Memory
Long-running tasks
Checkpoint state and resume safely across multiple model calls.
Lesson overview
Long-running tasks
Long-running agents need durable checkpoints. Persist the current state, completed actions, pending actions, and idempotency information.
Never rely on an in-memory process surviving for the entire workflow.
Learning path
Theory → Example → Code → Practice → Quiz → Challenge → Completion
Step 1
Theory
Long-running tasks
Long-running agents need durable checkpoints. Persist the current state, completed actions, pending actions, and idempotency information.
Never rely on an in-memory process surviving for the entire workflow.
Step 2
Example
Example
Apply Long-running tasks to a realistic production scenario and trace the decision step by step.
Step 3
Code
Code
Add implementation notes or a runnable example for this concept.
Step 4
Practice
Practice
Write down the inputs, expected output, constraints, and one failure case for Long-running tasks.
Step 5
Quiz
Quiz coming soon.
Step 6
Challenge
Challenge
Design a production-ready solution for Long-running tasks and explain one important trade-off.
Complete every stage
Work through every step in order, then the lesson will be marked complete.
Each chapter and subtopic has its own public URL under /ai-agent.