We Looked at 5 AI SRE Agent Products — Here’s What We Found

The AI Agent Market will expand to USD 42.7 billion by 2030 according to the latest research by MarkNtel Advisors. AI Agents are widely predicted to be the next big wave, and we’re already seeing them applied in Observability and DevOps—especially for incident management and root cause analysis (RCA).

Since we’re also building a product in this space, this article is both our research notes and an open conversation with the community.

What Are AI SRE Agents?

So first: what exactly is an “AI SRE Agent”?

No matter how it’s branded, the core problem it tries to solve is the same: during incidents, automatically pinpoint the root cause and speed up recovery. Some tools also conduct proactive anomaly detection and prediction (like Traversal), while others help with postmortem writing after the fact (like incident.io). The shared goal is clear: reduce the pain of on-call, and cut mean time to recovery (MTTR).

Start-ups Are Shaping the Next Wave

Broadly, there are two kinds of players in this space:

• Observability vendors who add an AI layer on top of their own data (e.g. Datadog’s Bits AI, Splunk AI Assistant). These let users query and analyze data through natural language.
• Independent startups who build an “intelligence layer” on top of existing observability stacks. They aim for a no vendor lock-in model and can connect to multiple data sources. Examples include Resolve AI and Traversal.

The limitation of vendor-led AI is that they can only analyze the data inside their own platform. But according to Grafana’s 2025 Observability Survey, most organizations use multiple observability tools—and many rely on open source stacks like Prometheus, Jaeger, and Grafana Loki. These teams know the pain of multi-tool incident analysis firsthand. That’s why we’re particularly interested in how the independent startups are approaching the problem.

So how are these products tackling it today? How well do they work? And how are they different from one another? We reviewed dozens of players in this space and picked five representative products to analyze, including Traversal, Resolve AI, incident.io, Hawkeye by Neubird and Syncause.

Traversal - Large-Scale Parallel Detection

Traversal positions itself as a platform for high-impact incidents rather than routine alert triage. It claims to continuously scan systems, catch early signs of failure even without alerts, trace them back to root causes, and provide a resolution plan. Its architecture relies on swarms of parallel AI agents to run statistical tests over large, heterogeneous datasets while keeping guardrails for system health.

Key points:

• Goes beyond reactive RCA with anomaly detection before incidents occur
• Claims competitors can only handle simple, frequent alerts, while Traversal can handle cross-platform, high-impact incidents
• Uses distributed agents for parallel analysis, quickly surfacing multiple candidate root causes
• Requires only read access, no agent installation

Resolve AI - Knowledge Graph Approach

Resolve AI takes a very different tack: it models the entire technical environment as a knowledge graph. This graph—covering services, dependencies, configs, and changes—is continuously built and updated. The system then uses a custom DSL (called Gragg in their public materials) to query the graph efficiently and securely, allowing the agent to “walk the graph” and reason about incidents.

Key points:

• Environment awareness powered by a live, evolving knowledge graph
• Custom query language for efficient graph traversal
• Improves reasoning accuracy over time through feedback and interaction

incident.io - Full-Stack Incident Management

incident.io looks less like a narrow RCA tool and more like an AI-augmented incident management platform. It covers the full lifecycle: creating incidents, enriching context in Slack, generating postmortems, even exploring auto-remediation. Its advantage is organizational integration—tying together alerts, communication, change history, response templates, and reviews into a single workflow.

Key points:

• Integrates with code changes, past incidents, logs, and metrics (though traces support seems unclear)
• Allows Slack-based queries for code, dashboards, and logs
• Covers the whole incident journey: from creation to RCA to postmortem, and in some cases, code remediation via auto-generated pull requests

Hawkeye by Neubird - Hybrid Data Integration

Hawkeye leans on your existing observability data (metrics, logs, alerts) to automatically surface likely problems and root causes.

Key points:

• The hybrid data integration: Uses data virtualization to unify diverse sources into one dataset and adds MCP Integration for real-time data access and validation
• Built for enterprise complexity: hybrid and multi-cloud

Syncause - Deep RCA with Kernel-Level Visibility

Syncause (our own product) currently focuses on the earliest stage of incident response: finding the problem source and direction during an active outage, to help teams recovery service quickly. Beyond ingesting existing observability data, it leverages eBPF to capture a “holographic view” of program execution—tracking CPU, IO, and network usage to reveal performance bottlenecks.

Key points:

• eBPF-based kernel-level data capture for fine-grained system insight. Identifies whether time is spent in CPU, file IO, or network IO.
• Cover complex issues, especially effective for spotting performance bottlenecks
• Slack integration for natural language queries to the bot
• No need to book a demo—sandbox is publicly available; private deployment supported via waitlist

What We Learned

These five products share the same north star: help teams recover faster and reduce MTTR. However, their approaches vary significantly: some focus on parallelized reasoning, others use knowledge graphs for environment modeling, some emphasize incident workflow management, and others dive deep into kernel and system-level data collection.

In the community, skepticism remains high. Many engineers don’t trust AI to make changes directly in production—and honestly, neither do we. What SREs really need isn’t a “god mode AI” but practical tools that plug into their existing workflows and meaningfully cut down diagnostic time.

This space is still very young. The shape of “AI for SRE” isn’t settled yet, and in the coming years we’ll likely see more diverse directions emerge.

So what do you think? Which approach best fits your team’s needs? Or what’s the single most painful point you’d want an AI SRE Agent to solve? We’d love to hear your thoughts in the comments.