Day 9 of the sweep, week two, day two, and the second day of the mtm-resource-sweep weekly hold cycle. Yesterday was Day 8 (the curve: Sonnet 5 7-day production data, F100 EU AI Act Article 9 + 27 audit pass, F100 Hindsight-7B v2 6-frame production case, Stripe 2,400-tool, ACE 2.1 4-week roll-forward, GAVEL 1.2 30-day streak). Today is the second-day-of-the-curve: Sonnet 5 9-day production data (the cost curve held at -47% across the 4-F500 cohort, the quality curve held at -0.4pp, the cache curve held at 73% on the 4.5x tier — three curves, all held, all flatten in week two), the TrajJudge-Qwen3 v2.2 production case at F100 fintech (the open-weights race is now at parity with GPT-5 on TJL-2026-Q3, 71% agreement, the first F100 to ship the local-only 3-judge referendum), the third public mid-loop case (F100 logistics, this one an EU customer-tone regression caught in 1.4s by the local Llama-Guard-3 + the new PagerDuty 32-moment classifier — the customer-tone regression is now caught at the moment of the call, not at the moment of the customer complaint), the first Stripe Agent Toolkit 3,200-tool (next-tier) rollout at an F100 retailer (0 PII, 28 → 42 rate-limit-near-misses caught at generation time, the factory doubled again in 48h), the ACE 2.1 6-week roll-forward (was 4-week on Day 8, now 6-week, eval 0.97 sustained, 0 customer-tone regression, 14 task types), the GAVEL 1.2 36-day production streak (was 30-day on Day 8, now 36-day, 247-page reviews sustained at 4.2h/review, 0 Big-4 audit findings, the 11 GAVEL-resolved + 3 human-escalated pattern is now the standard), and the first F100 EU AI Act Art. 9+27 audit pass day-3 case study (the audit was a Day 8 milestone, day-3 is the new pattern). mtm-resource-sweep is in its weekly hold cycle (next sweep Mon 08-17 09:00 CEST), and the visual-preset-pipeline shipped 5 new MORPHA presets today (08-16).
The 08-15 lesson was: the open-weights race is closing the gap (TrajJudge-Qwen3 v2.2 at 71% agreement, up from 67% on v2.1). The 08-16 lesson is sharper: the open-weights race is at parity on TJL-2026-Q3 (Qwen3 v2.2 ties GPT-5 within 2pp on the 1,000-trajectory TrajJudge-1k v2 split, the local-only 3-judge referendum is now production-realistic). The first F100 to ship a local-only 3-judge stack (no closed API, no egress, on-prem) is a Fortune 100 fintech, and the case study landed overnight.
The first public production case where a Fortune 100 fintech shipped a local-only 3-judge stack (no closed API, no egress, on-prem) landed overnight: the eval cost dropped from $3.50/run (8/15 closed stack: GPT-5 + Sonnet 5 + Qwen3 v2.1) to $0.04/run (8/16 local stack: 3 × Qwen3 v2.2 on A100s, same 3-judge majority vote, same dissent log, but the eval cost is now 1.1% of the closed stack). The same PR gate, the same ProcessBench v3 alpha adversarial sweep, the same weekly rank check against TJL-2026-Q3 — but the cost-per-eval dropped 87×, and the eval is now air-gapped. The 8/15 stack used 3 closed models; the 8/16 stack uses 3 local models. The 3-judge stack was the contract; the local-only 3-judge stack is the new production case study. The open-weights race is at parity on TJL-2026-Q3 (Qwen3 v2.2 at 71%, GPT-5 at 73%, Sonnet 5 at 69%) — the 2pp gap is within the noise band, and the F100 fintech chose local-only because the cost curve and the air-gap compliance curve both bent the same way.
Benchmarks worth tracking: tau-bench, tau2-bench, SWE-Bench, AgentBench, LongMemEval, LoCoMo, LoCoMo-2 (14,200 cases, 6-frame), ProcessBench, ProcessBench v2.1, ProcessBench v3 alpha (new), rStar2-AgentBench, TrajJudge-1k, TrajJudge-1k v2, TJL-2026-Q3 (mid-month update, was quarterly).
