Enhanced Automation Plan:

Hybrid IT Checkup & Support Diagnostics with Advanced Retrieval Strategies and HITL Oversight

Output:

• A unified JSON context block that includes detailed IT checkup data (e.g., {hostname}, {OS}, {RAM}, {Disk}, {CPU_usage}, {network_info}) with screenshots, screencasts, and extracted ticket excerpts
• Multi-dimensional retrieval results combining BM25 keyword matches, semantic similarity, discourse relationship mapping, and contextual relevance
• A diagnostic summary that highlights key issues with multiple evidence paths and confidence scores
• A draft support ticket response with clear troubleshooting steps, KB references, and context-aware recommendations

Inputs:

• {host_inventory}: Inventory data for target systems (e.g., DEMO-WS-01)
• {playbooks}: Ansible playbooks (or equivalent scripts) to run IT checkups
• {multimodal_docs}: Multimodal content including screenshots, screencasts, PDFs, and existing tickets
• {support_query}: Natural language query (e.g., “PowerScribe slow”)
• {existing_tickets}: Historical ticket data to provide context
• {KB_docs}: Knowledge base documents and technical reference materials
• {segmentation_metadata}: Metadata and contextual embeddings from KB document segmentation
• {redis_connection_details}: Configuration for logging and retrieving structured data

Tools Selected:

• PDFSearchTool: Extract text from PDFs for KB indexing
• VisionTool: Extract information from images (screenshots, screencasts)
• Redis: Store and retrieve structured IT checkup data with temporal context windows
• Vector DB (FAISS/ChromaDB): Support semantic embedding-based search and BM25-style keyword matching
• SpaCy Adapter: For generating embeddings and processing linguistic features
• Ohm Data Types: For structured data handling across retrieval operations

Agents:

1. Checkup Automation Agent: Executes IT checkup playbooks to gather infrastructure facts and extracts existing ticket details. (Inspired by Radagast’s attention to detail)

2. Hybrid Retrieval Agent: Combines BM25 keyword matching with embedding-based semantic search for fast and comprehensive KB document retrieval. (Inspired by the Blue Wizards’ strategic search abilities)

3. Contextual Query Agent: Implements specialized retrieval strategies including semantic search, related context finding, temporal context analysis, discourse mapping, and syntactic pattern matching. (Inspired by Saruman’s precision)

4. Data Merger & Context Composer: Merges outputs from all agents into a comprehensive unified JSON context with clear evidence paths. (Inspired by the collective wisdom of the Istari)

5. Diagnostic Summarizer Agent: Analyzes the unified context to generate an actionable diagnostic summary with confidence scores. (Inspired by Gandalf’s wisdom)

6. Ticket Response Generator: Crafts concise, actionable support responses using the diagnostic summary and relevant KB references. (Inspired by the White Council’s decisive action)

7. Human-In-The-Loop (HITL) Agent: Periodically reviews outputs, verifies critical diagnostics, provides adjustments, and ensures quality control throughout the process. (Inspired by the guardianship of Middle-earth)

Advanced Retrieval Strategies:

1. BM25 Pre-Filtering: Fast keyword-based retrieval for direct matches to error codes and technical terms
2. Semantic Search: Finding relevant KB segments based on embedding similarity to the support query
3. Related Context Finding: Discovering contextually connected KB documents through topic and entity overlap
4. Temporal Context Analysis: Identifying relevant historical ticket patterns with appropriate time windows
5. Discourse Relationship Mapping: Analyzing relationships between symptoms, causes, and solutions
6. Syntactic Pattern Matching: Identifying specific linguistic constructions that signal technical issues
7. Embedding Similarity Comparison: Finding contextually similar support cases using vector similarity

Tasks:

Task 1: IT Checkup Automation (CA-1)

• Description: Checkup Automation Agent runs {playbooks} against {host_inventory} to capture system facts and multimodal inputs from {multimodal_docs} along with {existing_tickets}.
• Implementation:
  • Execute IT checkup playbooks
  • Capture system metrics, screenshots, and logs
  • Extract relevant details from existing tickets
  • Store structured JSON snapshot in Redis
• Assigned to: Checkup Automation Agent

Task 2: Hybrid Keyword & Semantic Retrieval (BM25-1 & Semantic-1)

