Pattern 5: Concurrent Event-Driven Multi-Agent Loop. Coordinates multiple Claude sessions as OS threads sharing a common event bus and memory address space. Every loop cycle is a full improvement cycle: execute, eval against benchmark (KEEP/DISCARD), emit friction events during work, close with post_run_metrics, agent self-assessment survey saved to retrospectives, memory persistence, and Triple-Loop Retrospective trigger if friction threshold crossed. Four coordination topologies: turn-signal, fan-out, request-reply, triple-loop (Pattern D).
Pattern 5: Concurrent Event-Driven Multi-Agent Loop. Coordinates multiple Claude sessions as OS threads sharing a common event bus and memory address space. Every loop cycle is a full improvement cycle: execute, eval against benchmark (KEEP/DISCARD), emit friction events during work, close with post_run_metrics, agent self-assessment survey saved to retrospectives, memory persistence, and Triple-Loop Retrospective trigger if friction threshold crossed. Four coordination topologies: turn-signal, fan-out, request-reply, triple-loop (Pattern D).
Pattern 5 in the agent-loops taxonomy. Treats concurrent Claude sessions as OS threads
sharing a filesystem address space. The kernel event bus coordinates signals. Every cycle
includes real work, eval against benchmark, friction tracking, agent self-assessment survey,
post-run metrics, and memory persistence. The OS learns from every run.
Triple-Loop Architecture
There are two distinct Triple-Loop orchestration cycles operating at different scopes. Do not conflate them.
┌─────────────────────────────────────────────────────────┐
│ TRIPLE-LOOP ARCHITECT — OS Self-Improvement (this skill) │
│ │
│ os-improvement-loop evaluates and improves the OS │
│ workflows, protocols, agent coordination patterns, │
│ and this SKILL.md itself. │
│ │
│ Target: the OS machinery — ledgers, surveys, kernel, │
│ event bus, loop protocol. │
│ Eval gate: ORCHESTRATOR + PEER_AGENT run eval_runner.py │
│ on the OS skill being patched. │
│ Self-improvement: ORCHESTRATOR updates this SKILL.md │
│ when a confirmed protocol fix is found. │
└────────────────────┬────────────────────────────────────┘
│ spawns / governs
┌────────────────────▼────────────────────────────────────┐
│ TRIPLE-LOOP EXECUTOR — Individual Skill Improvement │
│ │
│ os-eval-runner + os-skill-improvement evaluate and │
│ improve a specific target SKILL.md (routing accuracy, │
│ trigger descriptions, example blocks). │
│ │
│ Target: a single skill's description and routing. │
│ Eval gate: os-eval-runner scores the target skill. │
│ Improvement: os-skill-improvement runs RED-GREEN-REFACTOR│
│ until score ≥ threshold. │
└─────────────────────────────────────────────────────────┘
Key distinction:
The OUTER loop asks: "Is the OS improvement process itself working correctly?"
The INNER loop asks: "Does this specific skill route and execute correctly?"
Triple-Loop Retrospective vs os-improvement-loop:Triple-Loop Retrospective (agent) is the
trigger/diagnostic layer — it analyzes friction events, identifies improvement targets,
and decides which Triple-Loop to invoke. os-improvement-loop (skill) is the execution
protocol that agents follow once a target has been identified. Do not conflate them.
User: "run a continuous improvement loop on the os-eval-runner skill"
ORCHESTRATOR reads last survey (notes INNER_AGENT flagged eval_runner.py flag confusion as
biggest friction). Writes strategy packet incorporating that fix. INNER_AGENT runs, emits
friction event when hitting the confusing flag, completes eval, saves survey noting the fix
worked. PEER_AGENT runs os-eval-runner independently, produces KEEP verdict with
score delta, saves survey noting zero friction. ORCHESTRATOR applies edit, runs post_run_metrics
(friction count dropped from 3 to 0), writes session log with before/after scores, promotes
fix to memory.md. No Triple-Loop Retrospective trigger needed — friction threshold not crossed.