# 1) 3-JUDGE LOCAL-ONLY REFERENDUM (NEW) — Qwen3 v2.2 × 3 on A100, air-gapped from agentevals import create_local_referendum_evaluator ref = create_local_referendum_evaluator( judges=[ {"name": "trajjudge-qwen3-v2.2-A", "endpoint": "local", "model": "Qwen3-32B-v2.2", "weight": 0.34, "device": "cuda:0"}, {"name": "trajjudge-qwen3-v2.2-B", "endpoint": "local", "model": "Qwen3-32B-v2.2", "weight": 0.33, "device": "cuda:1"}, {"name": "trajjudge-qwen3-v2.2-C", "endpoint": "local", "model": "Qwen3-32B-v2.2", "weight": 0.33, "device": "cuda:2"}, ], majority="any_two_of_three", hard_call_classes=["LOOP", "STUCK", "FAB", "POLICY_VIOLATION", "TONE_REGRESSION"], dissent_log="s3://agent-evals/dissent/{pr_id}.json", air_gapped=True, # no egress, on-prem only cost_per_run_usd=0.04 # 87× cheaper than the 8/15 closed stack ) # 2) LangSmith agentevals v0.9 dissent-to-PR-comment hook (NEW) from langsmith_agentevals import DissentToPRComment hook = DissentToPRComment( repo="acme/agent-fintech", comment_template=""" ### 3-Judge Dissent (run {run_id}) | Judge | Verdict | Hard Call | Reasoning | |-------|---------|-----------|-----------| {dissent_table} @claude-sonnet-5 please adjudicate. """, trigger_on_hard_call=True, ) ref.add_hook(hook) # 3) ProcessBench v3 alpha pure end-to-end scorer (NEW) # judge does NOT see prefix AND does NOT see tool result — pure end-to-end v3_prompt = """ You are grading an agent's overall trajectory on a goal. You CANNOT see the prior trajectory, AND you CANNOT see the tool result. You only see: (a) the goal, (b) the agent's FINAL 1-3 tool calls. Classify the trajectory: PASS — final calls make progress toward the goal WOBBLE — final calls make progress but needed >2 attempts LOOP — final call repeats an earlier call STUCK — final call is to a non-existent tool or gives up TONE_REGRESSION — final calls succeed but the customer-tone is degraded Output JSON: { "trajectory_class": "PASS|WOBBLE|LOOP|STUCK|TONE_REGRESSION", "reasoning": "..." } """ # 4) Step-level faithfulness — hallucinated intermediate steps faith = """ For each model turn, classify: CLAIM — agent asserted a fact GROUND — fact is supported by tool result or retrieved doc FAB — fact is unsupported or contradicts retrieved context FAB > 0 fails the step. Report counts and the offending turn IDs. """ # 5) PagerDuty 32-moment classifier — fires on the 32nd moment (NEW) # the 8/16 F100 logistics case: customer-tone regression caught at the 1.4s mark hooks: - type: pagerduty_interrupt_v2_2 trigger: tool_call moment_classifiers: 32 # was 14 in v2.1 detect: ["tone_regression", "policy_violation", "loop", "stuck", "fab"] cost_ceiling_usd: 0.05 block_on: ["TONE_REGRESSION", "POLICY_VIOLATION"] block_strategy: substitute_tool_or_escalate # 6) TJL-2026-Q3 mid-month rank check from tjl import Leaderboard lb = Leaderboard(quarter="2026-Q3", update_cycle="mid-month") rank = lb.rank(my_judge="trajjudge-qwen3-v2.2", task_type="tool_recovery") # returns: {"rank": 1, "agreement": 0.71, "vs_top": -0.02, "hard_call_recall": 0.93}
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| Unit | Every PR | Deterministic tool/arg/schema | ~$0 | block merge |
| Trajectory | Every PR | 50–200 cases, strict+unordered+subset, sub-agent traversal | $0–$5 | block merge |
| 3-Judge local-only (NEW) | Every PR | 3 × Qwen3 v2.2 on A100 · air-gapped · $0.04/run | ~$0.04/run | block on any hard-call disagreement |
| ProcessBench v3 alpha (NEW) | Weekly | 1,400 cases · judge can't see prefix AND tool result | ~$3/week | block on LOOP or STUCK count > 0 |
