tomevault-io RCA Copilot Agent
The RCA Copilot is the AI-powered interface that transforms raw observability data into actionable incident explanations. It leverages the Neptune knowledge graph and DynamoDB context cache to achieve sub-2-minute MTTR for Tier-1 incidents.
Core Responsibilities
- Context Retrieval: Fetch pre-computed incident context from cache
- Graph Expansion: Query Neptune for blast radius and lineage
- Evidence Ranking: Score and rank root cause candidates
- Explanation Generation: Produce natural language incident summaries
- Action Recommendation: Suggest remediation steps and runbooks
Architecture
┌─────────────────────────────────────────────────────────────────┐
│ RCA COPILOT │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Context Builder │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │
│ │ │ Cache │ │ Graph │ │Evidence │ │Timeline │ │ │
│ │ │ Fetch │ │ Expand │ │ Rank │ │ Build │ │ │
│ │ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │ │
│ │ │ │ │ │ │ │
│ │ └────────────┴────────────┴────────────┘ │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ ┌─────────────────────┐ │ │
│ │ │ Incident Context │ │ │
│ │ │ (Structured) │ │ │
│ │ └──────────┬──────────┘ │ │
│ └─────────────────────────┼───────────────────────────────┘ │
│ │ │
│ ┌─────────────────────────┼───────────────────────────────┐ │
│ │ ▼ │ │
│ │ ┌─────────────────────┐ │ │
│ │ │ LLM Interface │ │ │
│ │ │ (Claude/Bedrock) │ │ │
│ │ └──────────┬──────────┘ │ │
│ │ │ │ │
│ │ ┌─────────────────────┐ │ │
│ │ │ Explanation + Actions│ │ │
│ │ └─────────────────────┘ │ │
│ │ Explanation Engine │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
Context Builder
Cache Fetch (O(1) Latency)
async def fetch_incident_context(incident_id: str) -> IncidentContext:
"""Fetch pre-computed context from DynamoDB cache."""
response = await dynamodb.get_item(
TableName="IncidentContextCache",
Key={"pk": incident_id}
)
return IncidentContext(
incident_id=response["incident_id"],
primary_asset=response["primary_asset"],
top_evidence=response["top_evidence"],
blast_radius=response["blast_radius"],
timeline=response["timeline"]
)
Graph Expansion
async def expand_blast_radius(asset_urn: str, depth: int = 3) -> BlastRadius:
"""Query Neptune for downstream affected assets."""
query = f"""
g.V().has('urn', '{asset_urn}')
.repeat(out('PRODUCES', 'FEEDS_INTO').simplePath())
.times({depth})
.dedup()
.valueMap(true)
"""
downstream = await neptune.execute(query)
return BlastRadius(
source=asset_urn,
affected_assets=downstream,
depth=depth
)
Evidence Ranking
def rank_evidence(evidence_list: list[Evidence]) -> list[RankedEvidence]:
"""Rank root cause candidates by confidence and recency."""
scored = []
for e in evidence_list:
score = (
e.confidence * 0.4 + # Base confidence
recency_score(e.timestamp) * 0.3 + # Recent = higher
correlation_score(e) * 0.3 # Correlated to incident
)
scored.append(RankedEvidence(evidence=e, score=score))
return sorted(scored, key=lambda x: x.score, reverse=True)
Timeline Construction
async def build_timeline(incident_id: str, window: str = "1h") -> Timeline:
"""Construct incident timeline from events."""
events = await get_events_for_incident(incident_id, window)
return Timeline(
incident_id=incident_id,
events=[
TimelineEvent(
timestamp=e.timestamp,
event_type=e.type,
description=e.summary,
asset=e.asset_urn
)
for e in sorted(events, key=lambda x: x.timestamp)
]
)
Explanation Engine
LLM Prompt Template
EXPLANATION_PROMPT = """
You are an expert data observability engineer analyzing an incident.
