Why Traditional Root Cause Analysis Breaks Under Pressure
Most monitoring systems report symptoms. Some group alerts by time proximity. Few can prove why alerts belong together.
When incidents escalate, responders need answers that hold up under pressure:
- What failed first?
- What does it actually affect?
- What should we do next?
Root cause analysis that depends on static topology or time-based grouping often fails as infrastructure scales, fails over, or reconfigures in real time.
This framework is designed to evaluate whether dependency-aware reasoning survives real operational conditions — not just controlled demos.
What This Evaluation Tests
The Evaluator Test Plan walks through six structured scenarios to validate root cause, blast radius, and change risk using a live dependency model.
- Dependency Model Integrity
Confirm the underlying service dependency model is real, explainable, queryable, and current.
- Root Cause Analysis (Upstream Traversal)
Validate that initiating cause is identified using dependency paths — not just alert clustering.
- Blast Radius & Service Impact
Confirm downstream impact is calculated using real service dependencies and redundancy awareness.
- Staying Current During Infrastructure Change
Ensure temporary links fade out, new components are placed correctly, and service identity remains stable during scaling or failover.
- Change Risk Before Production Updates
Evaluate predicted downstream impact before making structural changes.
- Guardrails Around Automated Recommendations
Verify that suggested next steps are explainable, reviewable, and surface uncertainty when evidence is incomplete.
Each scenario includes steps, pass criteria, red flags, and structured evidence capture guidance.
What Makes Dependency-Aware Root Cause Different
This framework evaluates more than alert grouping. It tests whether reasoning is grounded in live dependency structure.
A dependency-aware system should:
- Traverse upstream to isolate initiating cause
- Traverse downstream to calculate real blast radius
- Preserve service identity as infrastructure shifts
- Reflect redundancy in service health
- Update as relationships change
- Surface uncertainty instead of guessing
- Show the dependency path behind every conclusion
If those conditions are not met, root cause analysis and blast radius calculations drift over time.
Who This Framework Is For
- SRE and incident response teams
- Platform engineering
- Reliability and architecture leaders
- Technical evaluators comparing approaches
- Organizations validating service dependency mapping tools
If you are evaluating reasoning quality — not just feature lists — this framework is designed for you.
What You Should Be Able to Confirm
By the end of the evaluation, you should be able to confidently determine whether:
- Initiating cause is identified, not just grouped
- Blast radius reflects real service structure
- Redundancy is handled correctly
- Reasoning remains explainable during incidents
- The dependency model stays accurate as systems change
- Change risk is specific enough to guide decisions
- Automated suggestions are safe and reviewable
If any of these fail, the scorecard makes it visible.
What You Need to Run This Evaluation
This framework assumes the following architecture:
- Asset Inventory Management (AIM)
Builds the live asset and service dependency model.
- Actionable Observability (AO)
Enables the Knowledge Discovery Engine (KDE) to reason over that model.
Some scenarios can be reviewed using uploaded data.
Scenarios involving structural updates require live integrations.
Download the Evaluation Materials
Download Evaluator Test Plan
Download Evaluation Checklist
Build the Foundation First
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Explore the Technical Architecture
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