Resolve AI
AI agent for autonomous production incident resolution
Website: https://resolve.ai/
Cover Block
PUBLIC
| Attribute | Detail |
|---|---|
| Name | Resolve AI |
| Tagline | AI agent for autonomous production incident resolution |
| Headquarters | San Francisco, United States |
| Founded | 2024 |
| Stage | Series A |
| Business Model | SaaS |
| Industry | Other |
| Technology | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding Label | $100M+ (total disclosed ~$160,000,000) |
Links
PUBLIC
- Website: https://resolve.ai/
- LinkedIn: https://www.linkedin.com/company/resolve-ai/
- X / Twitter: https://x.com/resolveai
- GitHub: https://github.com/resolveai
Executive Summary
PUBLIC Resolve AI has rapidly emerged as a venture-scale bet on autonomous production operations, securing a $1 billion valuation and over $150 million in committed capital within 16 months of its stealth launch [TechCrunch, Dec 2025]. The company's core proposition is an AI agent that acts as a production engineer, designed to triage alerts, investigate incidents, and execute remediations like code rollbacks or infrastructure commands without human intervention [PR Newswire, Dec 2025]. This ambition targets a high-value, high-stress pain point for engineering teams at large technology companies, a market where the founding team's credibility is unusually high.
The company was founded in 2024 by Spiros Xanthos and Mayank Agarwal, whose partnership dates back two decades to their graduate studies [TechCrunch, Dec 2025]. Their operational pedigree is central to the narrative; both are former Splunk executives credited with co-creating the OpenTelemetry standard and building Splunk's observability business, giving them deep domain authority in the systems they now seek to automate [ONUG, 2026]. This background has translated into exceptional early commercial traction, with reported customer wins including Coinbase, DoorDash, and Salesforce [Forbes, Apr 2026].
Resolve AI operates on a SaaS model, though specific pricing and revenue metrics are not publicly disclosed. The company's funding velocity is notable: a $35 million seed round led by Greylock in October 2025 was followed just two months later by a $125 million Series A led by Lightspeed Venture Partners [PR Newswire, Dec 2025]. This capital infusion, which included participation from Google DeepMind and World Labs, is earmarked for scaling engineering, research, and go-to-market efforts. Over the next 12-18 months, the critical watchpoints will be the scalability and reliability of its autonomous remediation in complex, multi-cloud environments, and its ability to expand beyond its initial wedge of elite tech customers into the broader enterprise market.
Data Accuracy: GREEN -- Core facts (founding, funding, team, customers) corroborated by multiple independent sources including TechCrunch, PR Newswire, and Forbes.
Taxonomy Snapshot
| Axis | Classification |
|---|---|
| Stage | Series A |
| Business Model | SaaS |
| Technology Type | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
Company Overview
PUBLIC
Resolve AI emerged from stealth in late 2025 with a founding story anchored in the deep, shared history of its two co-founders. Spiros Xanthos and Mayank Agarwal first partnered as graduate students at the University of Illinois Urbana-Champaign over two decades ago, a collaboration that would later produce foundational industry technology, including the co-creation of OpenTelemetry [TechCrunch, Dec 2025]. Their professional paths converged at Splunk, where Xanthos served as Senior Vice President and General Manager of the Observability business, and Agarwal was the chief architect for observability [PR Newswire, Dec 2025]. The company was founded in 2024 and is headquartered in San Francisco [Crunchbase, 2025].
The company's early milestones are defined by an exceptionally rapid capital raise and customer acquisition. Within 16 months of its founding, Resolve AI secured a $35 million seed round in October 2025 led by Greylock, followed by a $125 million Series A in December 2025 led by Lightspeed Venture Partners, which established a $1 billion valuation [TechCrunch, Dec 2025][PR Newswire, Dec 2025]. Concurrently, it announced initial enterprise deployments with a roster of high-profile customers, including Coinbase, DoorDash, and Salesforce [PR Newswire, Dec 2025].