## Incident Context
- Incident ID: {incident_id}
- Primary Asset: {primary_asset}
- Severity: {severity}
- Detection Time: {detection_time}
## Timeline
{timeline}
## Evidence (ranked by confidence)
{evidence}
## Blast Radius
Affected downstream assets:
{blast_radius}
## Your Task
1. Explain the root cause in plain language
2. Describe the impact on downstream consumers
3. Recommend immediate actions
4. Suggest preventive measures
Keep the explanation concise but complete. Focus on actionable insights.
"""
Explanation Generation
async def generate_explanation(context: IncidentContext) -> Explanation:
"""Generate natural language incident explanation."""
prompt = EXPLANATION_PROMPT.format(
incident_id=context.incident_id,
primary_asset=context.primary_asset,
severity=context.severity,
detection_time=context.detection_time,
timeline=format_timeline(context.timeline),
evidence=format_evidence(context.ranked_evidence),
blast_radius=format_blast_radius(context.blast_radius)
)
response = await bedrock.invoke(
model="anthropic.claude-3-sonnet",
prompt=prompt,
max_tokens=1000
)
return Explanation(
root_cause=extract_root_cause(response),
impact=extract_impact(response),
actions=extract_actions(response),
prevention=extract_prevention(response)
)
Output Format
Copilot Response
{
"incident_id": "INC-2026-01-04-001",
"query_latency_ms": 1823,
"root_cause": {
"summary": "Schema validation failed at ingestion gateway",
"details": "Field total_amount expected double, received string",
"confidence": 0.94
},
"evidence": [
{
"type": "schema_rejection",
"description": "94% reject rate increase after 12:10Z",
"confidence": 0.94
},
{
"type": "deployment_correlation",
"description": "Rejections started 2 minutes after orders-api deployment",
"confidence": 0.87
}
],
"impact": {
"blast_radius": ["orders_enriched topic", "bronze.orders_enriched", "silver.orders"],
"affected_teams": ["orders-team", "analytics-team"],
"data_loss_estimate": "~5000 events not ingested"
},
"recommended_actions": [
{
"action": "rollback",
"target": "orders-api",
"command": "kubectl rollout undo deployment/orders-api",
"urgency": "immediate"
},
{
"action": "runbook",
"target": "schema_mismatch",
"url": "https://runbooks.internal/schema-mismatch",
"urgency": "follow-up"
}
],
"prevention": [
"Enable schema compatibility check in CI pipeline",
"Add contract validation gate for orders-api endpoint"
]
}
API Endpoints
Query Incident
POST /api/v1/incidents/{incident_id}/explain
Ask Natural Language
POST /api/v1/ask
{
"question": "Why is the orders dashboard stale?",
"context": {
"asset_urn": "urn:dashboard:prod:orders-overview"
}
}
Get Blast Radius
GET /api/v1/assets/{asset_urn}/blast-radius?depth=3
Scripts
scripts/context_builder.py: Incident context assemblyscripts/graph_queries.py: Neptune Gremlin queriesscripts/evidence_ranker.py: Evidence scoring and rankingscripts/explanation_engine.py: LLM-powered explanationsscripts/action_recommender.py: Remediation suggestions
References
references/prompt-templates/: LLM prompt templatesreferences/runbook-mapping.md: Incident type to runbook mappingreferences/action-catalog.md: Available remediation actions
Configuration
rca_copilot:
enabled: true
context_builder:
cache_table: "IncidentContextCache"
graph_endpoint: "wss://neptune.us-east-1.amazonaws.com:8182/gremlin"
explanation_engine:
provider: "bedrock"
model: "anthropic.claude-3-sonnet"
max_tokens: 1000
temperature: 0.3
api:
port: 8080
rate_limit: 100 # requests per minute
sla:
max_latency_ms: 120000 # 2 minutes
Integration Points
| System | Integration | Purpose |
|---|---|---|
| DynamoDB | SDK | Context cache retrieval |
| Neptune | Gremlin | Graph queries |
| Bedrock | API | LLM inference |
| Slack | Webhook | Alert delivery |
| PagerDuty | API | Incident escalation |
| S3 | SDK | Runbook storage |
Performance Requirements
- Cache hit latency: < 10ms
- Graph expansion: < 500ms for depth=3
- LLM generation: < 2000ms
- Total query latency: < 2 minutes (SLA for Tier-1)
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