2025 Software Testing Trends: The Year Testing Split in Two
Testing is becoming a confidence production system, not a suite
Software testing did not get replaced by AI in 2025.
It got split.
One track uses agentic AI to speed up the testing you already do. The other track introduces new testing methods because more systems now behave like agents, not deterministic apps.
One line to remember: In 2025, testing stopped being a suite. It started becoming a verification system.
Executive summary
Two things happened at the same time in 2025.
First, change got cheaper. AI lowered the cost of producing code, so teams shipped more changes, more frequently, and more in parallel. That is not just a velocity story. It is a control story. When change volume rises, the constraint shifts away from execution and toward interpretation. You can run a thousand tests. You still need to decide what those results mean, what risk you are accepting, and whether you are shipping.
Second, behavior got harder to predict. More workflows started to include agents that plan steps, retrieve context, call tools, and adapt at runtime. In these systems, failures are less likely to be “a button is broken” and more likely to be “the system took the wrong action for this user in this situation.” The old testing model still applies in parts, but it no longer covers the full risk surface.
The result is a fork in the road:
Track A: Use agentic AI to accelerate existing testing approaches.
Track B: Build evaluation methods to test agentic workflows themselves.
Most organizations adopt Track A first because it maps cleanly to today’s QA operating model and budgets. Track B is moving faster because the major platforms are productizing it inside their agent builders.
Trend 1: The testing market split into two tracks
Track A: Agentic acceleration of classic testing
This is the “do the same job with less toil” wave. It is less about inventing new testing theory and more about compressing the manual parts of testing that never scaled well: authoring, maintenance, triage, environment wrangling, and evidence packaging.
In practice, Track A shows up as:
Faster test creation from intent (requirements, flows, usage telemetry)
Lower maintenance through self-healing patterns and resilient automation
Triage automation that classifies failures and proposes next actions
Execution industrialization through managed grids, parallelism, and shared control planes
This is why “autonomous QA” became a credible category. Not because organizations suddenly love AI. Because maintenance costs and bottlenecks become impossible to hide under higher change volume.
Track A is the fastest path to productivity gains. It is also the easiest place to overclaim. A tool can generate tests. The hard part is keeping them stable, keeping them relevant, and keeping their results interpretable in the release window.
Proof points (2025)
Functionize, autonomous QA funding push: https://www.functionize.com/blog/functionize-raises-41m-series-b-to-accelerate-the-future-of-autonomous-quality-assurance
Continuous testing as infra: https://testkube.io/blog/announcing-testkube-8m-series-a
Verification layer framing: https://momentic.ai/blog/series-a
Track B: New methods to test agentic workflows
Track B starts with a different assumption: the oracle changed.
If your system is an agent, expected results are not always a single deterministic answer. Correctness can depend on what context was retrieved, what tool was selected, what order actions were executed in, and whether safety rules were followed.
So Track B looks less like test automation and more like evaluation engineering:
Build datasets of scenarios that represent real user intents and risk conditions
Create rubrics and graders that formalize what “good” looks like
Score traces and trajectories to verify behavior, not just outputs
Run user simulation to stress the system under variance and ambiguity
Monitor production sampling to detect drift after release
If your organization is shipping agents without evaluation assets, you are not testing. You are demoing.
Proof points (2025)
Trajectory evaluation: https://cloud.google.com/blog/products/ai-machine-learning/introducing-agent-evaluation-in-vertex-ai-gen-ai-evaluation-service
User simulation harness: https://developers.googleblog.com/announcing-user-simulation-in-adk-evaluation/
Eval inside the builder: https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/build-smarter-test-smarter-agent-evaluation-in-microsoft-copilot-studio/
Runtime eval plus controls: https://aws.amazon.com/about-aws/whats-new/2025/12/amazon-bedrock-agentcore-policy-evaluations-preview/
Datasets and trace grading: https://openai.com/index/introducing-agentkit/
Trend 2: Managed execution is becoming table stakes
A quiet but important 2025 shift is that the hardest-to-operate parts of testing are being packaged as managed services by cloud vendors. This changes the economics of the testing tool market, and it changes the operating model for QA teams.
When browsers and devices can be provisioned and parallelized as a managed capability, the strategic question becomes simple: why are we spending scarce engineering time building and maintaining a grid.
