Harness memory & meta-cognition

Two stores, one purpose: capture what the harness has learned from your past runs, then use it to improve future ones. Harness self-memory Lives at ~/.cerevisor/memory/harness/ . Auto-populated at end-of-run. Three files: self.md : human-readable retrospective. What's been working, what hasn't, recent run log. self.json : structured data: per-role success rates, average cost, recurring patterns, error frequencies. corrections.ndjson : append-only log of every time you corrected the harness ("don't auto-add reviewers", "use the cheaper model for this role"). Visible in Memory view → Harness tab. Read-only: the harness manages this itself. What it captures After every workflow run, run-finalization.ts writes: Per-role metrics: how often each role succeeded, average cost, average duration. Recurring patterns: "Researcher agents have been failing on EU-domain queries 3 times in a row." Run log entry: one line per run with workflow name, status, cost, duration, what worked / didn't work. The harness later reads this to surface tips ("You've spent $40 on Opus in the last week; switching to Sonnet would have produced the same output for $5") and to drive meta-cognition. Meta-cognition Lives at ~/.cerevisor/memory/meta-cognition/ . Computed at end-of-run from a diff of harness-self snapshots. Two files: observations.{md,json} : typed observations like "role X drift", "blind spot Y persistent", "pattern Z reversal", "run-log recurring theme". harness-self-portrait.{md,json} : a compiled reflective summary of "what I have learned about working with you." Visible in Memory view → Reflection tab. Observation kinds Kind When emitted role_metric_drift A role's success rate dropped by ≥10% across runs. role_metric_recovery Recovered after a drift. role_new First-ever observation for a previously-unseen role. pattern_growth / pattern_stale / pattern_reversal Patterns in the run log changing over time. blind_spot_persistent Same failure mode in 3+ consecutive runs. blind_spot_resolved A previously-persistent blind spot stopped occurring. run_log_recurring_theme Themes appearing in multiple run logs. baseline First-ever observation (single emission on first run). The harness emits learned tips when a blind spot persists across 3+ runs. These show in the canvas as a badge on the affected agent or as a status-bar pill. The self-portrait The portrait has three sections: Learnings about user : 2-4 sentences describing what the harness has noticed about your preferences and patterns. Self-capabilities : 2-4 sentences on what the harness has been doing well lately. Enduring insights : up to 6 bullet-point insights that have repeated across runs. The portrait is refreshed on every run by the same LLM call that writes the meta-cognition narrative, so no extra round-trip. Toggling enrichment Two related settings in Settings → Memory : Setting Default Effect Meta-cognition enrichment enabled On Whether the optional LLM call runs to produce the narrative + portrait. Detector

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