Tutorials Prompt Engineering Tutorial
AI Task Management — Complete Guide
AI Task Management — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 56 of 100
AI Task Management
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 6: AI Agents
What is this?
Task management for agents means queues, priorities, idempotency, and status — same discipline as job workers.
Why should you care?
PromptVerse Agent Task Queue stores RUNNING, WAITING_TOOL, DONE, FAILED with retry policy.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
task = { id, agent_id, payload, status: "QUEUED", attempts: 0 }
worker.claim(task)
while status !== DONE && attempts < 3:
run_agent_step(task)
if tool_timeout: status = WAITING_TOOL, schedule_retryWhat happened?
- claim prevents double execution.
- WAITING_TOOL resumes after async tool callback.
- attempts cap avoids infinite retry.
Practice next
- Model one agent job as task row.
- Add idempotency key on create.
- Handle tool timeout resume.
- Priority queue for P1 incidents.
- Dead-letter FAILED tasks for review.
Remember
Tasks are durable. Idempotency keys. Retry with backoff.
Email backlog
2000 emails trigger agent.
Outcome: Queue drains with concurrency 10; failures isolated.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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