AI Tool Temporal for Agent Workflows: Drafting Durable Loops

End-of-lesson check

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1. In Temporal, what does 'durable execution' primarily refer to?

   1. The capacity of an activity to store data persistently in a database
   2. The ability of a workflow to continue running even if the underlying process crashes or restarts
   3. The mechanism that speeds up workflow execution through caching
   4. The feature that automatically backs up workflow code to cloud storage

2. When using AI to scaffold a Temporal workflow, which task is AI generally capable of performing reliably?

   1. Determining the exact business logic that belongs in each workflow
   2. Generating workflow and activity code skeletons with typed inputs and outputs
   3. Ensuring the workflow will pass all compliance audits
   4. Verifying that each activity will be idempotent when run in production

3. What does idempotency mean in the context of Temporal activities?

   1. Each activity must be executed by exactly one worker
   2. Activities cannot accept any input parameters
   3. Activities must complete within a strict time limit
   4. Running an activity multiple times produces the same final state as running it once

4. A developer notices their AI-generated Temporal workflow calls an external API directly from the workflow code. Why is this problematic?

   1. Workflow code must be deterministic, but external API calls introduce non-deterministic behavior
   2. External API calls are not allowed in any part of Temporal applications
   3. AI-generated code cannot make API calls due to security restrictions
   4. Workflow code runs too slowly to handle API responses

5. Where should non-deterministic operations like external API calls be placed in a Temporal application?

   1. In the workflow code directly, for better performance
   2. In activity functions, which run outside the workflow's replay logic
   3. In timer callbacks, which run asynchronously
   4. In signal handlers, which bypass replay entirely

6. Which of the following can AI typically generate for a Temporal workflow?

   1. A certification that the workflow is production-ready
   2. The complete business logic for the entire application
   3. A guarantee that the workflow will never encounter errors
   4. A table specifying retry policies and timeouts for each activity

7. What is a signal in a Temporal workflow used for?

   1. Scheduling activities to run at specific future times
   2. Pausing workflow execution permanently
   3. Sending asynchronous messages to a running workflow to change its behavior
   4. Logging errors that occur during workflow execution

8. Why must Temporal workflow code be deterministic?

   1. So multiple workflows can share the same memory space
   2. So the workflow can automatically scale across servers
   3. So the workflow can run faster on multi-core processors
   4. So the workflow can be replayed from the beginning while producing the same results

9. A Temporal activity is designed to charge a customer's credit card. What should be true about this activity?

   1. The activity cannot return any data to the workflow
   2. The activity should be idempotent, handling the case where the charge is attempted multiple times
   3. The activity must always complete in under 10 seconds
   4. The activity must be called from a signal handler, not directly

10. What is the primary purpose of a retry policy in Temporal?

    1. To speed up workflow execution by running tasks in parallel
    2. To guarantee that activities never fail under any circumstances
    3. To prevent activities from running more than once
    4. To automatically re-execute failed activities according to configurable rules

11. Which statement best describes why AI cannot fully verify activity idempotency?

    1. AI cannot observe the actual runtime behavior of external systems that activities interact with
    2. Temporal's replay mechanism automatically makes all activities idempotent
    3. Idempotency verification requires no understanding of the activity's implementation
    4. AI code generation always produces idempotent activities by default

12. What type of problem would cause a Temporal workflow to fail during replay?

    1. Adding more logging statements to activity code
    2. Calling a non-deterministic function like getting the current time directly in workflow code
    3. Increasing the timeout on an existing activity
    4. Adding a new activity to the workflow after deployment

13. A developer asks AI to scaffold a Temporal workflow for an agent loop. What should the AI include in its output?

    1. A deployment script that runs the workflow in production
    2. A guarantee that the generated workflow follows all best practices
    3. Signal handlers for external events and timer-based escalation logic
    4. Complete test cases that verify production behavior

14. What distinguishes an agent loop wrapped in Temporal from a simple loop in traditional code?

    1. The workflow can pause, resume, and retry across process failures while maintaining state
    2. The loop requires no code because Temporal generates it automatically
    3. The loop automatically optimizes itself based on performance metrics
    4. The loop runs faster because Temporal caches previous results

15. What is a timer used for in a Temporal workflow?

    1. Connecting to external time services
    2. Storing timestamped data for later retrieval
    3. Scheduling delayed actions or implementing timeouts within the workflow logic
    4. Measuring how long activities take to execute