AgentLoop vs QualGent: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AgentLoop and QualGent — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AgentLoop
Edward Yi
AgentLoop turns a single ChatGPT plan into unattended Codex worker + independent-critic cycles that build against your local rubric until the work passes.
Key features
- Fresh Worker Per Cycle: Each build cycle spawns a clean Codex worker with fresh context so long-running loops do not accumulate stale state or memory drift.
- Independent Critic Process: A separate fresh process grades every result against your rubric so passing tests never become permission to stop looking.
- Rubric in GUIDELINES.md: Definition-of-done lives as plain Markdown in your repo and is read on every cycle, so standards persist while prompts do not.
- Evidence Carried in Files: Worker output, critic verdicts, and fixes are written to project files so the next cycle inherits the actual state of the work.
- Bounded Goal + Cycle Budget: You cap the loop with a goal.md and cycle budget so unattended runs stop at a predictable ceiling.
- MCP Status Interface: Ask ChatGPT for status through MCP so you can monitor cycles, verdicts, transcripts, and cost without opening the dashboard.
- Local-first Install: git clone the pinned v1.1.0 release and run node src/daemon.js — no npm install, no hosted workspace, MIT licensed.
Best for
- Shipping a bounded feature: Add a CSV export across UI, API, and regression suite while the critic enforces end-to-end behavior and edge cases.
- Migration work: Run an unattended migration where fresh workers apply the change and the critic verifies each step against a rubric.
- Hardening pass: Give AgentLoop a hardening goal so it iterates on defects the existing test suite misses, like malformed input handling.
- Product polish loop: Point AgentLoop at a polish goal with clear acceptance criteria and let it converge to VERDICT: PASS.
- Unattended overnight runs: Kick off a long loop, monitor cycle verdicts, and cancel from the dashboard or via MCP when the receipt looks right.
- Enforcing team standards: Codify team engineering standards in GUIDELINES.md so every worker builds against the same definition of done.
QualGent
QualGent
Mobile-native AI QA agent that autonomously tests iOS and Android apps, mimicking human testers to find bugs and scale QA instantly.
Key features
- Human-like Mobile Testing: AI agents mimic real human testers to navigate app UIs, interact with elements, and discover functional and UX bugs without hand-written test scripts.
- Cross-Platform Coverage: Supports automated testing of both iOS and Android applications, enabling consistent QA across mobile platforms.
- Always-On Execution: Agents run 24/7 to continuously exercise app flows and return results in minutes, reducing the time between code changes and test feedback.
- Massive Horizontal Scaling: Infrastructure-style scaling that allows teams to provision from a single agent up to thousands (advertised scale from 1 to 10,000 agents) to increase parallel test coverage.
- Scriptless UI Understanding: The AI interprets and reasons about app UI structure and behaviors, eliminating the need to maintain manual scripted test cases for many scenarios.
- Rapid Results and Reporting: Designed to surface issues quickly so teams can act on bugs during development cycles rather than waiting for lengthy manual test runs.
- Mobile-native AI QA agents that mimic real human testers
- Automated testing for iOS and Android apps without manual test scripts
- UI understanding to interact with app screens and workflows
- 24/7 testing with rapid results (minutes, not weeks)
- Elastic scaling of agents (advertised from 1 to 10,000 agents)
- Comprehensive testing across app features and regressions
Best for
- Pre-release Regression Testing: Run continuous automated regression suites across iOS and Android builds to catch regressions minutes after changes are merged.
- Scaling QA Coverage Without Headcount: Expand testing capacity across devices and configurations instantly without hiring additional manual QA testers.
- Shortening Release Cycles: Provide fast, always-on feedback to developers so bugs are discovered and fixed earlier, enabling more frequent releases.
- Exploratory UI Testing: Use human-like agents to explore complex UI flows and find edge-case bugs that are costly to write manual scripts for.
- Nightly or Continuous Smoke Tests: Execute rapid smoke tests around the clock to ensure core functionality remains intact between development iterations.
- High-Parallel Device Testing: Run large numbers of parallel test sessions to validate app behavior across many device models and OS versions simultaneously.
- Automated regression and functional testing for mobile apps
- Continuous integration / continuous delivery (CI/CD) mobile test automation
- Exploratory and end-to-end testing that mimics human behavior
- Scaling QA capacity during major releases without hiring testers
- Rapid pre-release sanity checks to catch critical bugs