User: "audit 3 skills in parallel"
ORCHESTRATOR dispatches 3 INNER_AGENTs via claim_task. Each emits friction events during work,
runs eval_runner.py, saves survey. ORCHESTRATOR collects all results, identifies lowest scorer,
writes correction packet. After correction cycle, runs post_run_metrics — 4 friction events
for same cause (wrong CLI syntax in eval_runner). Triggers Triple-Loop Retrospective Full Loop to patch
eval_runner documentation in the skill. Closes with session log and memory promotion.
User: "replace AGENT_COMMS.md with the event bus and track whether it's faster"
ORCHESTRATOR establishes bus, runs Pattern A turn-signal cycle, records round-trip latency.
INNER_AGENT and PEER_AGENT both complete post-run surveys noting any friction with polling syntax.
post_run_metrics emitted. Session log records latency delta vs AGENT_COMMS baseline.
Surveys compared — if both agents report same confusion point, Triple-Loop Retrospective patches SKILL.md.
Session Lifecycle Invariant: The OUTER loop owns session lifecycle. INNER loop work
(os-eval-runner, os-skill-improvement) never closes a session. A session is incomplete
until Phase 6 (os-memory-manager) is executed. An INNER loop that completes without running
Phase 6/7 has silently discarded its learnings.
Each Triple-Loop has its own eval targets, its own memory artifacts, and its own close protocol.
A session that runs INNER loop work must still close through the OUTER loop's Phase 6/7
(os-memory-manager + os-skill-improvement) to persist learnings and harden OS-level routing.
See assets/diagrams/triple-loop-learning-system.mmd for the full visual.
CRITICAL: Two-Tier Loop Model
Every loop cycle uses one of two tiers. Triple-Loop cycle is the default.
Use Standard Cycle only when the north star is regressing or explicitly requested.
Triple-Loop cycle (7 steps, ~30 min) -- default for every run
digraph fast_cycle {
rankdir=TB;
node [shape=box, style="rounded,filled", fillcolor=white, fontname=Helvetica];
edge [fontname=Helvetica, fontsize=10];
Orientation [label="1. Orientation\nRead: ledger, registry, last survey, friction patterns"];
Scenario [label="2. Document Test Scenario\nWrite hypothesis + AC to tests/ BEFORE emitting loop.start"];
Execution [label="3. Execute\nINNER_AGENT reads packet, does work, emits friction events immediately"];
Eval [label="4. Eval Against Baseline\nINNER_AGENT + PEER_AGENT run eval_runner.py independently\nKEEP if both accuracy AND F1 >= baseline"];
Verdict [label="KEEP / DISCARD?", shape=diamond, fillcolor=lightyellow];
Apply [label="5a. Apply (KEEP)\nApply change to canonical skill or artifact"];
Correction [label="5b. Correction Packet (DISCARD)\nWrite correction-{CID}.md (CRITICAL/MODERATE/MINOR)\nRe-assign to INNER_AGENT"];
LoopClose [label="6. Loop Close -- all 4 required, no exceptions\n6a: ledger + registry row 6b: surveys (all agents)\n6c: metrics + report 6d: log M/L-class issues as tasks"];
Trigger [label="7. Trigger Check\n3+ same-type friction events this cycle?\nFlag Triple-Loop Retrospective for Full Loop on next session start"];
Orientation -> Scenario -> Execution -> Eval -> Verdict;
Verdict -> Apply [label="KEEP"];
Verdict -> Correction [label="DISCARD"];
Apply -> LoopClose;
Correction -> Execution [label="re-cycle", style=dashed];
LoopClose -> Trigger;
}
Orientation -- ORCHESTRATOR reads improvement-ledger.md (score trend, pending Section 2
items) and the last registry row (what was recommended next).
Test scenario documented -- ORCHESTRATOR writes hypothesis + acceptance criteria to
context/memory/tests/[CYCLE_ID]_[TARGET].md BEFORE emitting task.assigned.
