Product
PractiTest Query Language (PTQL) – Beta
PTQL is a new query language that lets you filter and retrieve PractiTest data using a flexible, human-readable syntax via the API. PTQL enables more precise queries across tests, requirements, issues, and their relationships, helping you access exactly the data you need.
This capability is currently in beta and is available only to Corporate accounts. Contact our support team to join the beta and start using PTQL.
Read more about PTQL in our documentation.
Expanded Testing Actions with New MCP Capabilities
We’ve added new MCP tools that extend what AI assistants can do inside PractiTest, enabling deeper interaction with your testing data:
- Retrieve test data – Get full details for a specific test, including steps, description, and fields
- Sync Jira items – Pull specific Jira tickets into PractiTest as Requirements or Issues
See the full list of tools in the MCP help page.
More Control and Visibility with Global Fields
Global Fields continue to expand, giving you greater flexibility and insight across your projects:
- Use any field type as a Global Field – Standardize all field types across projects
- View linked projects per field – See all projects using a specific Global Field directly from the settings
Coming Up
PractiTest Live Training
Join our Customer Success team for a live training session and ask everything you want to know.
Europe: Wednesday, May 20, 14:00 CEST
North America: Wednesday, May 20, 2:00 PM EDT / 11:00 AM PDT
Asia-Pacific: Wednesday, May 13, 2:00 PM AEST
From Test Management to QA Intelligence – A Webinar with Joel Montvelisky
Most QA teams have more data than ever, but less clarity on what it actually means. In this session, Joel will explore why pass or fail reporting falls short and what it takes to understand real readiness, coverage, and risk when your QA data is connected. The webinar includes a live demo showing how this works in practice.
Date: Wednesday, May 13
Time: 10:00 AM EDT | 16:00 CET
PractiTest and Beyond
The Problem With Chasing Fully Autonomous QA Agents
Fully autonomous AI in testing sounds promising, but the reality is that today’s tools still lack the context and reliability to operate independently. This article explains why the real value today comes from AI as a collaborator, not a replacement, and how connecting AI to real testing context using MCP changes the game.
Regression Testing at Scale: How Large QA Teams Stay Fast
Regression testing is critical, but running everything every cycle doesn’t scale. This article explores how large QA teams stay fast by prioritizing coverage based on risk, combining automation with smart execution strategies, and maintaining lean, effective test suites.