AI is no longer just a support function in testing – it’s quickly becoming the core. Jani Haapala, Service Owner, AI in QA at Gofore, explains how AI agent farms bring a new mindset to quality assurance, speeding up testing, improving quality, and reshaping the tester’s role from the ground up.
What exactly is an AI agent farm? And no, we’re not talking about sheep.
The term “AI agent farm” might sound exotic, but it simply refers to intelligent automation powered by artificial intelligence. Compared to traditional test automation, the key difference is that test scenarios can now be described in natural language, and the agents independently solve problems based on that input.
In this context, a “farm” refers to a group of agents that communicate with each other and work collaboratively on a shared task. These agents are not built to last – they are single-use, purpose-built tools designed for a specific test. More livestock than pets, you might say.
Where are we now – and why isn’t everyone doing this yet?
Many organizations are still in the experimental phase. AI-powered solutions clearly offer benefits, but building an agent farm remains a demanding task and requires a clear understanding of the problem to be solved.
Planning is key. It’s important to determine whether the problem at hand is something AI is even capable of solving.
Practical benefits: speed, quality, and broader insight
The biggest benefit of AI agents isn’t just speed – it’s their ability to tackle complex testing challenges one microtask at a time.
When agents solve smaller parts of the problem using well-targeted and high-quality data, the results are more accurate and valuable. Quality is created through the sum of multiple clearly scoped sub-tasks.
Additionally, AI can extend the scope of testing by suggesting test contexts a human might never have thought of – bringing more creativity and a broader perspective to the process.
6 ways AI agent farms are transforming testing
- Speed: Agents work in parallel and handle small microtasks – which speeds up the entire testing process.
- Coverage: AI can suggest test targets that might otherwise go unnoticed – including rare edge cases.
- Quality: Agents gain access to the right data at the right time – reducing hallucinations and improving accuracy by grounding results in the most relevant input.
- Roles: Testers move toward guiding, prompting, and orchestrating AI agents instead of manual execution.
- Data utilization: AI can process large volumes of data effectively – but also raises the bar for data quality.
- Predictability: Reporting becomes proactive instead of reactive – AI helps identify what should be tested next.
Data quality makes or breaks it – and AI can help clean it up
One of the biggest challenges is data quality. Many assume their organization’s data is in good shape – but in reality, it’s often “a bit of this and that.” AI works best when it has access to clean, machine-readable data.
Ironically, AI can also help clean that data. But this creates a new challenge: the volume of AI-generated content is growing, while the amount of unique human-generated data is declining. Going forward, we need new ways to identify which data can truly be trusted.
Testing roles are evolving – prompt engineering takes the spotlight
Using AI agents requires a new mindset from QA professionals. Completely new skills are needed. One must understand the problem, be able to translate it into effective prompts, and guide agents to carry out the task. It’s all about combining hands-on testing knowledge with AI-driven tooling.
Customer projects underway – and eyes on the future
At Gofore, we’ve already delivered projects where AI agent farms are part of daily testing. These setups always involve multiple agents working together. When the “Lego blocks” are built right, they can be reused for different needs.
For example, AI agents have already been used to test a telecom company’s e-commerce platform and a manufacturing company’s machine control logic. For such cases, Gofore has developed the Testing Genius service, which enables AI-powered testing as a continuous, scalable service. We are actively seeking new pioneer customers who want to unlock the full benefits of AI testing.
In the future, agent-to-agent communication will be standardized. Once agents can “speak the same language,” solutions will scale faster – unlocking major new opportunities and making testing more efficient and manageable.
Business value – and a smoother experience for the end user
AI agent farms give companies a competitive edge by reducing costs related to bugs and downtime. Faster and more accurate testing enables earlier releases to market, improving customer satisfaction, increasing revenue, and strengthening the brand’s reputation for quality.
From the end user’s perspective, AI-tested services are more stable and smooth from the very first use. Smart agents ensure even rare usage scenarios are tested and accounted for – meaning fewer bugs, fewer interruptions, and a more reliable overall experience.
Testing is reputation-critical work – and AI brings it the attention it deserves
Testing is still too often underestimated. It’s seen as a cost center – even though it’s business-critical, and sometimes even reputation-critical. AI offers an opportunity to elevate testing through efficiency and predictability.
Want to explore how an AI agent farm could work for your organisation?
Let’s talk – we’ll help you build a scalable, business-driven AI-powered testing solution that delivers real value.
