Urjasoft - Software, AI & SaaS Engineering Urjasoft - Software, AI & SaaS Engineering
Prototype ID: LAB-05 Category: Autonomous Agents & LLMs

Support Intelligence Hub & Autonomous Triage

Prototype testing semantic ticket clustering and assistive agent draft synthesis.

An internal prototype investigating whether semantic intent matching against engineering runbooks can prepare accurate draft responses for support operators without autonomous bot replies.

The Architectural Problem

Support engineers spend significant time manually tagging, routing, and copy-pasting routine troubleshooting steps across repetitive tier-1 tickets.

Working Hypothesis

Semantic intent matching against validated troubleshooting documentation can produce accurate zero-shot resolution drafts for human operator review.

System Topology

Prototype Architecture & Data Pipeline

Multi-stage execution model separating probabilistic reasoning from deterministic state mutations.

1

01. Ticket Ingestion

Inquiry subject and description normalized and stripped of sensitive data.

Ingestion
2

02. Intent Triage

Classification model suggests severity level and routing category.

Classification
3

03. Runbook Search

Semantic similarity search identifies relevant engineering runbook entries.

Knowledge Retrieval
4

04. Draft Synthesis

Synthesizes internal draft response citing source documentation for operator review.

Agent Copilot
Data Boundary Rule: No non-deterministic agent loop is permitted to execute writes directly to transactional databases without an intermediate policy gatekeeper.
Implementation Methodology

How We Engineered the Prototype

1. Ticket Ingestion: Support inquiry text normalized upon arrival.
2. Triage Classification: Intent classifier suggests category and urgency tag.
3. Runbook Matching: Embeddings retrieve relevant troubleshooting steps from verified runbooks.
4. Draft Synthesis: Synthesizes draft response in agent UI with documentation citations.
Current Working Capabilities
  • ✓ [Implemented] Automated intent classification and category routing on incoming ticket text
  • ✓ [Implemented] Assistive response draft generation citing internal runbook entries
  • ✓ [Planned] Direct webhook integration into commercial ticketing software
Known Limitations & Unsolved Cases
  • × Draft responses must always be reviewed by a human operator before dispatch (Safety Mandate)
  • × Ambiguous customer inquiries with insufficient context require human follow-up
Engineering Takeaways & Architectural Findings

“Design Expectation: Presenting model outputs as assistive internal drafts for human agents avoids the user frustration and trust loss common to autonomous customer-facing chatbots.”

Experiment Parameters
Maturity Status
Prototype
Research Discipline
Autonomous Agents & LLMs
Experiment Identifier
LAB-05
Tested Technologies
Laravel Python OpenAI API MySQL
Related Commercial Capability
SaaS Product Engineering

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