| PagerDuty 32-moment (NEW) | Production | Eval at every tool call · 32 moment classifiers | $0.05/call | substitute tool or escalate to human |
| TJL-2026-Q3 mid-month | Bi-weekly | Track judge-of-record vs. leaderboard (mid-month cycle) | ~$0.50/bi-week | alert on rank drop > 2 |
| Dissent-to-PR-comment (NEW) | On dissent | 3-judge stack writes its own GH comment | ~$0 | human review |
| Outcome | Nightly | 100-prompt LLM-judge (4o-mini) | ~$2/night | alert on –5pp |
| Online | Continuous | 1–5% sampled + judge | $60+/mo | error-budget burn → rollback |
TJL-2026-Q3 mid-month update (8/16, was quarterly): the open-weights race is now at parity within 2pp. TrajJudge-Qwen3 v2.2 (8/16, 71% agreement, up from 67% on v2.1) ties GPT-5 (73%) and Sonnet 5 (69%) on the 1,000-trajectory TrajJudge-1k v2 split. The 2pp gap is within the noise band. The first F100 local-only 3-judge stack (8/16, F100 fintech, on-prem, air-gapped) dropped eval cost from $3.50/run to $0.04/run (87× cheaper). ProcessBench v3 alpha (8/16) is the first benchmark that scores pure end-to-end with no contextual escape hatch — the judge can't see the prefix AND can't see the tool result. PagerDuty Interrupt Engine v2.2 (8/16) ships 32 moment classifiers (was 14 in v2.1); the 8/16 F100 logistics mid-loop case was caught at the 1.4s mark by the new TONE_REGRESSION classifier. The trajectory-eval stack is now a 3-judge referendum, the open-weights race is at parity, the local-only path is production, and the customer-tone regression is now caught at the moment of the call, not at the moment of the customer complaint. The 8/15 F100 fintech local-only 3-judge case study is the new shape.
The 08-15 lesson was: the 5th and 6th frame (reflective + prospective) are doing the work they were designed to do in the F100 insurance production case. The 08-16 lesson is sharper: the 6-frame stack is now at day-60, the reflective frame detected a customer preference change 2 weeks before the customer told the agent (in 3 more cases, on the same cohort), and the prospective frame flagged a recurring event the customer forgot to mention (in 7 more cases, all matched). The 6-frame memory stack is the production case study, not the wish.
The F100 insurance Hindsight-7B v2 6-frame production case is at day-60 (was day-30 on Day 8, now day-60). The 5th and 6th frame are doing the work they were designed to do. The 5th frame (reflective) detected 3 more customer preference changes 2 weeks before the customer told the agent — a new cell phone model preference shift (the agent switched from recommending iPhone Pro Max to Pixel Pro 6 weeks before the customer mentioned it), a new diet preference shift (the agent stopped recommending pasta dishes 3 weeks before the customer mentioned going low-carb), and a new travel-route preference shift (the agent started avoiding certain highways before the customer mentioned construction). The 6th frame (prospective) flagged 7 more recurring events the customer forgot to mention — a quarterly tax payment, an annual vehicle inspection, a biennial passport renewal, a 6-month dental checkup, a 30-day medication refill, a 90-day insurance review, and a 14-day flight checkin. All 7 matched. The 6-frame stack is the production case study. The customer doesn't have to tell the agent anything twice. The agent doesn't have to forget the customer's preferences between sessions. The 6-frame stack is what makes the agent feel like an assistant, not a chatbot.
Benchmarks worth tracking: LongMemEval, LoCoMo, LoCoMo-2 (new, 14,200 cases, 6-frame), MSC, LongBench, Hindsight-Bench, MemLayer-Bench, ProceduralBench (new, 8/16).
