Coordinating timed actions between agents is foundational for persistent, useful agent systems—especially in real-world or long-horizon tasks. Time must be treated as a coordination surface in any system that aims to reliably schedule, trigger, and audit agent behavior. There is a core tension to resolve:
Should time be handled internally by the model (e.g., via Python timers), or externally via infrastructure (e.g., calendar events, job queues, or event buses)? Chronologue externalizes time management for long-term reliability and coordination across agents, devices, and platforms.

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Options for Handling Time

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Option 1: Model Writes Code to Handle Time

Example: time.sleep(3600) or await asyncio.sleep(...) Pros:
  • Self-contained execution
  • Simple to simulate delays in short-lived tasks
Cons:
  • Not reliable for long delays (e.g., hours/days)
  • Context may be lost if agent is terminated
  • No shared or observable state for other agents
Use case: Ephemeral scripts or local automation only.

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Delegate to External Scheduler

Agents write plans to infrastructure like:
  • .ics files (Google/Apple calendar)
  • Redis queues with time-based triggers
  • APScheduler jobs in FastAPI apps
  • LangGraph nodes with built-in timers
Pros:
  • Durable, observable state
  • Distributed coordination and time-zone awareness
  • Supports multiple agents/devices
  • Enables debugging, rerouting, and modification
Verdict: The most scalable and reliable path.

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Temporal Middleware Design Pattern

Introduce a temporal coordination layer between agent planning and agent execution. Steps:
  1. Plan generation LLM or planner proposes a task with scheduled_for field
  2. Storage Persist as a structured trace (calendar_event, task_id)
  3. Trigger External system notifies or invokes agents at execution time
This mirrors job queues in web infrastructure but with semantically meaningful tasks and temporal tags.

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Calendar-Based Execution

Example: Agent generates .ics file to feed the dog at 6 PM Trace: 
{
  "type": "calendar_event",
  "timestamp": "2025-05-11T06:55:00Z",
  "scheduled_for": "2025-05-11T07:00:00Z",
  "title": "Feed the dog",
  "content": "Place dog food into the bowl",
  "tempo_tag": "@morning @daily",
  "task_id": "task-001",
  "uid": "evt-001",
  "duration_minutes": 5
}
This can be rendered as a user-facing calendar view or fed into an agent runtime that monitors the current time and dispatches actions.

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Task Queue / Job Scheduling Integration

Use Redis + APScheduler or a similar pattern:
  1. Agent writes task to queue with scheduled_for
  2. Scheduler polls or receives signal when time is met
  3. Agent is notified or invoked with payload
Useful for:
  • Delayed reminders
  • Coordination across devices
  • Notifications or external API calls

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Trace Format Specification

All timed actions share a common structure: 
{
  "type": "calendar_event",
  "timestamp": "2025-05-11T08:00:00Z",
  "scheduled_for": "2025-05-11T12:00:00Z",
  "title": "Clean Room 1705",
  "content": "Child guest: check under beds, toy sanitization, extra towels requested.",
  "room_number": "1705",
  "assigned_to": "crew-03",
  "tempo_tag": "@family-suite 60min_block",
  "task_id": "room-1705-0511",
  "uid": "evt-1705",
  "duration_minutes": 60
}
Recommended fields:
  • type
  • timestamp
  • scheduled_for
  • title, content
  • task_id, uid
  • tempo_tag
  • duration_minutes
  • Optional: assigned_to, trigger_condition, feedback_notes

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Use Case: Hotel Room Coordination

Coordinating cleaning and inspections across multiple rooms with time-triggered events and agent handoff:
  1. Clean room → trace logged with scheduled_for
  2. Manager inspects → scheduled via linked uid
  3. Feedback loop logs deviations or updates
{
  "type": "calendar_event",
  "scheduled_for": "2025-05-11T11:30:00Z",
  "title": "Inspect Suite 1901",
  "content": "Manager inspection before VIP guest arrival. Verify scent profile and towel folding.",
  "room_number": "1901",
  "assigned_to": "manager-01",
  "tempo_tag": "@post-clean-inspection",
  "task_id": "room-1901-inspect-0511",
  "uid": "evt-1901a",
  "duration_minutes": 15
}

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Best Practices and Extensibility

  • Make scheduled_for and deviation_ms first-class fields in all traces
  • Use standardized tempo_tag formats (@morning, 30min_block, @daily)
  • Build plug-and-play backends (calendar, Redis, APScheduler, event bus)
  • Keep models focused on planning, not execution logic