1. Overall AIOps GenAI lifecycle

This is the executive/architecture-level view.

flowchart LR A[Operational Data] --> B[Data & Knowledge Engineering] B --> C[GenAI Model Development] C --> D[Evaluation & Validation] D --> E[Model Registry] E --> F[Deployment] F --> G[Runtime AIOps] G --> H[Incident / Alert / User Request] H --> I[GenAI Reasoning & RAG] I --> J[Recommendation / Action] J --> K[Outcome] K --> L[Feedback & Telemetry] L --> B L --> C L --> D style A fill:#E3F2FD style B fill:#E8F5E9 style C fill:#F3E5F5 style D fill:#FFF3E0 style E fill:#FCE4EC style F fill:#E0F7FA style G fill:#E8F5E9 style L fill:#FFF3E0

2. Data & Knowledge Engineering Flow

This shows how raw AIOps data becomes usable knowledge for GenAI/RAG.

flowchart TD A[Enterprise / AIOps Data Sources] A --> B1[Logs] A --> B2[Metrics] A --> B3[Traces] A --> B4[Alerts] A --> B5[Incidents] A --> B6[CMDB] A --> B7[Topology] A --> B8[Change Records] A --> B9[Runbooks] A --> B10[Service Documentation] B1 --> C[Data Ingestion] B2 --> C B3 --> C B4 --> C B5 --> C B6 --> C B7 --> C B8 --> C B9 --> C B10 --> C C --> D[Parsing & Normalization] D --> E[Deduplication & Cleaning] E --> F[PII / Secret Removal] F --> G[Data Quality Validation] G --> H[Context & Metadata Enrichment] H --> I[Chunking] I --> J[Embedding Generation] J --> K[(Vector Database)] H --> L[(Operational / Knowledge Store)] H --> M[Structured Knowledge] M --> N[Entity / Service / Dependency Relationships] N --> O[(Knowledge Graph / CMDB)] K --> P[RAG Retrieval] L --> P O --> P style A fill:#E3F2FD style C fill:#E8F5E9 style K fill:#F3E5F5 style L fill:#F3E5F5 style O fill:#F3E5F5

3. GenAI Model Development Lifecycle

This is the model engineering / MLOps lifecycle.

flowchart TD A[Business / AIOps Use Case] A --> B[Define AI Task] B --> C{Model Strategy} C --> C1[Prompt Engineering] C --> C2[RAG] C --> C3[Fine-Tuning] C --> C4[Foundation Model Selection] C1 --> D[Model Development] C2 --> D C3 --> D C4 --> D D --> E[Training / Fine-Tuning] E --> F[Model Evaluation] F --> F1[Accuracy] F --> F2[Groundedness] F --> F3[Hallucination] F --> F4[Latency] F --> F5[Cost] F --> F6[Security] F --> F7[AIOps Task Performance] F1 --> G{Evaluation Passed?} F2 --> G F3 --> G F4 --> G F5 --> G F6 --> G F7 --> G G -- No --> H[Improve Prompt / Data / Model] H --> D G -- Yes --> I[Model Registration] I --> J[Versioning] J --> K[Approval] K --> L[Deployment Pipeline] L --> M[Dev] M --> N[Test] N --> O[Staging] O --> P[Production] style A fill:#E3F2FD style C fill:#FFF3E0 style D fill:#F3E5F5 style F fill:#FFF3E0 style I fill:#FCE4EC style P fill:#E8F5E9

4. End-to-End Runtime Request Flow

This is probably the most important diagram for explaining how an AIOps request travels through the GenAI system.

flowchart TD A[Alert / Incident / User Request] A --> B[API Gateway] B --> C[Authentication & Authorization] C --> D[AIOps Orchestrator] D --> E[Request Classification] E --> E1[Incident Analysis] E --> E2[Root Cause Analysis] E --> E3[Alert Explanation] E --> E4[Prediction] E --> E5[Remediation] E --> E6[Knowledge Query] E1 --> F[Context Collection] E2 --> F E3 --> F E4 --> F E5 --> F E6 --> F F --> F1[Logs] F --> F2[Metrics] F --> F3[Traces] F --> F4[Topology] F --> F5[CMDB] F --> F6[Recent Incidents] F --> F7[Changes] F --> F8[Alerts] F1 --> G[Context Enrichment] F2 --> G F3 --> G F4 --> G F5 --> G F6 --> G F7 --> G F8 --> G G --> H[RAG Retrieval] H --> H1[(Vector DB)] H --> H2[(Knowledge Graph)] H --> H3[(Runbook / Document Store)] H1 --> I[Retrieved Context] H2 --> I H3 --> I G --> J[Prompt Construction] I --> J J --> K[GenAI Model] K --> L[Guardrails & Validation] L --> M{Action Required?} M -- No --> N[Generate Explanation / Recommendation] M -- Yes --> O[Agent / Tool Execution] O --> O1[ITSM] O --> O2[Cloud] O --> O3[Kubernetes] O --> O4[Monitoring] O --> O5[Runbook Automation] O1 --> P[Execution Result] O2 --> P O3 --> P O4 --> P O5 --> P P --> Q[Post-Action Validation] Q --> R{Resolved?} R -- No --> F R -- Yes --> N N --> S[Response] S --> T[ChatOps / ITSM / Dashboard] style A fill:#E3F2FD style D fill:#E8F5E9 style H fill:#F3E5F5 style K fill:#7E57C2,color:#fff style L fill:#FFF3E0 style O fill:#FFCC80 style S fill:#A5D6A7

