Resolve.ai

# Resolve.ai: AI for Production Engineering

High-Level Overview

Resolve.ai is an AI-first software company that automates production engineering tasks, starting with on-call incident management and troubleshooting.[2][5] The company builds an AI Production Engineer—an agentic AI system that autonomously investigates production incidents, identifies root causes, and executes remediation actions, dramatically reducing Mean Time to Resolve (MTTR) and freeing engineers from operational bottlenecks.[2]

The company serves engineering teams at enterprises like Coinbase and DataStax who face mounting operational complexity as development velocity increases.[2] Resolve.ai's core problem statement is direct: as AI coding assistants accelerate feature development, the operational burden of managing increasingly complex production systems grows faster than teams can handle. Their solution automates the most stressful and time-consuming operational task—on-call incident response—while positioning themselves to expand across the entire production engineering lifecycle, including reliability optimization, security, testing, and bug fixing.[2][5]

Core Differentiators

Resolve.ai's competitive advantage rests on four foundational pillars:

• Deep System Comprehension: The platform automatically builds and maintains a real-time, dynamic knowledge graph of the entire production environment, continuously updating as deployments, code changes, and system events occur. This enables the AI to traverse dependencies, pods, and deployments with precision.[1][6]

• First-Principles Debugging: Rather than executing static runbooks, Resolve.ai forms hypotheses about incident causes (e.g., "Is this a bad deploy? A database issue? A cloud provider problem?") and systematically gathers evidence to prove or disprove them, mimicking how experienced engineers think.[1]

• Novel UX Paradigm: Recognizing that chat interfaces and static dashboards are inadequate for complex AI workflows, Resolve.ai generates custom, on-the-fly user interfaces for each incident that present only the most relevant data, hypotheses, and evidence. This builds trust by showing its reasoning and reduces cognitive load on human engineers.[1]

• Domain Expertise: The founding team's deep background from Splunk and OpenTelemetry provides credibility and technical depth that translates into a platform that genuinely understands production systems, code, infrastructure, and observability tools.[1]

Operationally, Resolve.ai integrates with tools engineers already use—AWS, Kubernetes, GitHub, and Slack—and can execute remediation actions including generating Git PRs, kubectl commands, and code fixes.[2][4]

Role in the Broader Tech Landscape

Resolve.ai sits at the intersection of two powerful trends: the explosion of agentic AI systems and the operational crisis created by accelerating software development velocity. As AI coding assistants like GitHub Copilot and Claude make developers more productive, the gap between deployment speed and operational maturity widens. Engineering teams are drowning in alerts, incidents, and on-call burden—a problem that traditional observability and incident management tools have failed to solve at scale.[2]

The timing is critical. The shift from reactive monitoring to proactive, AI-driven production engineering mirrors the broader industry transition from rule-based systems to learned, context-aware AI. Resolve.ai's focus on building genuine comprehension of production systems—rather than simple automation wrappers—positions them ahead of competitors who might attempt to bolt AI onto legacy incident management platforms.

Their influence extends beyond their direct customers. By demonstrating that agentic AI can handle complex, multi-step reasoning tasks in production environments, Resolve.ai validates the broader thesis that AI agents will reshape how engineering teams operate. They're also raising the bar for what "production-ready AI" means: systems that understand code, infrastructure, and observability holistically, not just chat interfaces that answer questions.

Quick Take & Future Outlook

Resolve.ai's immediate opportunity is clear: capturing the on-call automation market by delivering measurable MTTR improvements (up to 5x faster) and productivity gains (75% higher productivity claimed).[4] Their stated goal of auto-resolving 80% of alerts and incidents without human intervention is ambitious but grounded in real customer deployments.[2]

The longer-term vision is more expansive and strategically important. The company explicitly aims to become an indispensable AI partner across the entire production engineering lifecycle—from incident management to proactive reliability improvements, security automation, testing optimization, and bug fixing.[1][5] This positions them to become a foundational platform for how engineering teams operate in an AI-first world.

Key trends to watch: (1) whether they can maintain their technical moat as larger observability and DevOps platforms (Datadog, New Relic, Splunk) integrate agentic AI capabilities; (2) how quickly they can expand beyond incident management into adjacent production engineering tasks; and (3) whether their approach to building deep system comprehension scales across diverse customer architectures and tech stacks.

If Resolve.ai executes on their vision, they could reshape how engineering teams think about operational work—transforming it from a necessary burden into a domain where AI genuinely augments human expertise. That's a significant inflection point for the industry.