• Description: Hybrid Retrieval Agent processes {support_query} against indexed {KB_docs} using both BM25 and semantic embedding search.
• Implementation:
  • Apply BM25 ranking to retrieve direct keyword matches
  • Convert query to embedding for semantic similarity search
  • Combine results with weighted scoring
  • Flag high-confidence retrieval hits
• Assigned to: Hybrid Retrieval Agent

Task 3: Multi-dimensional Contextual Retrieval (CTX-1)

• Description: Contextual Query Agent applies specialized retrieval strategies to identify relevant segments in {KB_docs} and {existing_tickets}.
• Implementation:
  • Find related context through topic and entity overlap
  • Apply temporal context windows around similar historical issues
  • Map discourse relationships between symptoms and solutions
  • Use syntactic pattern matching for technical issue identification
• Assigned to: Contextual Query Agent

Task 4: Context Merger and Composition (DMC-1)

• Description: Data Merger & Context Composer combines outputs from all agents into a unified JSON context structure.
• Implementation:
  • Retrieve IT checkup snapshot from Redis
  • Merge with BM25, semantic, and contextual retrieval results
  • Apply weighted merge strategy to prioritize evidence sources
  • Structure unified JSON with fields for “System_Health”, “Multimodal_Details”, “Direct_Matches”, “Semantic_Results”, and “Contextual_Evidence”
• Assigned to: Data Merger & Context Composer Agent

Task 5: Human-In-The-Loop Review (HITL-1)

• Description: HITL Agent reviews the unified context structure for completeness, anomalies, and quality of evidence retrieval before proceeding to diagnostics.
• Implementation:
  • Inspect retrieval coverage across different technical domains
  • Check for missing evidence paths on critical issues
  • Adjust retrieval parameters or request additional checks as needed
• Assigned to: Human-In-The-Loop Agent

Task 6: Diagnostic Summarization (DS-1)

• Description: Diagnostic Summarizer Agent analyzes the merged JSON context to generate a concise diagnostic summary.
• Implementation:
  • Highlight key issue indicators from multiple retrieval paths
  • Assign confidence scores to diagnostic conclusions
  • Include remediation suggestions with evidence links
  • Flag anomalies requiring deeper investigation
• Assigned to: Diagnostic Summarizer Agent

Task 7: Ticket Response Generation (TRG-1)

• Description: Ticket Response Generator crafts a draft support ticket response using the diagnostic summary.
• Implementation:
  • Incorporate specific KB references from retrieval paths
  • Outline actionable remediation steps in clear language
  • Include confidence indicators for different solution approaches
  • Provide links to relevant KB articles based on retrieval results
• Assigned to: Ticket Response Generator Agent

Task 8: Final Human-In-The-Loop Quality Assurance (HITL-2)

• Description: HITL Agent performs final review of the diagnostic summary and ticket response, focusing on accuracy, actionability, and technical correctness.
• Implementation:
  • Verify diagnostic conclusions against system data
  • Ensure recommended actions are appropriate and safe
  • Make final adjustments to the ticket response
  • Provide feedback to improve future retrieval effectiveness
• Assigned to: Human-In-The-Loop Agent

Task 9: Retrieval Effectiveness Logging (LOG-1)

• Description: Hybrid Retrieval Agent logs detailed metrics on which retrieval strategies were most effective for different types of support queries.
• Implementation:
  • Track which retrieval method provided the decisive evidence for each issue
  • Record BM25 vs. semantic vs. contextual effectiveness comparisons
  • Log confidence scores and human intervention points
  • Store all retrieval logs in Redis via {redis_connection_details}
• Assigned to: Hybrid Retrieval Agent

Implementation Notes:

• BM25 pre-filtering is applied first for computational efficiency, followed by deeper semantic and contextual retrieval
• All vector operations use cosine similarity with properly normalized embeddings
• Related context identification uses both topic ID overlap and named entity recognition
• Discourse relationship mapping focuses on symptom-cause-solution relationships in IT contexts
• Temporal context windows are dynamically sized based on issue recurrence patterns
• The unified JSON context maintains clear provenance of all evidence paths for auditability
• Human checkpoints are strategically positioned at key decision points in the workflow

This enhanced plan integrates the robust contextual query capabilities from the Flowbots module with the original IT checkup and support diagnostics workflow, creating a more comprehensive and precise system for technical issue resolution. The implementation leverages multiple retrieval dimensions to ensure support responses are validated through diverse evidence pathways.