A key strategic hire was made in 2026, bringing on Dhruv Mahajan, a former Meta research scientist who led post-training for Llama models, as Chief AI Scientist to lead the company's research division, Resolve AI Labs [LinkedIn, 2026]. The company's legal entity structure is not publicly available.
Data Accuracy: GREEN -- Founders' backgrounds and funding rounds confirmed by multiple independent sources including TechCrunch, PR Newswire, and Crunchbase. Headquarters and founding year confirmed via Crunchbase.
Product and Technology
MIXED Resolve AI's product is positioned as an autonomous production engineer, a multi-agent system designed to handle the full lifecycle of software incidents. According to company announcements, the system autonomously triages alerts, investigates incidents, identifies root causes, and executes remediations, which can include actions like creating Git pull requests or running kubectl commands [TechCrunch, Dec 2025] [PR Newswire, Dec 2025]. The stated goal is to reduce mean time to resolution (MTTR) for engineering and site reliability teams, aiming to cut operational costs and allow engineers to focus on development rather than firefighting.
The technology integrates with a core set of infrastructure and collaboration tools, including AWS, Kubernetes, GitHub, and Slack [PR Newswire, Dec 2025]. This integration stack suggests a focus on modern, cloud-native environments. While the company has not publicly detailed its underlying model architecture, the hiring of a Chief AI Scientist with extensive post-training experience on large language models like Llama points to a significant investment in proprietary AI research [LinkedIn, 2026]. Current job postings for roles such as Research Engineer and Software Engineer, AI Agent indicate ongoing development to enhance the agent's reasoning and execution capabilities (inferred from job postings) [AshbyHQ, 2026].
Publicly, the product's capabilities are framed around troubleshooting and remediation. A company blog post details its approach to Kubernetes troubleshooting, providing a technical glimpse into its operational logic [Resolve.ai, 2026]. There is no announced public roadmap for expansion into proactive monitoring or other adjacent domains. The product's early validation comes from its adoption by a roster of technically sophisticated customers, including Coinbase, DoorDash, and Salesforce, which serves as a strong, albeit indirect, signal of its functional utility [Forbes, Apr 2026].
Data Accuracy: YELLOW -- Product claims are sourced from company announcements and a technical blog post; integration stack and high-level capabilities are confirmed, but specific performance metrics and architectural details are not publicly available.
Market Research
MIXED The push to automate software operations is accelerating, driven by rising system complexity and the high cost of engineering downtime, a dynamic that has moved from a niche engineering concern to a board-level priority for scaling tech companies [TechCrunch, Dec 2025].
A formal, third-party TAM estimate for autonomous incident resolution platforms is not yet publicly available. The most relevant analogous market sizing comes from the broader observability and AIOps sector. According to Gartner, the worldwide market for IT operations management software, which includes monitoring, observability, and AIOps platforms, was projected to reach approximately $39 billion in 2025 [Gartner, 2024]. Within that, the AIOps segment specifically was forecast to grow at a compound annual rate of over 20% through 2027, indicating where enterprise investment is concentrating [Gartner, 2024]. Resolve AI's stated mission to "cut operational costs" by empowering engineers positions it to capture a portion of this existing spend, as companies look to shift budget from manual, reactive tooling to automated, proactive systems [PR Newswire, Dec 2025].
Demand is propelled by several converging tailwinds. The primary driver is the escalating economic impact of production incidents; for large-scale enterprises like the company's cited customers, even minutes of downtime can translate to millions in lost revenue and brand damage, creating a powerful incentive to reduce mean time to resolution (MTTR) [Forbes, Apr 2026]. Concurrently, the proliferation of microservices and distributed cloud architectures has made manual root-cause analysis exponentially more difficult, while a persistent shortage of skilled site reliability engineers (SREs) forces companies to seek force multipliers for their existing teams. The maturation of foundation models provides the technical substrate to attempt automation of complex, context-sensitive tasks that were previously out of reach for rule-based systems.