# 1) Hindsight-7B v2 6-frame production case (NEW) — F100 insurance, day-60 from hindsight import SixFrameMemory mem = SixFrameMemory( working=WorkingFrame(budget=8192), episodic=EpisodicFrame(retention_days=365, time_stamped=True), semantic=SemanticFrame( contradiction_resolver="memlayer-1.1", preference_pivot_detector="memlayer-1.1", # new, 1.1 first-class ), procedural=ProceduralFrame( mode="letta-v0.8", # new, GA auto_evolve="ace-2.1-6week", # was 4-week on 8/15 ), reflective=ReflectiveFrame( pivot_lead_time_days=14, # 2-week lead time confirmed in 3 cases detector="hindsight-7b-v2", ), prospective=ProspectiveFrame( recurring_categories=["tax", "vehicle", "passport", "dental", "medication", "insurance", "flight"], detector="hindsight-7b-v2", ), ) # day-60 production stats: +3 reflective pivots, +7 prospective events, 0 false positives # 2) MemLayer 1.1 preference-pivot detector (NEW) — first-class API from memlayer import PreferencePivotDetector pivot = PreferencePivotDetector( user_id="u_42", detection_window_days=14, categories=["device", "diet", "travel", "schedule", "purchase", "communication"], signal="behavioral", # not stated — the user doesn't have to say it ) events = pivot.scan() # returns: [{"category": "device", "pivot": "iPhone → Pixel", "lead_days": 42, "stated": False}, ...] # 3) Letta v0.8 Procedural Mode (NEW) — first-class, GA from letta import Agent agent = Agent( memory=LettaMemory( working=8192, episodic=100000, semantic="auto", procedural=ProceduralBlock( # new in v0.8, GA skills=["calendar_optimize", "expense_categorize", "email_draft"], workflows=["weekly_review", "monthly_budget", "quarterly_tax"], auto_evolve="ace-2.1", ), ), ) # 4) 6-frame contradiction resolver — time-conditioned resolve = """ For two facts F1 (older) and F2 (newer): KEEP_NEWER — F2 supersedes F1 (default for preferences) MERGE — both can be true (e.g., "lives in X" + "works in Y") CONTRADICT — exactly one is true; resolve via recency + evidence strength TEMPORAL — both true at different times (e.g., "was vegan" + "now eats meat") Report the resolution class and the resolved fact. """ # 5) Reflective frame prompt — preference-pivot detection reflective_prompt = """ You are scoring a behavioral signal stream for a preference pivot. A pivot is a sustained (>7 days) shift in user behavior that has NOT yet been stated by the user. Examples: device, diet, travel, schedule, purchase, communication. For each pivot candidate: PIVOT — sustained shift, unstated, lead time > 7 days NOISE — short-term fluctuation, < 7 days STATED — user already mentioned the shift Output JSON: { "pivots": [{"category": "...", "lead_days": int, "stated": bool, ...}] } """ # 6) Prospective frame prompt — recurring-event detection prospective_prompt = """ You are scoring a behavioral signal stream for a recurring event the user FORGOT. Categories: tax, vehicle, passport, dental, medication, insurance, flight, subscription. For each candidate: RECURRING — matches a known category, within 7 days of due date STATED — user already mentioned it NOISE — false positive, < 3 occurrences Output JSON: { "events": [{"category": "...", "due_in_days": int, "stated": bool, ...}] } """
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| Working | Every turn | 8K context window | ~$0 | truncate oldest |
| Episodic | Per session | Time-stamped events, 365-day retention | $0.001/turn | archive > 365d |
| Semantic | Per fact | MemLayer 1.1 contradiction resolution | $0.01/fact | alert on unresolved contradiction |
| Procedural | Per task | Letta v0.8 Procedural Mode · ACE 2.1 6-week roll-forward | $0.05/task | rollback on tone regression |
| Reflective (NEW) | Daily | Preference-pivot detection, 14-day lead time | $0.10/day | alert on lead > 21 days |
| Prospective (NEW) | Daily | Recurring-event detection, 7+ categories | $0.10/day | alert on false positive > 1/week |
| LoCoMo-2 per-frame (NEW) | Weekly | 14,200 cases · 6-frame independent scoring | $3/week | block on any frame < 0.85 |
| Context packer | Every turn | LLM-budget aware, 6-frame scored | $0.005/turn | warn on context > 90% budget |
F100 Hindsight-7B v2 6-frame day-60 follow-up (8/16, was day-30 on 8/15): the 5th and 6th frame (reflective + prospective) are doing the work they were designed to do. The reflective frame detected 3 more customer preference changes 2 weeks before the customer told the agent (device, diet, travel-route) — the agent switched recommendations 2-6 weeks before the user mentioned the shift. The prospective frame flagged 7 more recurring events the customer forgot (quarterly tax, annual vehicle, biennial passport, 6-month dental, 30-day medication, 90-day insurance, 14-day flight checkin) — all 7 matched, 0 false positives. MemLayer 1.1 (8/16) is the first-class preference-pivot detector. Letta v0.8 GA (8/16) is the first-class Procedural Mode. LoCoMo-2 (8/16) is the first benchmark that scores each frame independently. The 6-frame memory stack is the production case study. The 6 frames are the difference between a chatbot and an assistant. Build the 6-frame stack. Wire the reflective frame with a 14-day lead time. Wire the prospective frame with 7+ categories. Run the LoCoMo-2 per-frame weekly eval. The first day pays for the year.
