Spec2TestAI, answered.

Spec2TestAI is an AI assistant for test engineering that helps teams turn product requirements, user stories, and acceptance criteria into clear, traceable test coverage — so stakeholders get higher release confidence with less QA friction.

Because Spec2TestAI helps teams reduce QA cost, speed up delivery, and catch more defects before release — significantly improving quality and reducing business risk.

It reduces time spent on:

• manual test design
• duplicate test review
• coverage analysis
• test maintenance

And it helps you focus automation efforts only where they matter most — avoiding wasted engineering time.

Teams can expect:

• faster validation cycles
• fewer defects escaping to production
• clearer, more consistent acceptance testing

That means less confusion, fewer requirement disputes, less rework, and more confidence at release time.

Unlike generic AI chat tools, Spec2TestAI is built for testing workflows. It focuses on translating business intent into test-first clarity, helping teams identify:

• requirement gaps
• ambiguities
• high-risk scenarios

Because it supports the full software development lifecycle with strong emphasis on requirements quality — not just running tests. It helps teams:

• improve decision-making from requirements
• strengthen coverage quality
• create traceable, execution-ready tests and coding-tool prompts

It can also generate AI coding prompts directly from the requirements to keep the work connected from requirements → development → test creation.

Faster releases with higher confidence, leading to:

• lower QA cost
• fewer production defects
• reduced support burden
• improved customer experience

Teams face delivery pressure with limited resources. Spec2TestAI provides a practical way to improve test quality and release confidence without headcount growth.

Step 1 focuses on capturing and structuring initial user requirements and breaking work into epics, features, and user stories — so product intent becomes test-ready inputs.

Use the format:

As a [user], I want [feature/action] so that [benefit/outcome]

This helps align stories with INVEST, REAL, COMPLETE, and Business principles and supports clearer acceptance testing.

Yes. Step 1 provides:

• user story templates
• acceptance criteria guidance
• document analysis to extract requirements and support structured story creation

A good story is Independent, Negotiable, Valuable, Estimable, Small, and Testable, with acceptance criteria that leave less room for interpretation during QA.

Yes. You can use $import with a Jira key (for example, $import PROJ-123) to bring stories into Spec2TestAI for refinement.

Use the help command anytime to get guidance for Step 1 and related workflows.

Step 2 enhances a user story into a more testable Enhanced User Story so it's ready for build and QA, and remains aligned with prior product decisions.

No. It keeps your original story intact and creates an enhanced refinement for review and approval.

It uses:

• the original story and acceptance criteria
• stakeholder Q&A
• project learnings (prior decisions)
• optional UI/code evidence

to surface ambiguity, gaps, or inconsistencies that could cause delays later.

It turns vague requirements into explicit, testable rules, including:

• validation behavior
• time zone / date handling
• sorting and tie-breakers
• error and empty-state behavior

If details are missing, it flags items as "Clarification Needed or Potential Defects."

• Enhanced User Story + Enhanced Acceptance Criteria
• prioritized clarification questions
• a requirements ↔ questions table
• transparency artifacts for quality and risk (e.g., defect summaries, INVEST/SMART)

Common high-impact decisions include:

• exact error / empty-state copy
• validation rules
• sorting logic and tie-breakers
• basis for time zones
• connecting-flight representation
• retry vs. empty-state behavior for network / provider failures

It uses existing approved decisions (Project Learnings). When a decision isn't available, it asks questions rather than guessing — helping teams avoid costly misalignment.

By making outcomes deterministic and acceptance criteria precise, QA testing becomes consistent, predictable, and less likely to uncover late surprises.

Not necessarily. Many backlog items start high-level. Step 2 helps surface what must be clarified to prevent:

• rework
• acceptance disputes
• delayed delivery

Step 2 clearly separates original vs. enhanced content, and links enhancements back to:

• original acceptance criteria
• stakeholder Q&A
• project learnings
• UI/code evidence

Step 3 converts story requirements into test assets such as test cases, edge cases, coverage notes, and traceability outputs — so teams know what to test and why.

