Test Plan for AI-Enhanced Workflow¶

Verify that all defined slash commands (/group, /knowledge, etc.) work as expected.
Verify the user confirmation step for all data-modifying operations. Test both the 'Confirm' path (action is executed) and the 'Cancel' path (action is aborted).
Verify that read-only queries do not trigger the confirmation step.
Test various natural language phrasings for the same intent to ensure robust interpretation.
Test for correct and clear success messages upon completion of a command.
Test for graceful error handling when a command is malformed or an entity (e.g., user) is not found.
Test chat history retrieval. For the "@" command, verify that typing "@" triggers an auto-complete list, that the list is sorted in descending order, and that typing further filters the list. For both the UI button and the command, verify that selecting a session loads the correct history and the conversation can be continued.

Critical: Confirm that a user without specific permissions cannot execute a restricted command. This check should happen before a plan is even presented.
For query commands, verify that the results are filtered according to the user's data access rights (e.g., a manager can see their team, but not other teams).
Attempt to inject malicious commands or scripts through the chat input to test for vulnerabilities.
Verify that unauthenticated users cannot access or trigger the chat workflow.

Conduct end-to-end tests from the UI to the api_gateway, ai_service, mcp_service, and back, covering both confirmed and canceled flows.
Verify that the ai_service correctly uses Redis for short-term memory and MongoDB for long-term logging.
Test the integration with the MCP server, ensuring it correctly calls the underlying platform APIs only after user confirmation.
Verify that chat history is being correctly persisted in MongoDB and can be retrieved accurately.

For every command tested (successful, failed, and canceled), verify that a corresponding, accurate entry is created in the security audit trail.
Ensure the audit log correctly identifies the user, the action they attempted, its proposed plan, and the final outcome (executed, canceled, or failed).

Verify that simple, read-only queries consistently return a response in under 3 seconds as per NFR-2.
Test the system's response time under a simulated load of multiple concurrent users to ensure performance does not degrade significantly.

Test the clarity and user-friendliness of the AI-generated responses and confirmation plans. Non-technical users should be able to easily understand the proposed actions.
Verify that the slash command structure is intuitive and that error messages for malformed commands are helpful.
Test the user experience of the chat history retrieval, including the @ command auto-complete feature.

Test the system's behavior when downstream services are unavailable. For example, if the mcp_service cannot connect to the auth_service, the chat should return a graceful error message.
Simulate a failure in the ai_service or its database connections (Redis, MongoDB) to ensure the main application remains stable and the chat component shows an appropriate disabled/error state.