How Does AI Change Development and Quality Assurance? The Top Software & Testing Trends for 2026

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Tricentis GmbH

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In 2026, risk-based testing, context engineering, and the controlled use of AI agents come into focus. What are the potentials, limitations, and practical approaches for development and testing teams?

Artificial intelligence will become the dominant theme in software development and quality assurance in 2026. After companies have gained initial experience, they are now looking for ways to achieve real cost and productivity benefits. Where are the greatest potentials? And why does human oversight remain indispensable?

1. Risk-Based Testing Replaces Broad-Based Testing

The increasing use of AI has rapidly changed the pace of software development. Updates are no longer rolled out quarterly but weekly or even daily. Code is often AI-generated and deployed faster than QA teams can review it.

At the same time, the number of failed integrations is rising. As quality declines and release rollbacks increase, many companies are realizing that traditional testing strategies can no longer keep up with the dynamics of the AI era. In 2026, this pressure will lead to a fundamental transformation: the focus will shift from a broad, manually driven approach to AI-supported, risk-based testing. Traditionally, quality assurance aims for 90 to 95 percent test coverage.

However, this strategy is not only too slow and labor-intensive but also risky. What if the greatest risks lie precisely in the remaining five to ten percent? AI, on the other hand, can automatically analyze which software components are affected by a change, what dependencies exist, and what impact an update has. This allows QA teams to specifically test the areas that truly need validation.

2. Context Engineering Becomes A Success Factor

Many companies are still struggling to derive true business value from artificial intelligence. While there are numerous pilot projects and showcases, a measurable ROI is often lacking.

One of the reasons is that companies use generic LLMs, which possess broad knowledge but are not experts in any specific field. When it comes to solving specialized and business-critical challenges, such models fail.

2026 marks a turning point: companies are increasingly developing tailored AI applications. Context engineering becomes a critical success factor in this process. Specifically, it involves equipping AI applications with company-specific knowledge, processes, and data to provide them with the necessary context for a task. This combination of powerful models and deep, structured expertise creates solutions that act as true growth drivers.

3. AI Requires Human Guidance And Control

In the past two years, AI has been like a talented teenager: fast and impressive, but overly confident and prone to errors. This has been particularly evident with so-called vibe coding, where the results should be approached with caution. Developers can generate code very quickly and easily by giving an LLM instructions in natural language.

However, in practice, much of this does not work correctly or is simply wrong. Despite all the excitement about what AI can achieve, we must not let it run unchecked. It is crucial to guide the technology properly, oversee it during tasks, and evaluate the results with sound judgment. Similar to guiding a teenager, ensuring they do not rush ahead faster than their abilities allow.

For example, it is helpful to break tasks into smaller steps, provide feedback, or ask the AI to explain its approach. Human oversight becomes indispensable with AI agents, as they are designed to autonomously plan and act. Only by carefully guiding and continuously monitoring these virtual assistants can we use them safely.

4. MCP Connects AI Agents to Workflows

The future does not lie in providing developers and QA teams with more and more tools. Instead, it is about connecting tools more intelligently and optimizing workflows.

AI agents and AI-driven orchestration layers play a central role in this. With their help, employees no longer have to navigate complex user interfaces but simply state the goal they want to achieve. The AI will then call up the right tools and execute the tasks. In 2026, AI agents will increasingly be linked into complete workflows.

This is made possible by the Model Context Protocol (MCP). Through this standardized interface, AI agents can communicate with one another without requiring individual integration. This enables quick and simple interoperability. In a QA workflow, connected AI agents can, for example, automatically generate test cases from Jira tickets, provide the appropriate test data, perform the testing, and prepare the results in a management-friendly format.

Conclusion: In 2026, AI Agents Will Mature

Companies have realized that speed only brings cost and productivity advantages if it does not come at the expense of quality. The challenge now is to put this insight into practice. Roman Zednik, Field CTO at Tricentis, summarizes: "Companies need quality assurance processes that can keep pace with the new development dynamics. Risk-based testing and the linking of AI agents into workflows are just as important as the ability to test AI-generated code and provide relevant context. One thing is clear: AI cannot replace human employees in quality assurance. We should rather view the technology as a partner that supports us when we guide and oversee it properly." (sg)

Roman Zednik is Field CTO at Tricentis

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