This does not mean execution is solved. It means it is being commoditized. The differentiation moves up-stack toward:
Test selection and coverage statements
Failure classification and root cause acceleration
Decision-grade evidence packs, not dashboards
Traceability from change to coverage to outcome
Proof points (2025)
Microsoft Playwright Workspaces overview: https://learn.microsoft.com/en-us/azure/app-testing/playwright-workspaces/overview-what-is-microsoft-playwright-workspaces
Azure App Testing and Playwright Workspaces: https://techcommunity.microsoft.com/blog/appsonazureblog/azure-app-testing-playwright-workspaces-for-local-to-cloud-test-runs/4442711
AWS Device Farm managed Appium endpoint: https://aws.amazon.com/about-aws/whats-new/2025/11/aws-device-farm-managed-appium-endpoint/
Trend 3: Testing moved upstream into pre-merge quality gates
As code output accelerates, quality has two choices: move earlier, or become an expensive lagging indicator.
In 2025, more attention moved to pre-merge controls: AI-assisted code review, automated PR checks, and policy-based gates that aim to reduce defect injection before runtime testing is even involved.
This is not a “testing replaces review” story. It is a “review becomes programmable” story. If you can codify what good looks like at the change level, you avoid paying for avoidable defects downstream.
This expands the definition of testing. Your quality system is no longer only a pipeline stage. It increasingly includes the controls that shape what gets merged in the first place.
Proof points (2025)
GitHub Copilot coding agent: https://github.com/newsroom/press-releases/coding-agent-for-github-copilot
CodeRabbit Series B, “quality gates for AI-powered coding”: https://www.coderabbit.ai/blog/coderabbit-series-b-60-million-quality-gates-for-code-reviews
Trend 4: Evaluation engineering emerged as a real testing discipline
A decade ago, test automation changed the discipline by shifting effort from manual execution to scripted verification.
In 2025, evaluation engineering began shifting effort again, from scripted verification to measured behavior.
Evaluation engineering mirrors familiar patterns:
Test case design becomes scenario design
Oracles become rubrics
Pass or fail becomes scored outcomes against criteria
Regression becomes dataset replay
Reliability becomes monitoring plus drift detection
This is where 2026 maturity will be decided. Teams that build evaluation assets with the same rigor as test assets will ship agents with less surprise.
There is also a new complication: your judge can be wrong. If you use an LLM as a judge, you need calibration, consistency checks, and rubric hardening. That will become as normal as test flake management.
Proof points (2025)
Copilot Studio Agent Evaluation: https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/build-smarter-test-smarter-agent-evaluation-in-microsoft-copilot-studio/
Vertex AI agent evaluation: https://cloud.google.com/blog/products/ai-machine-learning/introducing-agent-evaluation-in-vertex-ai-gen-ai-evaluation-service
Bedrock AgentCore evaluations: https://aws.amazon.com/about-aws/whats-new/2025/12/amazon-bedrock-agentcore-policy-evaluations-preview/
Trend 5: Simulation became mainstream for testing agents
Classic failures often live at the edges of UI and API contracts. Agent failures often live in the middle: intent interpretation, tool choice, step sequencing, and safety behavior under ambiguity.
That is why simulation is becoming standard. A simulated user lets you test the behavior envelope:
Does the agent get stuck or loop
Does it call the wrong tool
Does it take unsafe actions
Does it fail gracefully and escalate when it should
Simulation is becoming the agent equivalent of performance testing. You are not only testing one path. You are testing stability under variance.
Proof points (2025)
Coval simulation framing (TechCrunch): https://techcrunch.com/2025/01/23/coval-evaluates-ai-voice-and-chat-agents-like-self-driving-cars/
Bluejay seed round coverage: https://www.businesswire.com/news/home/20250828002083/en/Bluejay-Raises-4M-Seed-to-Help-Build-Reliable-AI-Agents
Trend 6: Observability converged with testing
For classic systems, testing is a gate.
For agentic systems, testing becomes a control loop. You need pre-release evaluation, but you also need post-release evidence that behavior remains stable as prompts, tools, models, and context sources evolve.
