How AI Agents Like In Cursor Create and Follow To-Do Lists: From Zero to Production

This tutorial explains how modern AI agents (like those in Cursor, IDE copilots, and autonomous coding tools) create, maintain, and execute to-do lists — and how you can build the same capability from scratch to production.

This is not a UX trick.

A to-do list is the core cognitive control structure that turns a language model from a chatty assistant into an agent that finishes work.

1. Why To-Do Lists Matter for Agents

Large Language Models (LLMs) do not naturally:

• Track long-term goals
• Maintain execution state
• Know what is “done” vs “pending”
• Recover after interruptions

To-do lists solve this by acting as:

Externalized working memory + execution plan

In tools like Cursor, the to-do list is often invisible — but it exists conceptually as:

• A task plan
• A checklist
• A sequence of commits
• A structured scratchpad

2. Mental Model: Agent = Planner + Executor + Memory

At a minimum, a productive agent has three internal subsystems:

┌─────────┐
│  Goal   │
└────┬────┘
     │
┌────▼─────┐
│ Planner  │  → creates to-do list
└────┬─────┘
     │
┌────▼─────┐
│ Executor │  → executes items
└────┬─────┘
     │
┌────▼─────┐
│  Memory  │  → tracks done / pending
└──────────┘

Cursor-style agents continuously re-plan as execution progresses.

3. Step 1: Turning a Goal into a To-Do List

3.1 The Planning Prompt

The agent starts by transforming a vague goal into concrete, ordered tasks.

Example goal:

“Add authentication to this API”

Planner output:

{
  "tasks": [
    {"id": 1, "description": "Inspect existing auth patterns"},
    {"id": 2, "description": "Choose auth mechanism"},
    {"id": 3, "description": "Add middleware"},
    {"id": 4, "description": "Update routes"},
    {"id": 5, "description": "Add tests"}
  ]
}

This list is:

• Ordered
• Finite
• Inspectable

3.2 Good To-Do Lists Have Constraints

Effective agent to-do lists:

• Are small (5–15 items)
• Are executable (no vague verbs)
• Include verification steps

Bad:

“Improve code quality”

Good:

“Run linter and fix warnings”

4. Step 2: Persisting the To-Do List (Memory)

Cursor-like systems persist task state outside the LLM.

Typical representations:

{
  "goal": "Add authentication",
  "tasks": [
    {"id": 1, "status": "done"},
    {"id": 2, "status": "done"},
    {"id": 3, "status": "in_progress"},
    {"id": 4, "status": "pending"}
  ]
}

Storage options:

• In-memory (simple agents)
• Local files (Cursor-style)
• Databases (production)

The LLM never owns truth — storage does.

5. Step 3: Executing One Task at a Time

Agents should never execute the whole list at once.

Execution loop:

while tasks remain:
  select next task
  execute task
  verify outcome
  mark done
  re-plan if needed

This mirrors how human developers work.

6. Verification: The Missing Ingredient

Cursor agents often implicitly verify by:

• Running tests
• Observing compiler errors
• Reading tool output

In production agents, verification should be explicit.

Example:

{
  "task": "Add middleware",
  "verification": "API returns 401 for unauthenticated requests"
}

Without verification, agents hallucinate progress.

7. Re-Planning: Why To-Do Lists Change

Good agents expect failure.

Triggers for re-planning:

• Tests fail
• Unexpected file structure
• Missing dependencies

Re-planning example:

{
  "action": "insert_task",
  "after": 2,
  "task": "Install auth library"
}

Cursor does this constantly.

8. How Cursor Makes This Feel Invisible

Cursor hides the to-do list by:

• Executing tasks incrementally
• Reflecting progress via code diffs
• Using the editor as implicit state

But internally, the agent is still:

• Planning
• Executing
• Verifying
• Updating task state

9. Minimal Implementation (Pseudo-Code)

goal = user_input()
tasks = planner(goal)
state.save(tasks)

for task in tasks:
    result = executor(task)
    if verify(result):
        state.mark_done(task)
    else:
        tasks = replan(goal, state)

This loop is the heart of agentic systems.

10. Production Considerations

10.1 Guardrails

• Max steps
• Cost budgets
• Timeout per task

10.2 Observability

Track:

• Tasks per goal
• Re-plans per task
• Success rate

10.3 UX Matters

Expose progress:

• “Step 3 of 7”
• Current task description

This builds trust.

11. Common Failure Modes

❌ Tasks too vague ❌ No verification ❌ Infinite re-planning ❌ Letting LLM mutate state directly

12. How This Scales to Multi-Agent Systems

In A2A systems:

• Planner = one agent
• Executors = specialist agents
• Shared to-do list = task contract

The to-do list becomes the coordination primitive.

13. Key Insight

AI agents don’t succeed because they are smart.

They succeed because they are organized.

To-do lists are how we give LLMs organization.

14. Further Reading

• Agentic Workflows
• ReAct Pattern
• Planner–Executor Architectures
• Cursor Engineering Blog
• Multi-Agent Task Decomposition

Final Takeaway

If you want to build agents that finish work:

• Force them to plan
• Externalize the plan
• Execute one step at a time
• Verify everything