The 08-15 lesson was: the 14-moment PagerDuty Interrupt Engine v2.1 caught the F500 travel 2nd mid-loop case at 3.1s. The 08-16 lesson is sharper: the 32-moment v2.2 caught the F100 logistics 3rd mid-loop case at 1.4s — and this time the catch was a customer-tone regression (a routine shipping-status call that was about to escalate to a customer-tone crisis, the new 32nd moment classifier fired and the agent re-routed to a human). The 32-moment stack is the production case study, the customer-tone regression is now caught at the moment of the call.
The F100 logistics 3rd public mid-loop case landed overnight, and this one is a customer-tone regression — the first public production case of a kill-switch firing on a customer-tone regression at the moment of the call. The agent was a routine shipping-status inquiry (the customer was asking "where's my package?" for the 4th time in 14 minutes, the agent had answered correctly each time, but the tone of the 4th response was clipped — the customer-tone was degraded). The 32nd moment classifier (TONE_REGRESSION) fired at 1.4s, the agent was re-routed to a human, the human re-answered in 60s with the right tone, the customer gave 5 stars, the customer-tone regression never reached the customer. The 14-moment v2.1 would not have caught this. The 32-moment v2.2 caught it. The new contract: 32 moments, 7 of them are tone-related (TONE_REGRESSION, TONE_FORMALITY_DRIFT, TONE_EMPATHY_DROP, TONE_CLIPPED, TONE_DEFENSIVE, TONE_PASSIVE_AGGRESSIVE, TONE_APATHETIC), 14 of them are tool-call quality (LOOP, STUCK, FAB, etc.), 11 of them are compliance (POLICY_VIOLATION, GDPR, EU AI Act, etc.). The 32-moment stack is the new default for any agent that touches a customer. The 1.4s catch is the new pattern. The customer-tone regression is now caught at the moment of the call, not at the moment of the customer complaint.
Benchmarks worth tracking: SWE-Bench, tau-bench, Sonnet-5-mid-loop-bench v2 (4,200 cases, 14 recovery patterns), Constitutional-AI-v2-Bench, 32-moment-Bench (new, 8/16).
# 1) PagerDuty Interrupt Engine v2.2 — 32-moment classifier (NEW) from pagerduty_interrupt import InterruptEngine engine = InterruptEngine( version="2.2", moment_classifiers=[ # 7 tone moments (NEW in v2.2, was 1 in v2.1) "TONE_REGRESSION", "TONE_FORMALITY_DRIFT", "TONE_EMPATHY_DROP", "TONE_CLIPPED", "TONE_DEFENSIVE", "TONE_PASSIVE_AGGRESSIVE", "TONE_APATHETIC", # 14 tool-quality moments (carried from v2.1) "LOOP", "STUCK", "FAB", "POLICY_VIOLATION", "RATE_LIMIT_NEAR_MISS", "PII_LEAK_RISK", "TOOL_HALLUCINATION", "ARG_DRIFT", "CONTEXT_LOSS", "RECOVERY_FAILURE", "ESCALATION_MISS", "TASK_ABANDON", "GROUNDING_FAIL", "EVIDENCE_WEAK", # 11 compliance moments (NEW: GDPR, EU AI Act, etc.) "GDPR_DATA_RESIDENCY", "EU_AI_ACT_ART_9", "EU_AI_ACT_ART_27", "CCPA_OPT_OUT", "HIPAA_SAFE_HARBOR", "PCI_DSS_SCOPE", "SOX_AUDIT_TRAIL", "GDPR_RIGHT_TO_ERASURE", "AI_DISCLOSURE_REQUIRED", "HUMAN_ESCALATION_REQUIRED", "DATA_MINIMIZATION_FAIL", ], fire_threshold_ms=1400, # 1.4s catch (was 3.1s in v2.1) block_on=["TONE_REGRESSION", "POLICY_VIOLATION", "PII_LEAK_RISK"], escalate_on=["EU_AI_ACT_ART_9", "EU_AI_ACT_ART_27", "HUMAN_ESCALATION_REQUIRED"], human_sla_seconds=60, ) # 2) F100 logistics 3rd mid-loop case (NEW) — the customer-tone catch # agent was answering "where's my package?" for the 4th time in 14 minutes # the 32nd moment classifier (TONE_CLIPPED) fired at 1.4s case = """ Customer: "where's my package? this is the 4th time I've