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Documentation Index

Fetch the complete documentation index at: https://docs.scrubbe.com/llms.txt

Use this file to discover all available pages before exploring further.

Ezra is Scrubbe’s embedded AI reasoning engine. When you submit an incident for analysis, Ezra runs a four-stage pipeline — context gathering, hypothesis generation, impact assessment, and remediation planning — producing a structured root cause analysis with confidence scoring. You can then generate audience-specific reports from that analysis, submit outcome feedback so Ezra learns from every incident, and query the recurring failure patterns it has detected across your incident history.
All endpoints require Authorization: Bearer <token>. Analysis runs asynchronously — submit with POST /analyse and poll GET /analysis/:incidentId until status is COMPLETE.
Base path: https://your-api-domain.com/api/v1/ezra

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Analyse an incident

Submit an incident to Ezra for AI analysis. Ezra examines the incident timeline, affected services, correlated signals, and recent changes to produce a root cause analysis, impact assessment, and remediation options. POST https://your-api-domain.com/api/v1/ezra/analyse
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incidentId
string
required
ID of the incident to analyse.
curl -X POST https://your-api-domain.com/api/v1/ezra/analyse \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{ "incidentId": "INC-20260522-0041" }'
​
analysisId
string
required
Unique identifier for this analysis run.
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incidentId
string
required
The incident being analysed.
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status
string
required
PROCESSING while the pipeline is running; COMPLETE or FAILED when done.
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estimatedCompletionSeconds
number
Approximate seconds before the analysis is ready to retrieve.

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List all analyses

Returns all completed and in-progress analyses for the workspace, ordered by creation date descending. Use this to audit Ezra activity or populate a history view. GET https://your-api-domain.com/api/v1/ezra/analyses
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page
number
default:"1"
Page number.
​
limit
number
default:"20"
Items per page (max 100).
​
status
string
Filter by analysis status: PROCESSING, COMPLETE, or FAILED.
cURL
curl "https://your-api-domain.com/api/v1/ezra/analyses?status=COMPLETE&limit=50" \
  -H "Authorization: Bearer <token>"
​
data
object[]
Array of analysis summary objects.
​
total
number
Total matching analysis count.

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Get the latest analysis for an incident

Returns the most recently completed analysis for a specific incident, including hypothesis, root cause, impact assessment, remediation options, and confidence score. GET https://your-api-domain.com/api/v1/ezra/analysis/:incidentId
​
incidentId
string
required
The incident ID.
cURL
curl https://your-api-domain.com/api/v1/ezra/analysis/INC-20260522-0041 \
  -H "Authorization: Bearer <token>"
​
analysisId
string
required
Unique analysis identifier.
​
status
string
required
Analysis lifecycle status.
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hypothesis
string
Ezra’s leading hypothesis from the hypothesis-generation stage.
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rootCause
string
AI-identified primary cause of the incident.
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impactAssessment
string
Structured assessment of user and business impact.
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contributingFactors
string[]
Secondary factors that amplified impact or extended duration.
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remediationOptions
string[]
Ordered list of recommended remediation steps.
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confidence
number
Confidence score from 0 to 1. Scores above 0.8 indicate high certainty.
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completedAt
string
ISO 8601 timestamp when the analysis pipeline finished.

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Generate a report

Generate a written report from a completed analysis, tailored to the target audience. Engineering reports include technical detail, system context, and step-by-step remediation. Leadership reports focus on business impact, timeline, and recovery status. POST https://your-api-domain.com/api/v1/ezra/report
​
analysisId
string
required
ID of the completed analysis to generate a report from.
​
audience
string
required
Target audience: ENGINEER or LEADERSHIP.
curl -X POST https://your-api-domain.com/api/v1/ezra/report \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{ "analysisId": "ANLYS-20260522-0099", "audience": "LEADERSHIP" }'
​
reportId
string
required
Unique identifier for the generated report.
​
audience
string
required
The audience the report was generated for: ENGINEER or LEADERSHIP.
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content
string
required
The generated report text.
​
generatedAt
string
required
ISO 8601 timestamp of report generation.

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Submit outcome feedback

Send outcome feedback to Ezra after an incident is fully resolved. Ezra uses this signal to improve hypothesis accuracy and pattern detection over time. Submit one feedback record per incident. POST https://your-api-domain.com/api/v1/ezra/learn
​
incidentId
string
required
ID of the resolved incident.
​
outcome
string
required
Resolution outcome: RESOLVED, DEGRADED, NEUTRAL, or WORSENED.
curl -X POST https://your-api-domain.com/api/v1/ezra/learn \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{ "incidentId": "INC-20260522-0041", "outcome": "RESOLVED" }'
OutcomeWhen to use
RESOLVEDIncident fully resolved; Ezra’s suggestions were accurate and helpful.
DEGRADEDIncident partially resolved; situation improved but not fully recovered.
NEUTRALNo clear improvement or worsening from the recommended actions.
WORSENEDFollowing Ezra’s suggestions made the situation worse.
Call POST /learn after every resolved incident to improve Ezra’s pattern detection and future recommendation quality.

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Get detected patterns

Returns recurring failure patterns that Ezra has identified across your incident history. Use this to find systemic weaknesses and prioritise reliability improvements before the next incident. GET https://your-api-domain.com/api/v1/ezra/patterns
cURL
curl https://your-api-domain.com/api/v1/ezra/patterns \
  -H "Authorization: Bearer <token>"
​
data
object[]
Array of detected failure patterns.

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Ezra reasoning pipeline

Ezra processes each analysis through four sequential stages. Understanding the pipeline helps you interpret confidence scores and result structure.
StageNameWhat Ezra does
1Context gatheringCollects the incident timeline, correlated signals, deployment history, and service topology.
2Hypothesis generationProposes candidate root causes ranked by supporting evidence.
3Impact assessmentQuantifies user and business impact from service dependency data.
4Remediation planningProduces ordered remediation options weighted by historical outcome data.