> For the complete documentation index, see [llms.txt](https://learning.contextqa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learning.contextqa.com/planning-and-decision-guides.md). # Planning & Decision Guides - [Planning & Decision Guides Overview](https://learning.contextqa.com/planning-and-decision-guides/decision-guides.md): Evidence-led guides for evaluating AI testing vendors, architecture, security, operating cost, team readiness, model routing, and workflow fit. - [AI Testing Vendor Demo Scorecard](https://learning.contextqa.com/planning-and-decision-guides/ai-testing-vendor-demo-scorecard.md): Compare AI testing vendors with the same workflow, evidence rubric, integration checks, operating boundaries, and portability test. - [Compare AI Testing Alternatives](https://learning.contextqa.com/planning-and-decision-guides/compare-ai-testing-alternatives.md): Shortlist AI testing platforms by workflow, maintenance, execution, evidence, security, portability, and operating constraints without invented rankings. - [Test Automation Cost per Release](https://learning.contextqa.com/planning-and-decision-guides/test-automation-cost-per-release.md): Measure test creation, execution, maintenance, failure investigation, integrations, infrastructure, and AI usage per usable release verdict. - [Testing as a Service or QA Platform?](https://learning.contextqa.com/planning-and-decision-guides/testing-service-vs-qa-platform.md): Choose a managed testing service, QA platform, in-house framework, or mixed model by clarifying ownership, evidence, skills, transition, and exit requirements. - [Test Runtime vs Release Lead Time](https://learning.contextqa.com/planning-and-decision-guides/test-runtime-vs-release-lead-time.md): Measure queue, setup, execution, retry, investigation, review, and approval time between a ready change and a usable release verdict. - [AI Testing Vendor Security Evidence](https://learning.contextqa.com/planning-and-decision-guides/ai-testing-vendor-security-evidence.md): Request verifiable evidence for AI data flows, model providers, repositories, credentials, access controls, retention, deletion, and revocation. - [Smallest Operable AI Testing Stack](https://learning.contextqa.com/planning-and-decision-guides/smallest-ai-testing-stack.md): Compare repository-led, managed-platform, and mixed AI testing architectures by system of record, trigger, execution, evidence, maintenance, and ownership. - [Chart & Download Assertion Evaluation](https://learning.contextqa.com/planning-and-decision-guides/chart-and-download-assertion-evaluation.md): Design evidence-led tests for chart values and downloaded files, separating business correctness from click success and unsupported automation. - [QA Skills for Agent-Assisted Teams](https://learning.contextqa.com/planning-and-decision-guides/qa-skills-for-agent-assisted-teams.md): Assess requirements judgment, test oracles, evidence, diagnosis, prompt design, tool permissions, and workflow ownership before scaling AI-assisted testing. - [Choose a QA Conference](https://learning.contextqa.com/planning-and-decision-guides/choose-a-qa-conference.md): Select a QA conference, workshop, community event, or recording by role, operating problem, required takeaway, evidence access, budget, and follow-up capacity. - [Turn a Conference Demo into an Evaluation](https://learning.contextqa.com/planning-and-decision-guides/conference-demo-to-evaluation.md): Capture a conference or vendor claim, distinguish slides from observed evidence, and turn one relevant idea into a bounded post-event experiment. - [OpenRouter Controls for QA Context](https://learning.contextqa.com/planning-and-decision-guides/openrouter-controls-for-qa-context.md): Review OpenRouter and provider logging, retention, ZDR, model routing, budgets, fallbacks, guardrails, and rejected-request behavior for QA workloads. - [QA Agent Workflow Graph or Simple Loop](https://learning.contextqa.com/planning-and-decision-guides/qa-agent-workflow-graph.md): Choose a linear agent loop or persistent workflow graph by evaluating branching, resumability, approvals, state, retries, idempotency, and termination needs. - [AI Testing Change-to-Decision Log](https://learning.contextqa.com/planning-and-decision-guides/ai-testing-change-decision-log.md): Decide whether an AI testing, agent, MCP, framework, or model-routing update should change a QA workflow, evaluation criterion, or existing guide. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://learning.contextqa.com/planning-and-decision-guides.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.