In practice, the loop becomes:
Define scenarios and rubrics
Run evaluations before release
Sample real production interactions
Score them with the same graders
Detect drift and regressions
Feed failures back into the dataset
This changes the QA org’s responsibilities. Quality is no longer only readiness. It becomes ongoing behavioral reliability.
Proof points (2025)
HoneyHive launch and funding: https://www.prnewswire.com/news-releases/honeyhive-a-leadingai-agent-observability-and-evaluation-platform-announces-launch-and-7-4m-in-total-funding-led-by-insight-partners-302419249.html
Cekura funding post: https://www.cekura.ai/blogs/fundraise
Trend 7: Multi-agent “mission control” created a new testing problem
As platforms moved from single agents to multi-agent orchestration, new failure modes emerged that look more like distributed systems problems.
The defect is often not within one agent. It is in the handoff:
The wrong agent gets the task
Context gets lost between steps
Two agents produce conflicting outputs
Retry loops create runaway behavior
Fallback paths do not trigger when needed
This introduces a new test category:
Collaboration tests that validate correct delegation
Contract tests between agents that validate artifact formats and assumptions
Orchestration policy tests that validate routing rules, priorities, and escalation paths
Trace-based debugging as a default operating mode
If you are adopting multi-agent architectures, orchestration becomes part of the system under test.
Proof points (2025)
Copilot Studio multi-agent orchestration (Build 2025): https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/multi-agent-orchestration-maker-controls-and-more-microsoft-copilot-studio-announcements-at-microsoft-build-2025/
GitHub Agent HQ: https://github.blog/news-insights/company-news/welcome-home-agents/
Market signals: where money flowed in 2025
This is not a directory. It is a signal that the market is funding both speed and control.
Track A: Agentic AI to speed up existing testing methods
These bets assume the enterprise problem is still classic QA, but the manual work and maintenance costs do not scale with AI-driven change volume.
Functionize, autonomous QA: https://www.functionize.com/blog/functionize-raises-41m-series-b-to-accelerate-the-future-of-autonomous-quality-assurance
Testkube, continuous testing infra: https://testkube.io/blog/announcing-testkube-8m-series-a
Momentic, verification layer framing: https://momentic.ai/blog/series-a
CodeRabbit, PR quality gates: https://www.coderabbit.ai/blog/coderabbit-series-b-60-million-quality-gates-for-code-reviews
Track B: New methods to test agentic workflows
These bets assume agents are becoming production-critical, and testing must become evaluation plus monitoring.
HoneyHive, evals plus observability: https://www.prnewswire.com/news-releases/honeyhive-a-leadingai-agent-observability-and-evaluation-platform-announces-launch-and-7-4m-in-total-funding-led-by-insight-partners-302419249.html
Coval, simulation for agents: https://techcrunch.com/2025/01/23/coval-evaluates-ai-voice-and-chat-agents-like-self-driving-cars/
Cekura, QA plus observability: https://www.cekura.ai/blogs/fundraise
The takeaway: the market is funding both speed and control. Speed is Track A. Control is Track B. Your 2026 testing strategy needs both.
What QE leaders should do next
1) Adopt a two-track testing strategy
Run Track A and Track B as separate programs, with separate artifacts and KPIs. Track A reduces toil. Track B reduces behavioral surprise.
If you treat them as one initiative, you will measure the wrong outcomes. You will optimize for test count and execution speed when the real risk is behavior quality and drift.
2) Build evaluation assets like you build test assets
Start building durable assets that survive tool changes: datasets, rubrics, scenario libraries, and trace schemas. Treat them like IP. Version them. Review them. Reuse them.
3) Assume execution is commoditizing
Use managed execution where it makes sense. Then invest differentiation budget into intelligence: test selection, evidence packaging, triage correctness, and decision automation.
4) Add coordination testing to your scope
If your organization is building agent teams, add collaboration, handoff, and orchestration testing as first-class scope.
5) Treat production as part of the quality loop
For agents, quality is monitored behavior. Build a practical control loop that uses the same rubrics before and after release.
The takeaway
2025 did not make testing irrelevant.
It raised the bar.
The winners will not be the teams with the biggest suite. They will be the teams with the best verification system: the ability to produce a clear verdict, with evidence, at the speed that change arrives.
A simple self-check for 2026:
Are you scaling test execution, or are you scaling confidence production.