Execution -- INNER_AGENT reads strategy packet, does real work, emits friction events
immediately on uncertainty or wrong syntax.
Eval against baseline -- INNER_AGENT runs eval_runner.py. PEER_AGENT runs it
independently. KEEP if both accuracy AND F1 score >= baseline. DISCARD otherwise.
On BASELINE verdict (first run of a skill): record the score, do not apply or revert any
change, proceed to step 5.
Loop close (4 required actions -- all mandatory every Triple-Loop cycle):
6a. Ledger + Registry -- Append one row to ledger Section 1 (date, cycle ID, target,
scores, verdict). Update context/memory/tests/registry.md row to CLOSED-KEEP or
CLOSED-DISCARD. Full scenario file fill-in and ledger Section 2+3 are Standard Cycle only.
6b. Survey child agents -- INNER_AGENT and PEER_AGENT each complete the Post-Run
Self-Assessment Survey (references/memory/post_run_survey.md), save to
context/memory/retrospectives/survey_[DATE]_[TIME]_[AGENT].md, and emit survey_completed.
Even on Triple-Loop cycle, surveys are required -- they are the source of truth for what to improve next.
6c. Metrics + report -- Run post_run_metrics.py --correlation-id "$CID". If this is a
KEEP cycle, optionally run generate_report.py to update the progress chart. Update
temp/agent-agentic-os-review/HOW-TO-RESTART.md in UPSTREAM with any state changes
(new known bugs fixed, backlog items added, what-exists status changed).
6d. Log issues as tasks -- Any problem, opportunity, or improvement observed this cycle
that is M-class or L-class (requires thought or architecture) MUST be created as a task file
in tasks/backlog/ in UPSTREAM using the naming convention NNNN-[slug].md:
# Task NNNN: [Title]
## Objective
[what needs to change and why -- cite cycle ID and agent that observed it]
## Acceptance Criteria
[specific, testable definition of done]
## Notes
[options considered, links to backlog.md entry if one exists]
S-class issues (trivial, <5 min fix) can go directly into backlog.md without a task file.
The next available task number is the highest NNNN across all lanes in tasks/ + 1.
Check with: ls tasks/backlog/ tasks/todo/ tasks/in-progress/ tasks/done/ | grep -o '^[0-9]*' | sort -n | tail -1
6e. ORCHESTRATOR memory ownership -- ORCHESTRATOR is solely responsible for writing and
keeping current all of the following at loop close. No other agent owns these files.
File
Location
When written
improvement-ledger.md
LAB context/memory/
Every Triple-Loop cycle (Section 1). Standard Cycle adds S2+S3.
tests/registry.md
LAB context/memory/tests/
Every cycle -- row updated to CLOSED.
tests/[CID]_[TARGET].md
LAB context/memory/tests/
Before emit (scenario). Results filled in on Standard Cycle.
memory/YYYY-MM-DD.md
LAB context/memory/
Standard Cycle only (session log).
loop-reports/report_[CID].md
LAB context/memory/loop-reports/
Standard Cycle only.
memory.md
LAB context/
Standard Cycle -- promoted L3 facts via os-memory-manager.
HOW-TO-RESTART.md
UPSTREAM temp/agent-agentic-os-review/
Every cycle -- reflect state changes.
tasks/backlog/NNNN-[slug].md
UPSTREAM tasks/backlog/
When M/L-class issue is observed.
references/meta/backlog.md
UPSTREAM references/
When any issue is observed (S/M/L).
SKILL.md (this file)
UPSTREAM .agents/skills/os-improvement-loop/
When applicable -- if the loop produces a confirmed protocol improvement (step unclear, gap found, new requirement), ORCHESTRATOR updates this file before closing the cycle. Self-improvement of the loop protocol is a first-class output of every loop.
A cycle that produces a protocol fix but does not update this SKILL.md has not fully closed.
