How BugHunter Works
From build upload to reproducible bug report. AI agents play your game, learn its logic, and show what breaks.
Built for live-ops and mid-core game teams that need faster QA cycles and clearer release confidence.
How It Works
Step-by-step workflow
How It Works
#01 SETUP PROJECT
- •A short overview of your game's core mechanics and genre
- •Documentation - GDD, technical specs, or any relevant documentation to help train the AI agent
- •Game build
The goal is not heavy setup. The goal is to give the agent enough context to understand what the game is, what matters, and where to begin.
How It Works
#02 AI PLAYS & LEARNS
Our AI
- •runs the game
- •explores mechanics
- •understands objectives
- •and builds a gameplay model - just like a human tester would on first playthrough.
This is where BugHunter moves beyond scripted QA. It begins to understand how the game behaves, not just how a pre-written test expects it to behave.
How It Works
#03 SETUP TEST CASES
- •Write tests in plain English - no code, no scripting.
- •Define the starting state and what "success" looks like. BugHunter turns that into executable steps.
- •Already have QA docs? Import them and run.
BugHunter turns human-readable intent into executable testing logic.
How It Works
#04 GET REPORT
- •Reproduction steps in plain English
- •Video evidence showing the bug occurring
- •Severity rating (critical/high/medium/low)
- •Confidence score indicating certainty
- •Game state context (level, progression, conditions)
The output is designed for decision-making, not just detection.
See BugHunter in action
Watch the complete product flow: from build interaction to bug detection and reproducible reporting.
Full demo showing setup, AI gameplay, test case creation, and bug reporting workflow.
3 play modes
Together, these modes let teams move from scripted QA to agent-based coverage.
Scout
Plays like a first-time player, figures out the rules, and turns that into usable docs.
Deterministic test cases
Runs the exact same steps every time - clean, repeatable regression checks.
Free play
You give a goal ("play 100 levels") - it just plays for hours and reports what breaks.
Why BugHunter gets more useful over time
BugHunter works with game-specific context and gameplay signals: logs, progression states, event signals, test outcomes, and repeated run patterns.
Every run improves context, coverage, and reporting quality.
What studios get from this workflow
Faster regression cycles
Reduce time spent on repetitive manual testing
More bug coverage per build
AI explores paths humans might miss
Clearer release confidence
Know what works before you ship
Less manual setup
No brittle scripts to maintain
Frequently asked questions
See how BugHunter would test your game
Bring a build. We will show how autonomous gameplay testing can fit your QA and release workflow.