Status
Live
Production-ready
Strategic Intelligence
Root Cause Agent
Investigates detected problems using multiple analysis layers: activity logs for correlated failures, knowledge graph for entity relationships and impact paths, vector memory for similar past cases, and an optional Pytho
Intelligence & AnalyticsLiveInternal (Colaberry Enterprise)Verified
Department
Strategic Intelligence
Intelligence & Analytics department for Colaberry Enterprise agents
Source
Internal (Colaberry Enterprise)
Built by Colaberry
About
About the Agent
What this agent does, the challenges it addresses, and where it delivers value.
Investigates detected problems using multiple analysis layers: activity logs for correlated failures, knowledge graph for entity relationships and impact paths, vector memory for similar past cases, and an optional Python ML proxy for advanced root cause analysis. Produces a structured root cause result with confidence scoring.
Challenges This Agent Addresses
- 1**IT Operations**: Traces an agent failure back to a configuration change or dependency issue using activity logs and knowledge graph
- 2**Marketing**: Identifies whether a conversion drop stems from targeting changes, content quality, or external factors
- 3**Platform Engineering**: Correlates error spikes with recent system changes across related entities
Workflow
How the Agent Works
Step-by-step operational flow showing how this agent processes tasks end-to-end.
1
Step 1
**Activity log analysis**: Queries failed actions from the past 48 hours to identify correlated failure patterns
2
Step 2
**Knowledge graph traversal**: Looks up the affected entity, finds related entities within 2 hops, and traces impact propagation paths
3
Step 3
**Vector memory search**: Searches for similar past investigations (similarity threshold above 0.6) to leverage institutional memory
4
Step 4
**Python ML proxy** (optional, best-effort): Sends problem metrics to the ML service for advanced root cause suggestions
5
Step 5
**Deterministic rules**: Applies problem-type-specific rules (e.g., "check agent configuration" for agent failures, "review campaign targeting" for conversion drops)
6
Step 6
**Confidence calibration**: Starts at 0.5 and increases incrementally based on evidence found at each layer (correlated failures +0.1, impact paths +0.05, similar cases +0.05 each, ML results +0.15)
Execution Modes
Trigger: event (invoked by the autonomous engine after problem discovery)
Data
Inputs & Outputs
What data this agent consumes and the artifacts or actions it produces.
Input Data
- `DetectedProblem` from the Problem Discovery Agent (type, severity, entity info, metrics)
- Trace ID for correlation across the investigation pipeline
Deliverables
- `RootCauseResult` containing:
- Original problem reference
- List of identified root causes (strings)
- Related entities from the knowledge graph
- Similar past cases with similarity scores
- Overall confidence (0 to 1)
- Human-readable reasoning summary
Core Tasks
- Strategic Intelligence
Integrations
Systems Connected
Internal systems, APIs, and tools this agent integrates with.
Tools & APIs
Receives problems from **Problem Discovery Agent**Queries **AiAgentActivityLog** for correlated failuresTraverses the **Knowledge Graph** for entity relationshipsSearches **Vector Memory** for historical investigationsOptionally calls the **Python ML Proxy** for advanced analysisOutputs feed into **Action Planner Agent** for remedy recommendations
Specifications
Agent Specs
Technical specifications, requirements, and deployment details.
Related
Related Agents
Other agents in the same department or industry.
Enterprise AI
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