Cycle Completion Checklist — a Triple-Loop cycle is complete only when ALL of these exist:
Registry row updated to CLOSED (tests/registry.md)
At least one survey saved (context/memory/retrospectives/)
Metrics run (post_run_metrics.py --correlation-id "$CID")
Claude auto-memory reviewed and updated if warranted (memory/MEMORY.md) — see 4.9
loop.close event emitted
Missing any item = incomplete cycle. Do not start the next cycle until the checklist is done.
Trigger check -- if 3+ friction events of the same type this cycle, flag Triple-Loop Retrospective
for Full Loop at next session start. Read context/memory/improvement-ledger.md Section 3:
if the last two Trend values are both negative, emit north_star_regression event and trigger
Triple-Loop Retrospective immediately (do not wait for next session).
Emitting eval.result without completing steps 6a-6d and 7 is an incomplete Triple-Loop cycle.
Standard Cycle -- adds these steps after step 5, before step 6
Used when: north star completion rate declining, or explicitly requested.
These steps are NOT required on every run:
4.2 Surveys -- both PEER_AGENT and ORCHESTRATOR complete Post-Run Self-Assessment Survey,
save to context/memory/retrospectives/.
4.5 Loop report -- write context/memory/loop-reports/report_[CYCLE_ID].md with baseline
vs result table, survey summary, artifacts updated.
4.6 Test registry close -- fill Results section of scenario file, update registry.md
row to CLOSED, write recommended next test.
4.7 Ledger Section 2 + 3 -- Section 2: one row per friction item that generated a change
(with grep verification -- see improvement-ledger-spec.md). Section 3: north star row.
4.8 Memory promotion -- run os-memory-manager for L3 promotion.
When to Use This Pattern
Use when:
Two or more Claude sessions coordinating continuous improvement work
N skills, workflows, or artifacts to eval and improve in parallel
You want every cycle to produce measurable improvement and persistent memory
Do NOT use for:
Single-session work (use learning-loop or triple-loop instead)
Signal-only coordination with no eval, survey, or memory steps
Agent Roles
Role
Responsibility
ORCHESTRATOR
Orients, writes strategy packets, applies improvements on KEEP, owns git, runs metrics, closes all memory files, updates SKILL.md when protocol improvements are found
These events are counted by post_run_metrics.py at close and drive the Triple-Loop Retrospective
auto-trigger (3+ friction events of same type = Full Loop improvement automatically).
Bash Polling Pattern
poll_for_event() {
local AGENT=$1 ACTION=$2 CID=$3
for i in $(seq 1 30); do
EVENTS=$(python "$KERNEL_PY" read_events --agent "$AGENT")
MATCH=$(echo "$EVENTS" | python -c "
import sys, json
evs = json.load(sys.stdin)
hits = [e for e in evs if e.get('action') == '$ACTION'
and (not '$CID' or e.get('correlation_id') == '$CID')]
print(json.dumps(hits[0]) if hits else '')
")
if [ -n "$MATCH" ]; then echo "$MATCH"; return 0; fi
sleep 2
done
echo ""; return 1
}
Stage 1: Setup and Orientation
Goal: Every agent orients before any work begins. No agent starts cold.
New target? Before running any eval cycle on a target skill for the first time, use
os-eval-lab-setup to bootstrap the experiment dir. This deploys:
evals/evals.json — test prompts with should_trigger boolean schema (REQUIRED — legacy
expected_behavior string fields score 0.0 and will destroy accuracy)
evals/results.tsv — baseline ledger (written when you run evaluate.py --baseline)
references/program.md — your optimization goal, target score, and max iterations
Without this setup, evaluate.py will fail with exit code 2 (missing experiment structure).
