Agentic AI Incident Resolution System
An enterprise AI platform that automates incident resolution workflows using watsonx Orchestrate, vector databases, and intelligent automation tools.
Overview
Intelligent SRE Assistant that coordinates specialized tools for automated incident resolution through a lightweight orchestration layer.
Core Approach: watsonx Orchestrate handles routing and flow control → External tools perform heavy computational tasks
Architecture
Orchestration: watsonx Orchestrate provides intent classification, tool routing, and flow control
Tools: MCP servers and REST services handle retrieval, planning, execution, and verification
System Components
1. Intake & Classification
Extracts incident context (service, environment, severity, symptoms, error codes) and routes to appropriate tools
2. Hybrid Retrieval
Primary: Milvus vector search across IBM documentation (top_k=5, threshold=0.82)
Fallback: FAISS for internal SOPs and automation catalog
3. Intelligent Planning
Agentic RAG validates preconditions, analyzes Instana data, and requests approvals for risky operations
4. Safe Execution
Ansible automation with mandatory dry-runs, RBAC enforcement, and automatic rollback capability
Hybrid RAG Strategy
HyDE + BM25 Retrieval:
- Parse incident symptoms and apply platform/environment filters
- Generate hypothetical documents (HyDE) and encode
- Dual retrieve: HyDE + BM25 keyword search
- Fuse results, validate preconditions, and re-rank
- Select best template based on composite score
Selection Criteria:
- Platform/environment match (mandatory)
- Preconditions satisfied (all must be true)
- Risk level (prefer lowest)
- Verification coverage (prefer highest)
Technology Stack
AI & Orchestration:
- watsonx Orchestrate for agent coordination
- watsonx.ai for LLM inference and planning
- Milvus for vector similarity search (10M+ documents)
- FAISS for internal SOPs and policies
Automation & Monitoring:
- Ansible Automation Platform with RBAC
- Instana APM for health verification
- ServiceNow for ITSM/CMDB integration
Supported Platforms:
- Operating Systems: RHEL, AIX, Windows Server
- IBM Middleware: Db2, WebSphere, MQ, Tivoli, DataStage
Safety & Guardrails
Execution Controls:
- Mandatory dry-run before implementation
- RBAC enforcement at Ansible layer
- Explicit approval for destructive operations
- Production environment gates
Reliability:
- Configurable timeouts (default 300s)
- Idempotent playbook design
- Mandatory rollback templates
- Automated verification gates
Security:
- mTLS for inter-service communication
- Vault integration for credentials
- Audit logging for all actions
Performance Targets
Response Times:
- Retrieval operations: Less than 10s (P99)
- Automated resolutions: Less than 20 min MTTR
- System availability: 99.9%
Scalability:
- 100 concurrent incidents
- 1000 requests/minute per tenant
- Milvus sharding for 10M+ documents
Workflow Example
Incident Resolution Flow:
- Submit incident with context and symptoms
- Retrieve similar past incidents from vector database
- Plan remediation strategy using AI reflection
- Approve medium/high-risk operations
- Execute dry-run, then actual playbook
- Verify health metrics via Instana
- Rollback automatically if degradation detected
- Update ServiceNow ticket with results
Key Features
Intelligent Context:
- Historical incident similarity search
- Platform-aware template selection
- Environment-specific preconditions
Autonomous Operation:
- Auto-resolve for low-risk incidents
- Approval workflows for production
- 24/7 incident response capability
Complete Audit Trail:
- All actions logged for compliance
- Execution artifacts persisted
- ServiceNow integration for tickets
Results
Operational Impact:
- Reduced MTTR for common incidents
- Automated handling of repetitive tasks
- 24/7 autonomous response capability
Safety & Reliability:
- Approval gates prevent unauthorized changes
- Validation ensures remediation effectiveness
- Rollback minimizes risk of failures
Governance:
- Complete audit trail for compliance
- RBAC integration with enterprise identity
- Automated documentation generation
Technologies Used
AI/ML: watsonx.ai, watsonx Orchestrate, Milvus, FAISS
Automation: Ansible Automation Platform, Python
Monitoring: Instana APM
Integration: ServiceNow, Model Context Protocol (MCP)
Infrastructure: Docker, Kubernetes, RHEL, AIX, Windows