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What Is a “Good” Event Feed?

Agentic learning systems depend on high-quality behavioral data to evaluate the impact of their actions. This guide explains which data matters most and provides a framework for prioritizing event instrumentation.

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Categories of Data in Agentic Systems

Agentic systems are designed to learn by observing the effects of their actions on users. To facilitate that learning, they rely on three fundamental categories of data: user attributes, content attributes, and timestamped events. Each of these serves a distinct role in the agent’s learning environment.
  • User attributes (location, language, subscription tier, behavioral rollups) are static and lack timestamp precision. They can’t measure treatment impact directly but help filter eligibility and match similar users for learning acceleration.
  • Content attributes (genre, artist, brand, feature category) enable content personalization but describe treatments rather than outcomes. They show what was recommended, not how users responded.
  • Timestamped user events are most valuable for learning. These actions occur at specific moments (starting a stream, completing a purchase, reading an article), enabling causal inferences about treatment effects.Agentic Learning Needs Timestamped Events More Than Any Other Kind of Data

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Why Timestamped Events Matter Most

Agentic learning requires isolating individual treatment effects. Agents must track when treatments are delivered, to whom, and what happened afterward. Timestamped events make this linkage robust. Without them, or with only coarse aggregates, agents lose the ability to learn precise cause-effect relationships. Not all events are equally valuable. The best signals represent volitional, meaningful, spontaneous engagement. Subscription renewals are business-critical but predictable and scheduled, making them poor reward events. Account linking actions are finite and non-repeatable, failing to provide ongoing feedback. Quantity matters alongside quality. Too narrow an event set starves agents of signal. Rich event variety captures subtle response patterns and prevents agents from misinterpreting missing data as non-response. Broad coverage ensures genuine influence patterns remain visible.

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Characteristics of Good Reward Events

Good reward events are distinguished by a set of properties that make them reliable indicators of user intent and value alignment. These events provide the backbone for agentic learning because they allow the system to observe how users react to treatments in ways that are both timely and meaningful. Not every user action qualifies. Some are noisy, passive, or structurally unsuitable for learning. The most effective reward events share these qualities:
  • Volitional: The user initiates the action by choice, not passively or automatically.
  • Spontaneous: The event can occur at any time; it is not bound to a predefined schedule.
  • Repeatable: The user can perform the action multiple times without exhausting the opportunity.
  • Value-Aligned: The event reflects real usage of the product or service in a way that supports the business’s long-term goals.
These traits ensure that the system learns from actions that are not only detectable but also meaningful. For instance, a user streaming a song, purchasing a product, or completing a fitness goal are all behaviors that are voluntary, time-sensitive, and repeatable. By contrast, subscription renewals or account linkages may be strategically important, but they occur on predictable schedules or as one-time actions, making them poor choices for continuous feedback and learning.

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A Checklist for Event Prioritization

This checklist ranks event types by usefulness to agentic learning. Provide as many as possible, starting from the top:
  • Tier 1: Core Value Actions
    Direct indicators of meaningful product use: content streamed/consumed (music, video, articles), products purchased, transactions completed (bill payments, fund transfers), key actions logged.
  • Tier 2: Feature Engagement
    Deliberate interaction with features: adding to playlists/wishlists/carts, toggling settings or features, modifying subscription plans. Strong signals of intentional use.
  • Tier 3: Session and Message Engagement
    User presence indicators: app opens, session starts/stops, notification clicks, message views. Establishes attribution windows and general engagement, though more ambiguous for value measurement.
  • Tier 4: Utility Interactions
    Intent signals weakly linked to consumption: site search, browsing, tag-based exploration. Often preliminary to meaningful behaviors but less discriminative alone.
  • Tier 5: Passive Interaction Events
    Generic page views or button clicks not tied to specific features/content. Useful for interface flow and debugging, but limited value for modeling satisfaction or preferences.
  • Tier 6: Scheduled or One-Time Outcomes
    Business milestones that are externally timed or terminal: subscription renewals, trial completions, account linkages. Poor causal indicators, unsuitable as reward signals.
Note that events that make very poor reward signal may still be very important to the business. Agents generally cannot learn user preferences based on subscription auto-renewals, but those renewals should still be tracked over time, since better, more meaningful, and more frequent engagement should correlate with increased retention.

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Avoiding Unnecessary Data Volume

For many organizations - particularly those operating in event-driven architectures such as streaming services - the volume of raw behavioral data can be immense. Ingesting and transmitting all raw playback events, particularly if sampled at regular time intervals, is both financially and technically infeasible. In these situations, it helps to identify compact and representative signals. For streaming apps, a single “stream start” event per session or item is typically sufficient. This approach preserves the essential behavioral insight without overwhelming infrastructure. A corresponding “stream stop” event could also be useful and meaningful. The broader principle is that agentic learning depends on timing, not exhaustive detail. A sparse set of carefully selected, timestamped events offers more actionable learning signal than high-volume logs that are costly to transmit and difficult to operationalize.