ORCHESTRATOR reads (in order):
context/memory/improvement-ledger.md — cross-session OS-level trajectory per skill, survey-to-action trace, north star trend
<target-experiment-dir>/evals/results.tsv — per-experiment baseline and iteration history (written by os-eval-runner's evaluate.py); this is the authoritative score history for the specific target being improved
context/memory/tests/registry.md — what has been tested, what was recommended next
context/memory.md (L3 long-term facts)
Last session log: context/memory/YYYY-MM-DD.md
Last retrospective surveys: context/memory/retrospectives/ (most recent per agent)
context/events.jsonl last 100 lines for friction patterns from prior cycle
ORCHESTRATOR answers before writing any strategy packet:
What does the improvement ledger show for this target's score trajectory? (flat = try a different approach; declining = revert last change)
Is the north star completion rate regressing 2+ sessions in a row? (if yes, trigger Triple-Loop Retrospective before this cycle)
What does the test registry say was the recommended next test?
Has this hypothesis already been confirmed or falsified? (check registry — do not re-run)
Which survey friction items from prior cycles have not been acted on yet? (Section 2 gaps)
Confirm agents.json lists all participating agents.
ORCHESTRATOR documents the test scenario in context/memory/tests/[CYCLE_ID]_[TARGET_SLUG].md
per references/testing/test-registry-protocol.md — hypothesis, acceptance criteria, failure criteria,
prior results consulted, known weaknesses — BEFORE emitting loop.start.
Add row to context/memory/tests/registry.md with status IN PROGRESS.
MANDATORY GATE: ORCHESTRATOR must receive eval.result with KEEP/DISCARD verdict from
PEER_AGENT before applying any improvement or emitting orchestrator.decision. The
eval.result event carries the verdict AND the PEER_AGENT self-assessment reference.
Merging on task.complete alone is a protocol violation.
Stage 3: Mandatory Loop Content (Every Agent, Every Cycle)
INNER_AGENT Execution Obligation
Every time INNER_AGENT receives task.assigned, it MUST:
Read the strategy packet at the path in the event summary.
Execute the assigned work — edit target skill, workflow doc, or artifact.
Emit friction events immediately when hitting uncertainty, wrong syntax, or needing help.
Run the eval engine using the os-eval-runner canonical scripts.
The experiment dir must have been bootstrapped by os-eval-lab-setup first
(deploys evals/evals.json with should_trigger boolean schema, evals/results.tsv,
and references/program.md).
Option A — pure scorer (get JSON metrics, decide KEEP/DISCARD manually):
python ./scripts/eval_runner.py --skill path/to/target/
# Pass the FOLDER path, not a file. Output: JSON with accuracy + F1 scores.
See os-eval-runner Troubleshooting section for exit code reference, keywords footgun,
and 4-character word floor.
If DISCARD: revert edit, note failure in output file, emit task.complete --status fail.
Write output to handoffs/out-${CID}.md.
Complete the Post-Run Self-Assessment Survey (see Stage 4.2).
Emit task.complete including score, output path, and survey path in summary.
PEER_AGENT Eval Obligation
Every time PEER_AGENT receives signal.wakeup for eval, it MUST:
Read the INNER_AGENT output file at the path in the wakeup summary.
Run evaluate.py independently — do NOT read the score from the INNER_AGENT event.
Use evaluate.py (loop gate) for KEEP/DISCARD; it compares against results.tsv baseline
automatically and returns exit code 0=KEEP or 1=DISCARD.
python ./scripts/evaluate.py --skill path/to/target/
# Note: PEER_AGENT runs this from its OWN session independently.
DISCARD if exit code 1. Note: results.tsv is the authoritative per-experiment baseline
(written by os-eval-runner). The improvement-ledger.md tracks cross-cycle OS-level trajectory.
Complete the Post-Run Self-Assessment Survey (see Stage 4.2).
