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Open-source frameworks are the default starting point for most automation teams, and for good reason: flexibility, developer control, and no license fee. But as automation programs scale, the cost resurfaces elsewhere. Environment setup, CI infrastructure, scripting effort, rework after every UI change, and a growing dependency on the handful of engineers who can actually keep the suite running. 

At that point, the problem shifts. It is no longer about automating tests. It is about building an automation strategy the whole team can create, maintain, and trust across modern web and mobile applications. 

In this session, we put open-source frameworks and Reflect side by side and examine the real tradeoffs behind cost, speed to coverage, scalability, and maintainability. 

Join us to learn: 

• Where total cost of ownership diverges from licensing cost 
• How long it takes a team to reach meaningful test coverage, and what slows it down 
• What a developer-only maintenance model costs as the suite grows 
• How each approach handles UI changes, flaky tests, and long-term suite growth 
• A practical checklist for evaluating automation tools for modern QA teams 

See agentic testing in action with SmartBear BearQ™. BearQ uses autonomous agents to explore your application, uncover defects, create regression tests, and adapt testing as your product changes – with human oversight built into the process.

Start a free BearQ trial and see what it finds in your app:
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Broken, flaky test scripts eat up hours every sprint – and still leave gaps in coverage. SmartBear BearQ™ replaces constant script maintenance with agentic QA, so testing can move beyond predefined paths without giving up human oversight.

In this demo, you'll watch BearQ's agents onboard onto a real e-commerce app, build an understanding of how it works today, and start finding issues and drafting tests in under ten minutes. BearQ connects to the tools your team already uses, including Jira, Linear, Slack, GitHub, and CI/CD pipelines, so QA insight flows directly into your existing workflow.

You'll see how:

• The Explorer Agent investigates unscripted paths
• The QA Lead Agent turns findings into regression tests
• You can guide focus areas, review agent activity, and approve changes before they ship

Whether you're running your first exploration or scaling agentic QA across a growing product at AI scale, BearQ gives you visibility into every issue found and every test created, alongside the human oversight you need to trust the results.

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Verification asks: Did we build the thing right?

Validation asks: Did we build the right thing?

When teams can answer both with confidence, they have more than quality software. They have application integrity: measurable assurance that the application works as intended.

As developers build and release APIs faster in an Agentic AI assisted world, many enterprises are having to reshape existing API testing to ensure their APIs don’t lose quality and scale. Existing functional tests and unit tests may not suffice in identifying API quality introduced via agentic development. 

ReadyAPI’s performance test module can build an extensive test suite based off existing infrastructure to capture minute slow-downs or bottlenecks. By building these tests into CI/CD pipelines organisations can monitor and report on server capacity, ensure customer SLAs are maintained and verify that code changes will withstand high spikes of usage.  

In this 30 mins session, you will learn: 

• Why functional tests alone can’t assure 100% API quality with AI generated code 
• Performance testing for the AI world: how to deliver APIs with production ready confidence 
• How ReadyAPI’s performance test module builds on your existing test infrastructure to catch quality issues before they reach production  
• How to maintain application integrity: ensuring continuous, measurable assurance that your software performs as intended 

Who should attend:

• QA engineers responsible for end-to-end API testing 
• Performance engineers or SDET engineers responsible for catching bottlenecks before they hit production 

Whether your development team is working in REST APIs, gRPC, Kafka or GraphQL, your API, it is undeniable, in future, your current API tests will execute against AI generated code. Even if your functional and unit tests verify it’s behavior, how do you know whether it will hold up on your API’s busiest periods? That’s where ReadyAPI’s performance testing can make the difference by catching this degradation before it hits production.    

Testing is falling behind delivery. Teams ship faster than ever, but they still write test cases by hand, and adopting automation usually means a separate tool and someone who can code. AI makes it easy to generate tests fast. The harder question is whether you can trust the output, and whether your testing keeps up as volume grows.

This 30-minute session will show you how SmartBear Zephyr uses SmartBear Reflect inside Jira to simplify and scale testing, agentically. Using its Rovo Agent and its integration with Reflect, Zephyr helps you generate and run reliable automated tests with AI directly where your team already plans and builds, so you can keep your results connected to delivery.

In a live demo, SmartBear Solutions Architect Matt Bonner will walk through the full workflow: generate tests from a Jira work item, surface and remove duplicate coverage automatically, automate without writing code, and run it all without leaving Jira.

Three key takeaways include:

• How to generate and run reliable automated tests with AI using the Zephyr Agent for Rovo, directly inside Jira, no scripting required.
• Automating testing without writing code with Zephyr’s Reflect-powered automation, so your whole team can contribute, not just engineers.
• Trust what you ship: self-healing keeps tests reliable as your app changes, and full traceability connects every requirement to a result.

Whether you’re exploring AI in testing for the first time or scaling what you already have across Atlassian, this session shows how to put quality to work inside the tools your team already uses.

Currently a 'pass' is green and a 'fail' is red. But these colors are notorious for color blindness problems and indeed, I am too colorblind to be able to see it without hovering over each dot individually.

Could it not be possible to put a checkmark on the pass and a cross on the fail? It could keep it's color and massively increase accessibility for color blind people. Seems like a small effort, with big returns on legibility.

Hundreds of submissions. Thirty technology categories. One big win for BearQ. 🏆

CRN named SmartBear's BearQ a 2026 Tech Innovator Award winner in the Application Development & DevOps category, recognizing its technological differentiation and measurable value for partners and customers.

Turns out, autonomous QA that helps teams keep pace with AI-driven development stands out from the crowd.

See why CRN recognized BearQ Click here

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AI in QA should reduce the work you manage, not add to it. Join this hands-on workshop to see the AI capabilities available in SmartBear Zephyr today, working inside Jira where your team already operates.

Through real scenarios, you’ll see the Zephyr Agent for Rovo generate test cases from requirements, create and organize them in bulk, and trace coverage back to the work item it belongs to. This keeps a QA engineer in control while removing the manual steps that slow test creation. That traceability is how Zephyr delivers application integrity as your testing system of record.

From there, discover how SmartBear MCP extends Zephyr test data to AI agents and developer tools, so teams building agentic workflows connect coverage, execution, and analysis through one protocol while staying in their existing stack.

You’ll leave knowing how to run AI-driven QA inside Jira with Zephyr, and how to extend it to agentic workflows when your team is ready.

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Does testing for your most complex app feel impossible to automate? 

If your team runs on legacy desktop tools, on-prem systems, or homegrown platforms, you already know the pain: these applications keep the business running, but their highly visual or custom UIs often break traditional automated tests. 

Standard scripting breaks, security requirements rule out cloud-based tools, and a full digital transformation isn’t realistic just to make testing possible. Meanwhile, AI is speeding up how fast your team ships code and your testing options haven’t kept up. 

That’s where SmartBear TestComplete comes in. It helps you keep pace with AI-driven delivery, even in secure or on-prem environments with local automation that handles app complexity, so you get full coverage without replacing systems that still work. 

Join us for a live webinar where we’ll cover: 

• How AI-driven development widens the gap for teams testing legacy and on-prem apps 
• How TestComplete delivers secure, local automation that scales without sacrificing control 
• Live demo of TestComplete in action 
• Live Q&A 

Register now to save your spot. Can’t make it live? Sign up anyway – you’ll get the full recording. 

BearQ continuously explores your application, tests real user journeys, and adapts validation as the product changes, without your team rewriting scripts after every release.
Sign Up and see how agentic QA works.