XIII · Cognition & IntrospectionEmerging★

Mode-Adaptive Cadence

also known as Idle/Intense Modes, Variable Tick Rate, Salience-Driven Cadence

Vary the agent's loop interval based on current salience so the agent thinks faster when something is happening and slower when nothing is, instead of running on a fixed cron.

Context

A team is running an agent on a continuous tick loop whose workload is bursty by nature: long quiet stretches with nothing happening, punctuated by intense periods when the user is actively engaging, a deadline is close, or new events keep arriving. The agent has access to signals about its own current load — salience scores on recent ticks, affect levels, the recency of external input — but its loop interval is a single fixed number set in configuration.

Problem

A fixed-cadence loop is wrong in both directions. Running every fifteen seconds wastes tokens on idle evenings when nothing has changed since the last tick. Running every five minutes makes the agent feel sluggish during active conversation when the user is waiting for the next response. The agent already has the signal needed to decide which regime it should be in, but nothing reads that signal and adjusts the interval, so compute spend and responsiveness are decoupled from what is actually happening.

Forces

  • Cadence too high wastes tokens on nothing happening.
  • Cadence too low misses fast-moving events.
  • Self-set cadence can run away if the agent rewards itself for going faster.
  • The user may need to force a mode without the agent overriding.

Example

A long-running personal agent runs a fixed-cadence loop every 60 seconds, which is wasteful when nothing is happening and too slow when the user is actively typing. The team adds mode-adaptive-cadence: each tick scores its own salience, and crossing a threshold locks the agent into a 15-second 'intense' mode for the next several ticks before drifting back to the 60-second 'idle' cadence. Mode transitions are written to the ledger. Compute spend drops on quiet evenings and responsiveness rises during active windows.

Diagram

Solution

Therefore:

Define two (or more) modes with different sleep intervals (idle around 60s, intense around 15s). Score each tick's outcome for salience or external impulse; if it crosses a threshold, lock into intense mode for N ticks. Otherwise drift back to idle. Mode transitions are written to the ledger. The user can force a mode but cannot bypass the configured floor and ceiling. Lock-in cannot be self-extended without an explicit external trigger.

What this pattern forbids. The cadence cannot exceed configured floor or ceiling (e.g. minimum 5s, maximum 5min), and mode lock-in cannot be self-extended by the agent without an explicit external trigger; runaway intense mode is blocked.

The smaller patterns that complete this one —

  • usesSalience Attention Mechanism★— Score every candidate memory item with a weighted salience function so each tick attends to a small, relevant top-k subset rather than re-reading all memory.

And the patterns that stand alongside it, or against it —

  • complementsSalience-Triggered Output·— Have the agent emit a message only when an internal salience signal crosses a threshold, not on every cycle.
  • complementsStep Budget★★— Cap the number of tool calls or loop iterations the agent is allowed within a single request.
  • alternative-toScheduled Agent★★— Run the agent on a fixed schedule independent of user requests.
  • complementsCognitive-Move Selector·— Restrict idle-tick cognition to a small agent-vetted menu of named cognitive moves so the next thought has a determinate shape rather than free-form drift.
  • complementsMeditation Mode·— Switch the agent into a bounded runtime mode where external I/O pauses but internal inference accelerates, with the tool surface collapsed to inner-only operations and output written to a private journal.
  • complementsAmbient Presence Sensing·— Read pacing signals from the human's frontend (typing rate, idle duration, tab visibility) as ambient weather between messages, derive a presence-quality value the agent can act on, never replaying the raw signals back.
  • complementsAdaptive Compute Allocation★— Allocate inference-time compute (thinking tokens, samples, depth, model size) per query based on input difficulty, rather than using a fixed budget across all queries.

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References

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