Agentic QA Assistant
On-premises LLM system for test result analysis, report interpretation, and QA decision support
Overview
Quality engineering involves repetitive cognitive work — parsing logs, comparing test runs, writing reports. This project designed a locally-hosted LLM assistant that analyses results, interprets performance data, suggests investigation paths, and supports day-to-day QA decisions. All inference runs on-premises via Ollama — no data leaves the network.
Challenge
A significant share of QA effort goes to predictable, pattern-based tasks — log review, baseline comparison, report formatting.
Performance report interpretation requires domain context that is difficult to transfer to new team members.
Cloud-based AI tools raised data privacy concerns when processing internal test data and system information.
Generic AI assistants lack the domain knowledge needed to provide useful QA guidance without extensive prompting.
Approach
Designed an agent architecture using Ollama for local LLM inference, ensuring all data stays on-premises and no internal information is sent to external services.
Built specialized prompt templates for common QA tasks: test failure analysis, performance comparison, regression investigation, and report generation.
Implemented an orchestration layer that can chain multiple analysis steps — e.g., parse results → compare to baseline → generate summary → suggest next actions.
Created a knowledge base of QA patterns, common failure modes, and performance benchmarks that the agent can reference during analysis.
Designed the system for extensibility — new analysis capabilities can be added as modular prompt chains without restructuring the core architecture.
Technology Stack
AI / LLM
Architecture
QA Domain
Integration
Outcomes
Demonstrated practical local AI integration for QA without cloud dependency or data privacy concerns.
Reduced time spent on routine result analysis and report interpretation.
Created a reusable framework for building domain-specific QA agents adaptable to different project contexts.
Established patterns for human-AI collaboration in quality engineering — augmenting judgment, not replacing it.
Summary
AI in QA works best when it accelerates analysis rather than replacing judgment. A locally-hosted, domain-aware assistant reduced repetitive cognitive work while keeping all data under team control.