Key outputs include:

• test cases
• security test cases
• edge and boundary cases
• coverage analysis
• traceability mapping
• optional usability / UAT test cases

Because it helps teams:

• reduce coverage gaps
• prepare for execution and/or automation faster
• improve QA readiness earlier in the cycle

They're typically a strong starting point and should be reviewed for clarity and completeness — and they accelerate stakeholder review and reduce missed scenarios.

They're structured, prioritized, and deterministic — making them easier to review, execute, and automate.

Yes. Outputs can usually be saved, exported, imported, and reused for automation and test-management workflows.

Yes. Spec2RunAI is the test executor inside Spec2TestAI and is sold on its own for teams that want plain-English automated execution without adopting the full platform. It runs existing manual test cases and new plain-English scenarios against web applications, with no automation scripts or hard-coded selectors to maintain, and produces a Run Evidence Report for every run.

Yes. That is the design center. The test case runs as the tester wrote it: no required keywords, no reformatting into a command vocabulary, business jargon and multi-action lines handled as written. Ambiguous steps are flagged and a clearer wording is offered for one-click acceptance; the stored test changes only when a person approves it.

No. There is no script in the worker and no generated code to maintain. An AI agent reads each plain-English step at run time, resolves it against the live page, acts, and verifies the expected result. Proven steps replay from selector memory afterward without a model call. The Spec2TestAI platform can export BDD and script files for other purposes, but execution does not depend on them.

No. Autonomous Tuning may reword how a failed step is performed against the live screen; it never changes the expected result. That rule is Assertion Lock, and every Run Evidence Report proves it held: each expected result appears exactly as it was evaluated, each test case carries a SHA-256 fingerprint taken before step 1, and every rewrite is shown beside the original wording. A genuine application defect still fails, on record.

Spec2TestAI is the Quality Intelligence platform: requirements intelligence, AI test generation and synthetic data, test execution, and predictive quality, traceable from requirement to release. Spec2RunAI is the test execution product inside it, available on its own. Teams that only need to run their manual test cases buy Spec2RunAI; teams that want the full lifecycle connected buy Spec2TestAI, which includes it.

Browser-delivered applications, including packaged enterprise UIs such as Insurity, Guidewire, Salesforce, SAP, Workday, and ServiceNow. The built-in browser is headless Chromium; real Google Chrome and Microsoft Edge on Windows and macOS run through the customer's own BrowserStack account; mobile and tablet viewports run by emulation on the built-in browser. Applications behind a firewall run through the local agent. Native iOS, Android, and desktop applications are not in scope today.

A Run Evidence Report: verification coverage (each step marked Verified, Not verified, Failed, or Not run, with the reason), findings for reviewers, comparison with the previous suite run, failures classified as application defect, test defect, environment, or data with developer-ready defect reports, an AI provenance appendix (Assertion Lock, script fingerprints, every rewrite), and a PDF with signature lines for QA review and business sign-off, or Markdown for a tracker.

No. RPA platforms are built for unattended production automation with orchestration, queues, credential vaults, and retry policies. Spec2RunAI is built to notice when a screen changed and report it, to record every decision for a reviewer, and to run suites on a release cadence. It shares with RPA the ability to drive any browser UI from plain English without scripting, which teams use for test data setup and sandbox operations, and it can serve as a regression harness for RPA processes, which break on the same UI changes that break test suites.

Yes, for authenticator-app (TOTP) 2FA. The test account's authenticator key is stored once in the encrypted Secrets vault and referenced by name in the step; the engine types the current code at run time, waits rather than typing a code about to expire, and never replays a used code. The key is never shown again and never reaches a local agent, which receives only the current code. SMS and email codes are not supported.

Yes. Upload the workbook you already use as a dataset; its column headers become tokens that the engine matches to on-screen labels at run time, so there is no column-to-locator mapping. Each row runs as its own iteration with its own verdict and evidence.

Yes. AgileAI Labs holds a SOC 2 Type II attestation. Beyond the attestation: data is isolated per tenant, your source code is never accessed, local agents hold no AI key and connect outbound over HTTPS only, and every AI decision is recorded in an audit trail that the Run Evidence Report draws on. Ask us during your security review.