Emit eval.result with KEEP/DISCARD verdict, score delta, and survey path:
Every agent that performed work this cycle MUST complete the Post-Run Self-Assessment Survey
(references/memory/post_run_survey.md). Answer every section — do not skip.
human_interventions — count of human rescues this cycle
workflow_uncertainty — count of uncertainty friction events
missed_steps — count of skipped required steps
cli_errors — count of wrong CLI syntax errors
friction_events_total — total friction events
hook_errors — count from context/memory/hook-errors.log
4.4 Write Session Log
ORCHESTRATOR writes context/memory/YYYY-MM-DD.md:
# Session Log: YYYY-MM-DD (Cycle: CYCLE_ID)
## Summary
[What was improved, which skills/workflows were modified]
## Eval Results
- Target: [skill or artifact]
- Score before: [baseline from results.tsv]
- Score after: [new score]
- Verdict: KEEP / DISCARD
- Gaps remaining: [from PEER_AGENT survey]
## Metrics (from post_run_metrics.py)
- Human interventions: N
- Friction events: N
- CLI errors: N
- Hook errors: N
## Agent Surveys
- INNER_AGENT: retrospectives/survey_DATE_TIME_INNER_AGENT.md
- PEER_AGENT: retrospectives/survey_DATE_TIME_PEER_AGENT.md
- Top recommendation: [single most impactful change from surveys]
## Skills / Workflows Updated
- [skill name]: [what changed and why]
## Open Items
- [ ] [Gaps flagged CRITICAL or MODERATE in surveys for next cycle]
4.5 Loop Report (Every Cycle — Published Before Memory Close)
ORCHESTRATOR writes a Loop Report before running os-memory-manager. This is the
cycle's official record. Save to context/memory/loop-reports/report_[CYCLE_ID].md:
# Loop Report: [CYCLE_ID] — [YYYY-MM-DD HH:MM]
## Agent Summaries
### ORCHESTRATOR
[2-3 sentence summary: what was assigned, what decision was made, what was applied]
### INNER_AGENT
[2-3 sentence summary: what was executed, what score was produced, what friction was hit]
### PEER_AGENT
[2-3 sentence summary: eval run, verdict, gaps identified, self-assessment headline]
## Baseline vs Result
| Metric | Before | After | Delta |
|--------|--------|-------|-------|
| Eval score | [results.tsv baseline] | [new score] | [+/-] |
| Friction events | [prior cycle count] | [this cycle count] | [+/-] |
| Human interventions | [prior] | [this cycle] | [+/-] |
## Survey Response Summary
- INNER_AGENT biggest friction: [one line from survey qualitative section]
- PEER_AGENT biggest friction: [one line from survey qualitative section]
- ORCHESTRATOR biggest friction: [one line from survey qualitative section]
- Top improvement recommendation: [the single most impactful change cited across surveys]
## Artifacts Updated This Cycle
- [ ] Skill updated: [path] — [what changed]
- [ ] Script updated: [path] — [what changed]
- [ ] Hook updated: [path] — [what changed]
- [ ] Memory updated: context/memory/YYYY-MM-DD.md
- [ ] L3 promoted: [N facts to context/memory.md]
- [ ] Survey saved: retrospectives/survey_[DATE]_[AGENT].md (each agent)
## Status
- [ ] Results saved to memory: YES / NO
- [ ] Triple-Loop Retrospective triggered: YES (cause: [friction pattern]) / NO
Recommended next test (hypothesis, target, design improvement)
Update context/memory/tests/registry.md row from IN PROGRESS to CLOSED with verdict.
If the hypothesis was Confirmed: promote the finding to context/memory.md L3 with
a dedup ID and a reference to the cycle ID as evidence.
If the hypothesis was Falsified: add a "DO NOT RE-TEST" entry to context/memory.md
with the cycle ID, so future cycles do not waste time re-running it.
If Inconclusive: note what additional data would be needed and what to change in
the test design before retrying.
4.7 Update Improvement Ledger (Every Cycle — No Exceptions)
After the test registry update, ORCHESTRATOR appends to context/memory/improvement-ledger.md.
This is the longitudinal record that makes the cycle of improvement visible over time.
