undisclosed.
"AI for prod" and "minutes not hours" are aspirational framing with no public benchmark to check against.
Autonomous-agent-in-production is a newer pattern than PagerDuty's alerting playbook, though Coinbase, Salesforce and Zscaler are already running it in production.
SRE and platform teams at mid-to-large companies ready to hand real triage authority to an agent.
You want self-serve pricing or a lightweight pilot before committing engineering trust to an autonomous agent.
Common questions answered by our AI research team
Fill out the pricing form with your full name and work email, and someone will follow up with details on enterprise plans and integrations for your environment.
Yes. Agents participate in every on-call rotation, triaging and investigating alerts so engineers can step in to direct and take action rather than handle every alert manually.
Yes. Teams can build custom agents by plugging Resolve AI into their existing ecosystem using MCP, API, and Skills, or by bringing their own tribal knowledge and skills into the platform.
Resolve AI is designed to meet stringent compliance standards, starting with SOC 2 Type II certification, and is compliant with GDPR and HIPAA for handling PII and PHI data.
Customer data stays scoped to your org, with data protections including redaction, encryption, and retention controls, plus auditable activity and support access logging.
Who Resolve AI competes with, and how each one differs
Chat-native incident coordination platform (Slack/Teams) that layered agentic root-cause investigation onto its existing on-call and postmortem tooling, versus Resolve AI's standalone, agent-first investigation platform.
Bundles its autonomous "Bits AI SRE" investigation agent inside its own observability suite, working best on Datadog-native telemetry, versus Resolve AI's platform built to reason across whichever logging/metrics/tracing stack a customer already runs.
Public incumbent (NYSE: PD) alerting and on-call platform that layered an SRE Agent and Insights Agent onto its 32,000-customer base in October 2025, versus Resolve AI's ground-up agentic architecture and enterprise-only go-to-market.
Uses causal ML (a "Production World Model" and Causal Search Engine) rather than Resolve's multi-agent parallel-hypothesis approach, and targets large enterprises with on-prem deployment support; raised a $48M Series A led by Kleiner Perkins with Sequoia participating.
Smaller, safety-first rival (about $9.8M raised vs. Resolve's $190M) that keeps its agent strictly read-only, only recommending fixes rather than executing remediations, versus Resolve's agents that can generate and open remediation pull requests directly.
Resolve AI is a San Francisco-based startup building an AI system that monitors alerts, diagnoses root causes, and resolves production incidents for engineering teams.