5. Agentic Remediation Flow

For AIOps, this deserves its own diagram because the model may not simply return text. It can decide to invoke tools or automation.

flowchart TD A[Incident / Alert] --> B[GenAI Agent] B --> C[Understand Problem] C --> D[Collect Context] D --> E[Logs] D --> F[Metrics] D --> G[Traces] D --> H[Topology] D --> I[Incident History] E --> J[Reasoning] F --> J G --> J H --> J I --> J J --> K[Generate Hypothesis] K --> L[Validate Hypothesis] L --> M{Sufficient Evidence?} M -- No --> D M -- Yes --> N{Remediation Required?} N -- No --> O[Generate RCA / Recommendation] N -- Yes --> P[Select Runbook / Tool] P --> Q{Approval Required?} Q -- Yes --> R[Human Approval] Q -- No --> S[Execute Action] R --> T{Approved?} T -- No --> O T -- Yes --> S S --> U[Automation Platform] U --> U1[Restart Service] U --> U2[Scale Resource] U --> U3[Rollback Change] U --> U4[Clear Resource] U --> U5[Execute Runbook] U1 --> V[Validate System] U2 --> V U3 --> V U4 --> V U5 --> V V --> W{Issue Resolved?} W -- No --> X[Escalate / Investigate Further] W -- Yes --> Y[Close / Update Incident] Y --> Z[Capture Outcome & Feedback] style A fill:#E3F2FD style B fill:#7E57C2,color:#fff style J fill:#F3E5F5 style P fill:#FFF3E0 style R fill:#FFCC80 style U fill:#E8F5E9 style Y fill:#A5D6A7 style Z fill:#FCE4EC

6. Continuous Learning & Improvement Loop

This completes the lifecycle. Production incidents become feedback for improving prompts, RAG, evaluation datasets, and potentially the model.

flowchart LR A[Production AIOps Requests] --> B[LLM Observability] B --> B1[Latency] B --> B2[Token Usage] B --> B3[Cost] B --> B4[Model Errors] B --> B5[Hallucinations] B --> B6[Tool Failures] A --> C[Outcome Data] C --> C1[Resolved] C --> C2[Not Resolved] C --> C3[Human Override] C --> C4[User Feedback] B --> D[Evaluation Dataset] C --> D D --> E[Offline Evaluation] E --> F{Quality Gap?} F -- No --> G[Continue Monitoring] F -- Yes --> H[Identify Root Cause] H --> H1[Prompt Issue] H --> H2[RAG Issue] H --> H3[Data Issue] H --> H4[Model Issue] H --> H5[Tool / Agent Issue] H1 --> I[Improve Prompt] H2 --> J[Improve Knowledge / Retrieval] H3 --> K[Improve Data] H4 --> L[Fine-Tune / Change Model] H5 --> M[Improve Agent / Tools] I --> N[Regression Testing] J --> N K --> N L --> N M --> N N --> O[Model / Application Evaluation] O --> P{Approved?} P -- No --> H P -- Yes --> Q[Deploy New Version] Q --> A style A fill:#E8F5E9 style B fill:#E3F2FD style D fill:#FFF3E0 style E fill:#F3E5F5 style H fill:#FFCC80 style Q fill:#A5D6A7

How the six diagrams connect

flowchart LR A["1. Overall Lifecycle"] B["2. Data & Knowledge"] C["3. Model Development"] D["4. Runtime Request"] E["5. Agentic Remediation"] F["6. Continuous Improvement"] A --> B B --> C C --> D D --> E E --> F F --> B F --> C style A fill:#E3F2FD style B fill:#E8F5E9 style C fill:#F3E5F5 style D fill:#FFF3E0 style E fill:#FFCC80 style F fill:#FCE4EC