Two Diverging Paths in AI RCA Agents and Where Syncause Fits

In the world of AI-driven software engineering, product differentiation often emerges from the problems being solved rather than the technologies being used. This divergence is evident in both the AI coding and SRE domains.

From AI Coding Tools to AI SRE Agents

In the AI coding ecosystem, products generally fall into two broad categories:

1. Tools that enhance developer productivity — such as Cursor or Claude Code — helping engineers write, refactor, or understand code more efficiently.
2. Tools that enable non-engineers to create software — like Lovable or Bolt.new — where AI abstracts away the complexity of programming altogether.

This same pattern is now visible in the SRE and incident management space. Based on our market analysis, AI products in this domain can be grouped into two distinct categories according to their underlying problem-solving approach.

Two Approaches to AI in SRE

Observability-driven RCA Agents

These products come from teams with observability or data analytics backgrounds. Their goal is to detect and explain system failures by analyzing metrics, logs, and traces. Typical examples include Datadog Bits, Resolve AI, and Traversal.

Conceptually, these systems evolved from the “AIOps” generation — where machine learning models tried to correlate anomalies across large datasets. In the LLM era, the user experience has improved dramatically: engineers can now query and reason about observability data in natural language, with LLMs assisting in interpreting anomalies and generating hypotheses.

Incident Management–driven AI Systems

These originate from incident management platforms like PagerDuty, Incident.io, and Rootly. Their teams focus on workflow orchestration, postmortems, and on-call automation, and are now extending into RCA as a natural next step — since identifying root causes is a key part of incident response.

Their approach focuses on learning from historical incidents. When a new issue occurs, the system looks for similar past events, recalls how they were resolved, and proposes possible remediation paths.

This mimics how humans reason under uncertainty — we recall prior cases, compare symptoms, and apply familiar solutions.

Typically, these systems also integrate with observability tools to gather additional context.

• Strength: Highly effective for recurring incidents, where pattern recognition from past data is valuable.
• Limitation: Requires extensive historical data and pre-integrated observability sources. Without that foundation, their AI components have little to learn from.

If we imagine a two-dimensional space with “Observability” and “Incident Management” as the horizontal and vertical axes, current AI SRE tools distribute themselves across these two dimensions. Most products lean toward one of the poles, with only a few starting to bridge the two.

Where Syncause Fits

Syncause belongs to the observability-driven RCA agent category. Its mission is to conduct incident analysis by uncovering causal relationships behind failures using data from multiple observability sources.

Unlike traditional RCA approaches that rely on statistical correlation or historical incidents, Syncause is built on causal inference using eBPF-based system data. This enables it to understand what directly led to an incident rather than what merely co-occurred with it.

See Causality in Action: A Real-World Scenario

Imagine a typical production fire-fight: latency for your checkout service suddenly spikes. Your dashboards light up—database CPU is at 100%, a Redis cache shows mass timeouts, and Kubernetes pods are restarting. Traditional tools will show you that these events are correlated, but which one is the root cause?

Syncause builds a real-time causal graph from the ground up. It would show you that a newly deployed background job (PID 45678) initiated heavy disk I/O, which starved the database’s primary transaction process (PID 12345) of resources. This single bottleneck was the direct cause of the API latency. The cache timeouts and pod restarts were merely downstream symptoms.

This is the power of causality over correlation.

What Makes Syncause Different

1. No long-term model training required
   Syncause does not rely on statistical learning from past data. By collecting real-time causal data through eBPF, it directly observes how failures propagate.
2. Independent of historical incidents
   Syncause performs RCA from current system behavior — even without any prior incident records. Historical data, when available, is used as context rather than a requirement.
3. Effective on novel failures
   Because the analysis is causal rather than pattern-based, Syncause can diagnose entirely new types of issues that machine learning models or rule-based systems would miss.
4. Explainable RCA results
   The output is not just a prediction but a structured causal explanation, showing how system components influenced one another to produce the failure.

Closing Thoughts

As AI continues to penetrate the SRE domain, we’re seeing a natural divergence — between products that learn from the past and those that observe the present. Both have their strengths, and both will likely converge over time.

Syncause is committed to advancing the observability-driven path, where causality, not correlation, becomes the foundation of reliable root cause analysis. But our vision extends beyond simply shortening incidents. By continuously identifying hidden causal chains, we help you uncover latent architectural weaknesses and anti-patterns. We empower your team to move from reactive fire-fighting to proactive resilience engineering, building a fundamentally more robust system.

If you’re interested in how Syncause achieves higher RCA accuracy through causal data analysis, we invite you to read our previous technical deep dive.

Take the Next Step

Curious to see the causal graph for yourself? Dive into our interactive sandbox—no setup or registration required—and explore a real-world incident firsthand.

If you want to see how this could impact your team's metrics, feel free to book a demo where we'll show you how Syncause can cut MTTR for incidents within your specific tech stack.