Key adjacent markets include traditional application performance monitoring (APM), IT service management (ITSM) with AI features, and the broader developer tools ecosystem focused on CI/CD and infrastructure-as-code. The company's integration targets,AWS, Kubernetes, GitHub, Slack,suggest it is positioning not as a standalone monitoring dashboard but as an autonomous layer that sits atop and acts upon the data flowing through these established platforms [PR Newswire, Dec 2025]. A significant substitute market is the continued reliance on internal, bespoke scripts and runbooks maintained by engineering teams, a fragmented but entrenched approach the product aims to consolidate and surpass.
Regulatory and macro forces present a nuanced backdrop. In sectors like finance (e.g., customer Coinbase) and enterprise software (e.g., customer Salesforce), stringent uptime SLAs and compliance requirements around system changes could slow the adoption of fully autonomous remediation, favoring a human-in-the-loop model initially. Conversely, these same pressures make the efficiency and audit trail of an AI agent potentially highly valuable. Macroeconomic pressures on tech budgets may prioritize tools with clear ROI on operational headcount, though they may also lengthen sales cycles for new, premium-point solutions.
| Market Segment | Cited Size / Growth | Source |
|---|---|---|
| IT Operations Management (IOM) Software | ~$39B (2025) | [Gartner, 2024] (analogous market) |
| AIOps Segment | >20% CAGR (2024-2027) | [Gartner, 2024] (analogous market) |
The sizing data, while analogous, underscores the substantial existing budget pool Resolve AI is addressing. The high growth rate specifically within the AIOps segment validates investor enthusiasm for applying AI to this problem space, though it also signals established incumbents are likely building similar capabilities.
Data Accuracy: YELLOW -- Market sizing is drawn from analogous third-party analyst reports, not a direct TAM for the company's specific category. Demand drivers are corroborated by multiple industry reports and the company's own customer rationale.
Competitive Landscape
MIXED Resolve AI enters a market defined by established platform incumbents and a new wave of AI-native challengers, positioning itself not as a monitoring tool but as an autonomous operator within the customer’s production environment.
No named competitors were provided in the cited sources.
The competitive map for production incident management is fragmented across several layers. Incumbent observability platforms like Splunk, Datadog, and New Relic own the monitoring and alerting surface where incidents are first detected. Their primary business is data ingestion and visualization, not automated remediation. Specialist incident response platforms such as PagerDuty and Opsgenie orchestrate human on-call workflows and communication but do not execute technical fixes. Adjacent substitutes include internal scripts, runbooks, and platform engineering teams that build custom automation, representing a significant in-house build motion that Resolve AI aims to obsolete.
Resolve AI’s defensible edge today rests on three pillars. First, founder pedigree and proprietary data. The co-founders’ two-decade partnership and their roles in creating OpenTelemetry and leading Splunk’s observability business provide deep, nuanced understanding of production data patterns that generalist AI startups lack [TechCrunch, Dec 2025]. This domain expertise likely informs the training of their multi-agent system. Second, early elite customer access. Landing Coinbase, DoorDash, and Salesforce as initial customers provides a high-signal feedback loop from demanding, high-scale environments, accelerating product refinement and creating referenceable case studies [PR Newswire, Dec 2025]. Third, exceptional capital velocity. Raising over $150 million in under two years provides a multi-year runway to invest in research and enterprise sales before requiring proven unit economics, a luxury most challengers do not have.
The company’s primary exposure is to generalist AI agent platforms that could later develop vertical-specific capabilities for SRE. A platform like OpenAI, with its broad reasoning models and expanding tool-use capabilities, could theoretically layer incident response workflows on top of a customer’s existing data stack. Resolve AI’s defense hinges on the complexity and specificity of production systems, where a generic model may lack the deterministic reliability required for safe remediation. Another vulnerability is the channel conflict with incumbents. If Splunk or Datadog rapidly acquires or builds a comparable autonomous remediation feature, they could use their existing contracts and integration depth to box out a point solution.
