Temporal Relevance Explained

Temporal relevance refers to weighting newer information more heavily than older information. In a search system this means that newer content is surfaced first, while older items gradually fade in influence. Humans naturally do this when looking for answers. When you ask a colleague for the latest project update, you do not want to hear what was true six months ago.

For AI agents this distinction is critical. Without temporal relevance, an agent may retrieve outdated tickets, stale metrics, or old product documentation that no longer reflects reality. This leads to hallucinations, outdated answers, and lost trust. Temporal relevance gives you, the agent developer, control to trade off semantic accuracy for freshness. This ensures your agents are not misled by outdated results.

How Airweave applies Temporal Relevance

Airweave’s temporal relevance blends semantic similarity with a time-aware scoring system. To make this work in practice, Airweave applies a decay function that gradually reduces the influence of older items as they age. This ensures the system continuously balances what is most similar with what is most current.

• Dynamic calibration: What counts as “new” or “old” depends on the source. A week-old email may already be outdated, while a week-old GitHub file may still be considered new. Airweave automatically detects the oldest and newest data points in your collection and adjusts the decay curve accordingly, so temporal weighting fits the natural rhythm of your data.

• Linear decay: New data is favored, older data gradually receives less weight. The final score is computed by multiplying semantic similarity with a time-based decay value:

final_score = similarity_score * ((1 - recency_bias) + recency_bias * decay_value)

where

• decay_value 1.0 - (age_of_datapoint / total_time_span)
  Computed automatically by Airweave based on the relative age of each datapoint in the collection.

• recency_bias is a user-set parameter (default 0.3) that controls how much recency influences the score, where:

  • recency_bias = 0 → only similarity matters, recency has no effect;

  • recency_bias = 1 → ranking is dominated by recency (though similarity still plays a minimal role);

  • With recency_bias = 0.3: newest items keep full similarity score, oldest items are reduced to 70%, and everything else scales linearly in between.

• Filter-aware: When you filter to a subset of data, Airweave recalculates the oldest and newest points inside that scope. This ensures temporal relevance is applied relative to the filtered dataset rather than the entire collection. For example, if you filter for this month’s tickets, recency is measured within that subset so results remain accurate and current.

This means your agents can always pull the right balance of what is relevant and what is current.

Why this matters for AI Agents

Agents designed to do knowledge work need to be trusted. Users will not rely on an assistant that surfaces an outdated policy, a deprecated API method, or a resolved incident. Temporal relevance directly tackles this problem by ensuring that the information your agent delivers is not only semantically accurate but also aligned with the present moment.

This is especially important in domains like:

• Customer support where agents need the latest tickets, not old cases.

• Engineering knowledge where deprecations and new releases change the right answer quickly.

• Business intelligence where yesterday’s numbers may no longer be useful today.

Airweave’s temporal relevance gives your agents the time-awareness they need to ground their answers in what matters now.

Next steps

Temporal relevance is only the first step in making agents time-aware. We are exploring:

• Nonlinear decay functions for domains or connectors with faster or slower information cycles.

• Contextual temporal relevance where some queries favor the newest answer, while others surface older canonical knowledge.

• Timeline awareness so agents can not only prioritize recent data but also understand the progression of events leading up to the present moment.

Temporal relevance is one more way we are helping teams build agents you can trust.