Forrester, a leading global research and advisory firm, recently identified a major turning point in software testing in its Autonomous Testing Platforms Landscape, Q3 2025. The report notes that testing is evolving from traditional scripted automation toward AI-augmented testing systems capable of learning, adapting, and acting under human guidance. This evolution signals the rise of agentic automation: intelligent, autonomous systems that operate within defined parameters to create, run, and optimize tests.
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.
See all the insights in the full report: The Autonomous Testing Platforms Landscape, Q3 2025.
From Automation to Intelligence
Traditional automation accelerated execution but left teams managing brittle scripts and endless maintenance. AI-augmented testing changes that dynamic. These systems:
- Learn continuously from results and application change.
- Adapt test scope and prioritization based on business risk.
- Optimize coverage while maintaining human oversight.
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.
The Three Business Values Driving This Shift
Forrester highlights three outcomes motivating investment in more intelligent testing systems:
- Accelerate Time to Value – AI-driven generation and self-healing shorten feedback loops and reduce maintenance.
- Reduce Strategic Risk – Risk-based orchestration and built-in governance connect quality metrics directly to business priorities.
- Democratize Testing – Low-code authoring and natural-language interaction let non-developers participate in quality, closing skill gaps.
Agentic automation brings these together: human-directed intent, machine-driven efficiency, and transparent oversight.
How AI-Augmented Systems Complement Human Expertise
AI in testing works best as augmentation, not replacement. By handling repetitive execution and maintenance, intelligent systems free QA professionals to focus on:
- Defining risk and coverage strategy.
- Establishing governance frameworks that maintain trust.
- Collaborating earlier with product and development teams.
Agentic automation shifts QA leadership from running tests to steering quality outcomes.
The Role of Visual and Experience Validation
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.
Building Toward AI-Augmented Readiness
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:
- Stabilizing automation foundations and addressing flakiness.
- Adopting AI-assisted detection of UI and data changes.
- Integrating experience-level validation for richer feedback.
- Connecting quality analytics to business metrics for continuous improvement.
Each step builds the trust and data maturity required for agentic automation to succeed under human orchestration.
What QA Leaders Can Do Next
Forward-looking teams are already experimenting with:
- Adaptive execution that prioritizes tests dynamically.
- Governance dashboards linking coverage, risk, and compliance.
- Visual AI that helps systems understand real user impact.
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.
Frequently Asked Questions
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.