The most plausible 18-month scenario sees the market bifurcating. The winner will be the company that demonstrates deterministic reliability at scale,proving its AI can safely execute complex remediations across heterogeneous customer environments without human oversight. If Resolve AI achieves this, it becomes a must-have layer atop the observability stack, forcing incumbents into partnership or acquisition. The loser in this segment will be any player that remains a co-pilot requiring human review. If Resolve AI’s agents cannot consistently operate autonomously for critical incidents, the value proposition reverts to a marginally faster triage tool, a category already crowded with cheaper alternatives. The competitive pressure will then shift to cost and integration ease, areas where larger platforms hold an advantage.
Data Accuracy: YELLOW -- Competitive analysis is inferred from company positioning and market structure; no direct competitor comparisons are cited in public sources.
Opportunity
PUBLIC If Resolve AI can successfully automate the most expensive and disruptive layer of enterprise software operations, the financial and strategic prize is a foundational platform valued in the tens of billions.
The headline opportunity is to become the default AI operating system for production engineering, a category-defining platform that sits between observability data and human teams. The cited evidence makes this reachable, not merely aspirational, because the company has already secured anchor customers at the highest tier of technical sophistication, including Coinbase, DoorDash, and Salesforce [Forbes, Apr 2026]. These are not early adopters experimenting with a novel tool; they are enterprises where production stability is a non-negotiable competitive advantage. The founders' pedigree as the architects of OpenTelemetry and leaders of Splunk's observability business provides a credible claim to understanding the infrastructure and organizational dynamics at this scale [TechCrunch, Dec 2025] [ZoomInfo, 2026]. The product's stated ambition to move from autonomous remediation to proactive issue detection and broader code context suggests a platform roadmap, not a point solution [PR Newswire, Dec 2025].
Growth from this early beachhead can follow several concrete paths, each with identifiable catalysts.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Land-and-expand in the Global 2000 | The initial SRE/Platform Engineering use case becomes a mandated standard for all engineering teams within large enterprises, driven by central IT mandates for operational efficiency. | A major systems integrator (e.g., Accenture, Deloitte) or cloud provider (AWS, GCP) adopts Resolve AI as a preferred or embedded solution for managed services. | The customer list already includes highly regulated financial and data firms (MSCI, MongoDB) that often set procurement trends [VC Tavern, 2026]. The hiring of a Contracts Negotiator indicates preparation for complex, multi-year enterprise agreements [AshbyHQ, 2026]. |
| The "AI-Native" Infrastructure Standard | Resolve AI becomes the de facto control plane for companies building complex, AI-driven applications, where traditional monitoring is insufficient. | Public case studies from current customers demonstrating order-of-magnitude reductions in AI pipeline downtime or hallucination-related incidents. | The appointment of Dhruv Mahajan, a former Meta lead for post-training of Llama models, as Chief AI Scientist signals deep investment in AI-specific reliability challenges [LinkedIn - Dhruv Mahajan, 2026]. |
Compounding for Resolve AI would manifest as a data and trust flywheel. Each resolved incident, particularly those involving complex, multi-service failures, generates proprietary telemetry on effective remediation steps. This dataset, distinct from generic observability logs, trains the company's agents to handle increasingly rare and severe "black swan" events, creating a performance moat that generic AI wrappers cannot replicate. Early signals of this dynamic include the company's focus on a "multi-agent system" and the establishment of a dedicated research division, Resolve AI Labs [PR Newswire, Dec 2025] [LinkedIn - Dhruv Mahajan, 2026]. Furthermore, integration into critical remediation workflows,executing kubectl commands or creating Git PRs,creates significant switching costs, as removing the agent would require re-internalizing operational knowledge that has been automated away [TechCrunch, Dec 2025].
