Temporal Context and the Event Clock

Why Time Matters

Consider a simple fact: “Paula works at Microsoft.” This seems straightforward, but it's incomplete. When did she start? Does she still work there? What was true in 2022?

Existing knowledge graphs and databases generally don't solve this. They define entities like Person and Organization, but they don't have native primitives for “this relationship was true from date X to date Y.”

“We've built trillion-dollar infrastructure for what's true now. Almost nothing for why it became true.”

For AI agents making decisions, this temporal blindness is a critical limitation. Agents need to understand not just current state, but historical patterns and precedent.

The Two Clocks Problem

Every system has two fundamentally different types of time tracking:

The event clock tells you how the system behaves—and behavior is what you need to simulate and predict. This is what experienced employees have that new hires don't: a better mental model of how things have changed over time.

Temporal Validity

Temporal validity is the practice of tracking when facts are true, not just what facts are true. It requires adding time boundaries to every piece of information:

{
  "fact": "Paula works at Microsoft",
  "validFrom": "2020-03-15",
  "validUntil": "2024-08-30",
  "source": "hr_system_record_#12345",
  "confidence": 0.99
}

{
  "fact": "Paula works at Google",
  "validFrom": "2024-09-01",
  "validUntil": null,  // Still current
  "source": "linkedin_profile",
  "confidence": 0.85
}

Key Capabilities of Temporal Validity

• ✓Point-in-time queries: “What did we believe about this account when we made that decision?”
• ✓Change tracking: “How has this customer's tier changed over time?”
• ✓Precedent search: “Find similar decisions from the same time period.”
• ✓Decision replay: “Recreate the context that existed when this was decided.”

Fact Resolution

When you extract facts from content, you get assertions—not necessarily truth. Content can be wrong, outdated, or contradictory.

Fact resolution is the process of determining what's canonical, what's superseded, and what's corroborated. It requires judgment about:

• ●Temporal ordering: Which information is more recent?
• ●Source authority: Which source is more authoritative?
• ●Corroboration: Do multiple sources agree?
• ●Update semantics: How does later information update earlier assertions?

“Freshness and recency are not the same as accuracy and precision. Just because data is old and obscure doesn't mean it's not still valid.”

Implementation Approaches

Building temporal context into your systems requires specific infrastructure choices:

Impact on AI Reasoning

Temporal context fundamentally changes what AI agents can accomplish:

The path to economically transformative AI might not require solving continual learning. It might require building world models that let static models behave as if they're learning—through expanding evidence bases and inference-time compute to reason over them.