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Autonomous AI Agents for Automated Gameplay Testing & Performance Validation

Autonomous AI Agents for Automated Gameplay Testing & Performance Validation

A mid-sized gaming studio based in Helsinki, Finland, developed multiple mobile and PC titles with rapid release cycles. Manual QA teams spent hundreds of hours navigating levels, detecting glitches, validating FPS stability, and checking edge-case behaviors across characters and maps. To scale testing and accelerate releases, the studio partnered with our engineering team to build autonomous, multi-capability AI agents that could explore environments, trigger scenarios, report defects, and benchmark performance with minimal human input.

Image representing case study

55%

Reduction in full regression cycle time

92%

Reproducibility for agent-detected bugs

24X

More gameplay hours tested per week

About  

Problem Statement

  • Manual exploratory testing was slow, inconsistent, and difficult to scale.
  • Edge-case bugs appeared during random gameplay sequences that were hard to reproduce manually.
  • Performance metrics (FPS, CPU/GPU load, memory spikes) required continuous monitoring.
  • Regression testing across new maps, characters, and inventory updates consumed excessive QA effort.
  • No automated system existed to validate in-game rendering fidelity or physics accuracy.

Industry: Gaming & Interactive Entertainment

Services: Agentic AI Development, Autonomous Testing, AI-Driven QA

Region: Finland

Our Tech Stack

Tech stack we used

Solution Approach

  • Created behavior policies using PPO reinforcement learning to navigate dynamic maps, interact with objects, and trigger diverse quests.
  • Connected Airtest + Poco for direct interaction with UI elements, environmental objects, and inventory actions.
  • Implemented OpenCV pipelines to detect missing textures, clipping issues, broken animations, and UI overlaps.
  • Integrated with Unity ML-Agents to generate training replicas of game environments for safe RL exploration.
  • Set up Grafana + InfluxDB telemetry to measure FPS dips, rendering delays, GPU spikes, and physics timing variance.
  • Automated bug report generation that included screenshots, stack traces, and sequence actions leading to the issue.
  • Containerized the entire testing pipeline using Docker, enabling dozens of parallel autonomous test runs.
  • Added scenario replay logic so agents could reproduce recurrent bugs with >92% accuracy.

Benefits

  • Reduced test cycle duration by 55%, enabling faster release cadences.
  • Autonomous agents discovered deep edge-case issues missed by manual QA.
  • Provided continuous gameplay monitoring across new builds and patches.
  • Generated reproducible bug sequences and detailed performance insights.
  • Increased QA coverage across maps, characters, and item combinations without human scaling.

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