> 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/business-use-cases/connected-delivery-workflows.md). # Connected Delivery Workflows ## Quick answer Connect ContextQA requirements, tests, CI/CD execution, notifications, defects, reports, and AI agents across the delivery lifecycle. Use this page to connect the business problem to a supported ContextQA workflow, its expected value, and its operating considerations. ## What this page covers {% hint style="info" %} **Business problem:** Test automation creates less value when requirements, build gates, failures, and owners remain disconnected across product and engineering tools. {% endhint %} ContextQA can connect authoring and execution to issue trackers, source workflows, CI/CD, notifications, and MCP-compatible agents. Choose integrations by the decision they support, then keep the detailed execution evidence in ContextQA as the source of truth. ## Reference flow 1. A product story or requirement becomes the traceable source for generated test cases. 2. QA reviews and approves the positive, negative, boundary, and end-to-end coverage. 3. The cases are organized into suites and a release plan. 4. CI/CD triggers the appropriate plan for a pull request, deployment, or release candidate. 5. ContextQA executes and returns status to the pipeline. 6. A concise result is sent to the team channel; the full report retains step, screenshot, video, console, network, and trace evidence. 7. A confirmed product defect becomes a linked work item with the relevant reproduction context. 8. Trend, flakiness, root-cause, and coverage views guide the next release decision. ## Integration patterns | Need | Pattern | Design note | | ------------------------- | ---------------------------------------------------------------------- | ------------------------------------------------------------------------ | | Requirements traceability | Generate from Jira or another supported source and preserve links/tags | Keep one owner for ambiguous acceptance criteria | | Pull-request feedback | Trigger a smoke or impacted-test plan from CI/CD | Set a timeout and an explicit policy for infrastructure failures | | Release regression | Schedule or manually start a broader plan | Freeze environment and test-data assumptions for the run | | Team awareness | Send a summary to Slack or another approved channel | Include status, scope, failure count, owner, and report link—not secrets | | Defect handoff | Create a ticket after evidence review | Avoid filing duplicate tickets for test-data or environment failures | | Agent-assisted QA | Use MCP tools to create, execute, and analyze tests | Restrict credentials and permissions to the minimum required scope | ## Build-gate policy Define how the pipeline handles each result: * **Passed:** continue when the required scope completed and all blocking assertions passed. * **Product failure:** block or require an explicit release exception. * **Test failure or flake:** quarantine only with an owner, evidence, and review date. * **Environment or infrastructure failure:** do not label the product as defective; retry or escalate according to the service policy. * **Timed out or incomplete:** treat as unknown, not passed. Keep smoke gates fast and deterministic. Run deeper, longer, or integration-heavy coverage at the appropriate release stage. ## Cross-channel and agentic workflows Applications may span chat, email, Slack, WhatsApp, voice, or third-party agents. Coverage is integration-dependent. Define a proof-of-concept contract for each channel: input, identity, expected action, observable outcome, timeout, fallback, and human-escalation behavior. For an agent workflow, verify both the user-facing response and the action trail: tools invoked, API or record result, authorization boundary, refusal behavior, and audit history. Do not assume that a passing conversational response proves the downstream action occurred. ## Success measures * Stories or acceptance criteria linked to approved automated tests * Deployments evaluated by an appropriate automated plan * Failed gates assigned to the correct owner from available evidence * Time from confirmed failure to a reproducible defect ticket * Duplicate or low-quality automated defects * Test and result access granted with least privilege * Agent actions with a verifiable downstream outcome and audit trail ## Related documentation * [Integrations overview](/integrations/integrations.md) * [GitHub Actions](/integrations/github-actions.md) * [Jira integration](/integrations/jira.md) * [Slack integration](/integrations/slack.md) * [MCP Server overview](/mcp-server/overview.md) * [Reporting overview](/reporting/reporting.md) --- # Agent Instructions This documentation is published with GitBook. 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