See references/memory/improvement-ledger-spec.md for the full format and writing protocol.
Section 1 — Eval Score Progression (one row, every cycle):
After running: "Progress chart updated at context/memory/reports/progress_[TIMESTAMP].png. Want to see the summary?"
Only display the chart/summary if the user says yes — never auto-display.
If north star regresses 2 consecutive sessions: log a warning in the ledger and invoke
Triple-Loop Retrospective in Full Loop mode at the start of the next session. Do not wait for the
friction event threshold — a completion rate decline is a systemic signal.
4.8 Promote to Long-Term Memory
Run os-memory-manager to evaluate session log entries for L3 promotion:
Ephemeral state -> SKIP
System facts, architectural decisions, new conventions -> PROMOTE with dedup ID
Use <SUPERSEDE old_id=NNN> if overwriting a prior fact
4.9 Update Claude Auto-Memory (MEMORY.md)
After os-memory-manager runs, review the session for facts worth persisting in Claude's
cross-session auto-memory (memory/MEMORY.md in the project memory directory).
This is distinct from os-memory-manager (which promotes facts into context/memory.md
inside the lab). Auto-memory persists across all future conversations — it is the agent's
durable long-term knowledge about the user, project, and working patterns.
What belongs here (not in os-memory-manager):
New non-obvious user preferences or feedback on how to collaborate
Structural decisions made this session (e.g. skill moved, plugin renamed, pattern adopted)
Surprising findings that should inform future sessions (e.g. sweep results, failed approaches)
Project state changes that will be non-obvious next session
What does NOT belong here (use os-memory-manager instead, or skip):
Code patterns, file paths, architecture derivable by reading the repo
Temporary/ephemeral task state
Anything already in CLAUDE.md
Procedure:
Read memory/MEMORY.md — check for stale entries that need updating
For each non-obvious fact worth preserving: write a new memory file or update an existing one
Add/update pointer in memory/MEMORY.md
Checklist — ask before closing:
Did the user give explicit or implicit feedback on my approach? → feedback_*.md
Were structural decisions made (skills moved, plugins renamed, patterns adopted)? → project_*.md
Were there surprising findings that will matter next session? → project_*.md or feedback_*.md
Did I learn anything about what the user values or how they work? → user_*.md
If all four answers are "no", skip this step. Otherwise, update memory before emitting loop.close.
Note: The most common omission is feedback memory — if the user corrected an approach or
confirmed a non-obvious choice worked, that should be saved. Watch for it.
4.10 Triple-Loop Retrospective Trigger Check
After metrics are collected, ORCHESTRATOR checks the friction threshold:
FRICTION=$(python -c "
import json
events = [json.loads(l) for l in open('${CLAUDE_PROJECT_DIR}/context/events.jsonl') if l.strip()]
# Count friction events by cause this cycle
from collections import Counter
causes = Counter(e.get('summary','').split('cause:')[-1].split()[0]
for e in events if e.get('type') == 'friction' and e.get('correlation_id') == '$CYCLE_ID')
print(max(causes.values()) if causes else 0, list(causes.most_common(1)))
")
If any single friction cause appears 3+ times this cycle: invoke Triple-Loop Retrospective in
Full Loop mode automatically. Pass the friction pattern and relevant survey excerpts.
The learning loop will run root cause analysis (Kernel/RAM/Stdlib layer), propose a fix,
run the eval-gate, and apply the improvement before the next cycle begins.
Autonomous Workflow Completion Rate: percentage of cycles that complete the full
loop.start -> task.complete -> eval.result -> orchestrator.decision -> loop.close
chain without human rescue. Track this in the session log. Goal: increase every cycle.
Supporting metrics (all tracked by post_run_metrics.py, goal: decrease every cycle):
Human Interventions
Workflow Uncertainty events
Missed Step Rate
CLI Error Rate
Friction Events Total
References
triple-loop protocol - strategy packet format, correction packets, verification (inlined from agent-loops)
