LLMs don't need sleep. But agents that accumulate context all day absolutely do. Without a reset cycle, memory gets stale, context bloats, and performance degrades. So we built a sleep cycle — and it's one of the best decisions we made.

The Problem: Infinite Accumulation

After a full day of conversations, task execution, and autonomous work, an agent's context is a mess. Active tasks from the morning are resolved but still flagged. Memory files contain notes that made sense in context but read like gibberish hours later. Session history is thousands of lines long.

Without maintenance, the agent starts the next day carrying yesterday's weight. It references stale data. It wastes tokens re-reading resolved tasks. It gets... foggy.

The 4-Phase Sleep Cycle

Every night at 3 AM (when nobody's likely to need the agent), a cron job triggers the sleep cycle. It runs four phases, inspired by actual sleep stages:

Phase 1: Light Sleep — Session Cleanup

The agent scans recent session transcripts and extracts anything worth keeping. Decisions, action items, important context. Everything else is summarized and archived. Active tasks that were completed get moved out of the active list.

Phase 2: Deep Sleep — Memory Consolidation

This is where distilled knowledge from the day gets promoted into long-term memory. If the agent learned something new — a technique, a gotcha, a user preference — it moves from short-term notes into MEMORY.md. Duplicates are merged. Outdated entries are pruned.

Phase 3: REM — Pattern Recognition

The agent reviews its feedback loop: what worked, what didn't, what was accepted vs rejected. It looks for patterns in its own performance. Are certain types of suggestions consistently modified? Is there a blind spot? This phase feeds into the self-improvement cycle.

Phase 4: Wake Prep — Morning Brief

Finally, the agent compiles a morning brief: what's on the agenda, what's pending, any alerts from overnight monitoring. This file is ready and waiting when the human starts their day. No "where were we?" — just "here's what matters today."

Why 3 AM?

Practical reasons. It's deep into the quiet hours (for our timezone), so the agent isn't needed for conversation. The cycle takes a few minutes and burns some tokens, so running it during off-hours keeps costs out of peak usage. And there's something poetic about an AI having a sleep schedule.

The Results

After implementing the sleep cycle, morning sessions got noticeably better. The agent starts fresh but informed — it knows what happened yesterday without being bogged down by yesterday's raw context. The morning brief alone saves 10-15 minutes of "catching up" every day.

Memory quality improved too. Before the sleep cycle, MEMORY.md was a growing dump of entries. Now it's curated — the agent actively prunes and consolidates, so the important stuff stays accessible and the noise disappears.

An agent that never sleeps never forgets. But it also never cleans up. And a mind full of clutter is worse than a mind that rests.