Update & TL;DR
This post was written while Forrester’s research on agentic and autonomous testing was still emerging. Since publication, Applitools has been included in The Forrester Wave™: Autonomous Testing Platforms, Q4 2025. The perspective outlined below reflects how this shift has since been validated and formalized by independent industry analysts.
• Agentic automation shifts testing from brittle, script-driven execution to intelligent systems that adapt based on change, risk, and context.
• AI augments human intent rather than replacing QA teams, enabling people to focus on quality strategy, governance, and risk decisions.
• This model is increasingly shaping how autonomous testing platforms are evaluated in the market.
Forrester, a leading global research and advisory firm, identified a major turning point in software testing in its Autonomous Testing Platforms Landscape, Q3 2025. The research describes a shift from traditional scripted automation to AI-augmented systems that can learn, adapt, and act under human guidance. This shift signals the rise of agentic automation: intelligent systems that create, run, and optimize tests within defined boundaries.
As delivery cycles compress and complexity grows, quality and engineering leaders are redefining what effective testing means in practice. Agentic automation bridges human intent with machine-driven precision—transforming testing from a reactive maintenance task into a proactive engine for reliability, speed, and continuous improvement.
Traditional automation accelerated execution but left teams managing brittle scripts and endless maintenance. AI-augmented testing changes that dynamic. These systems:
The result is testing that behaves less like a checklist and more like a self-improving quality partner, one that scales reliability across every release.
Forrester highlights three outcomes motivating investment in more intelligent testing systems:
Agentic automation brings these together: human-directed intent, machine-driven efficiency, and transparent oversight.
AI in testing works best as augmentation, not replacement. By handling repetitive execution and maintenance, intelligent systems free QA professionals to focus on:
Agentic automation shifts QA leadership from running tests to steering quality outcomes.
Intelligent automation depends on reliable validation signals. Traditional assertions can’t always capture what matters to real users: layout, accessibility, and experience consistency.
Visual and experience validation fill that gap, giving AI-augmented systems context they can trust. When machines validate what users actually experience, teams gain both speed and confidence—without rigid pixel-level comparison.
Forrester describes this as a maturing market: organizations are blending traditional automation with AI capabilities to move toward greater autonomy over time. QA leaders can start by:
Each step builds the trust and data maturity required for agentic automation to succeed under human orchestration. As adoption increases, these maturity steps align with how leaders in the market are being evaluated on autonomous capabilities.
Forward-looking teams are already experimenting with:
The goal isn’t full autonomy—it’s AI-augmented confidence: testing that’s faster, smarter, and more inclusive across roles. Read the full report now.
Agentic automation refers to AI-augmented systems that can learn, adapt, and act within human-defined boundaries to create, run, and optimize tests. Instead of simply executing scripts, these systems continuously improve based on feedback and business context.
By using self-healing and adaptive test generation, AI-augmented testing identifies and fixes broken tests automatically. It also adjusts coverage based on application changes and risk, minimizing the need for manual upkeep.
The Forrester research identifies three key outcomes: faster time to value through automation and learning; reduced strategic risk through governance and risk-based prioritization; and democratized testing through natural-language and low-code interfaces.
AI systems handle repetitive execution and maintenance so human experts can focus on strategy—defining risk models, shaping governance, and collaborating earlier in the delivery process. This partnership amplifies QA’s influence across engineering.
Visual and experience validation let AI systems measure what users actually see and feel—not just code-level outputs. This gives machine-driven tests the contextual awareness to evaluate accessibility, layout, and experience consistency accurately.