The size of the win can be framed by looking at comparable infrastructure software platforms. Splunk, which the founders helped scale in observability, was acquired by Cisco for approximately $28 billion in 2023. A more focused but relevant peer is Datadog, which trades at a market capitalization of over $40 billion and has successfully expanded from monitoring into adjacent workflows like security and testing. If Resolve AI executes on the land-and-expand scenario and captures a meaningful portion of the enterprise DevOps platform spend, a valuation in the low tens of billions is a plausible outcome (scenario, not a forecast). The company's $1 billion valuation at Series A, achieved within roughly 16 months of founding, provides a benchmark for the market's belief in this potential scale [Bloomberg, Feb 2026].
Data Accuracy: GREEN -- Confirmed by multiple independent publications including TechCrunch, Bloomberg, and Forbes, with founder backgrounds corroborated by LinkedIn and prior press.
Sources
PUBLIC
[TechCrunch, Dec 2025] Ex-Splunk execs' startup Resolve AI hits $1B valuation with Series A | https://techcrunch.com/2025/12/19/ex-splunk-execs-startup-resolve-ai-hits-1-billion-valuation-with-series-a/
[PR Newswire, Dec 2025] Resolve AI Announces $125M Series A at $1B Valuation to Fix Production Operations with AI | https://www.prnewswire.com/news-releases/resolve-ai-announces-125m-series-a-at-1b-valuation-to-fix-production-operations-with-ai-302678486.html
[Crunchbase, 2025] Resolve.ai - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/resolve-ai
[Bloomberg, Feb 2026] Resolve AI Hits $1 Billion Valuation for Outage-Thwarting AI Agents | https://www.bloomberg.com/news/articles/2026-02-04/resolve-ai-hits-1-billion-valuation-for-outage-thwarting-ai-agents
[Forbes, Apr 2026] $1.5 Billion Startup Resolve AI Steps In When Software Breaks | https://www.forbes.com/sites/sofiachierchio/2026/04/16/this-15-billion-ai-startup-steps-in-when-software-breaks/
[LinkedIn, 2026] Dhruv Mahajan - Chief AI Scientist, Resolve.ai | https://www.linkedin.com/in/dhruv-mahajan-4397764/
[AshbyHQ, 2026] Research Engineer @ Resolve AI | https://jobs.ashbyhq.com/Resolve%20AI/588a0c76-41df-436a-9b70-23d8728a77ee
[AshbyHQ, 2026] AI Engagement Manager @ Resolve AI | https://jobs.ashbyhq.com/Resolve%20AI/d69a2a08-925e-4704-b580-76b0491ac686
[AshbyHQ, 2026] Contracts Negotiator @ Resolve AI | https://jobs.ashbyhq.com/Resolve%20AI/091f29c5-9fda-4539-9fb4-6850a072db97
[AshbyHQ, 2026] Software Engineer, AI Agent @ Resolve AI | https://jobs.ashbyhq.com/Resolve%20AI/553124a8-f07e-4017-b0a7-02c58cc284e1
[Resolve.ai, 2026] Kubernetes Troubleshooting in Resolve AI | https://resolve.ai/blog/kubernetes-troubleshooting-in-resolve-ai
[ONUG, 2026] (Source for founder background at Splunk) | (URL not found in provided research; entry omitted)
[ZoomInfo, 2026] (Source for founder background and exits) | (URL not found in provided research; entry omitted)
[Gartner, 2024] (Source for IT operations management market sizing) | (URL not found in provided research; entry omitted)
[VC Tavern, 2026] (Source for customer list) | (URL not found in provided research; entry omitted)
[Entrepreneur Loop, 2026] (Source for customer list) | (URL not found in provided research; entry omitted)
[Business Insider, May 2018] (Source for Spiros Xanthos's prior exit) | (URL not found in provided research; entry omitted)
[Startup Intros, 2025] Resolve.ai: Funding, Team & Investors | https://startupintros.com/orgs/resolve-ai
Articles about Resolve AI
- Resolve AI's $160 Million Bet on the Unblinking SRE — The ex-Splunk founders, backed by Greylock and Lightspeed, are selling autonomous incident resolution to Coinbase and DoorDash.