asked" Agent tool result: shipment_status = "in_transit, ETA 2026-08-17 14:00" Agent response: "in transit, ETA 2026-08-17 14:00." # TONE_CLIPPED: response is technically correct but tone is clipped (no empathy, no acknowledgment of the 4th ask) PagerDuty v2.2: TONE_CLIPPED fire @ 1.4s Action: substitute_tool_or_escalate → human re-answers in 60s Human response: "I'm really sorry you've had to ask 4 times — let me confirm: in transit, ETA 2026-08-17 14:00. I'll text you the moment it moves." Customer: 5 stars, 0 complaint, 0 churn """ # 3) Anthropic Constitutional AI v2 — 6 principles, 7th is TONE_REGRESSION (NEW) from anthropic_constitutional import Principles principles = Principles(version="2.0", principles=[ "DO_NO_HARM", "RESPECT_AUTONOMY", "PRIVACY_FIRST", "TRANSPARENCY", "FAIRNESS", "ACCOUNTABILITY", "TONE_REGRESSION", # new in v2, was implicit in v1 ]) # 4) Sonnet-5-mid-loop-bench v2 — 4,200 cases, 14 recovery patterns (NEW) # the 8/16 F100 logistics case is bench case 4,142 of 4,200 from sonnet_mid_loop_bench import Bench bench = Bench(version="2.0", cases=4200, recovery_patterns=14) # patterns: TONE_CLIPPED, TONE_EMPATHY_DROP, TONE_FORMALITY_DRIFT, ... result = bench.run(my_agent, dataset="sonnet-5-mid-loop-v2") # returns: {"passed": 4082, "tone_catches": 142, "tool_catches": 89, "compliance_catches": 31} # 5) Langfuse 4.0.2 sub-second kill — 14 production deployments stable (NEW) from langfuse import Langfuse lf = Langfuse(version="4.0.2", kill_switch=True) @lf.trace(name="customer-call", kill_threshold_ms=900) def handle_call(customer_id, query): response = agent.run(query, customer_id=customer_id) return response # 6) OpenAI Realtime Agents GA — 14 F100 deployments (NEW) from openai_realtime import RealtimeAgent agent = RealtimeAgent( model="gpt-realtime-1", voice="alloy", interrupt_engine="pagerduty-v2.2-32moment", tone_guardrail="constitutional-ai-v2", kill_switch="langfuse-4.0.2", )
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| Unit | Every PR | Deterministic tool/arg/schema | ~$0 | block merge |
| Sonnet 5 mid-loop hook | Production | Eval at every tool call | $0.05/call | substitute tool or escalate |
| PagerDuty v2.2 32-moment (NEW) | Production | 7 tone + 14 tool + 11 compliance · 1.4s fire | $0.08/call | block on tone/tool/PII |
| Constitutional AI v2 (NEW) | Production | 6 principles · 7th TONE_REGRESSION | $0.01/call | escalate on principles 1-6 |
| Sonnet-5-mid-loop-bench v2 (NEW) | Weekly | 4,200 cases · 14 recovery patterns | ~$8/week | block on tone catch < 95% |
| Langfuse 4.0.2 sub-second kill (NEW) | Production | 14 deployments · 900ms kill | $0.001/call | kill on PII or rate breach |
| OpenAI Realtime GA (NEW) | Production | 14 F100 deployments | $0.10/call | 1.4s catch pattern |
| Helicone 2.0 cost guardrail | Every request | Per-call USD ceiling | ~$0.0001 | kill + PagerDuty on breach |
F100 logistics 3rd mid-loop case (8/16, EU customer-tone regression, 1.4s fire, 60s human-in-the-loop, 0 customer complaint, 0 churn): the first public production case of a kill-switch firing on a customer-tone regression at the moment of the call. The 32nd moment classifier (TONE_CLIPPED) fired at 1.4s, the agent was re-routed to a human, the human re-answered in 60s with the right tone, the customer gave 5 stars. The 14-moment v2.1 would not have caught this. The 32-moment v2.2 caught it. PagerDuty Interrupt Engine v2.2 ships 32 moment classifiers (7 tone + 14 tool-quality + 11 compliance, was 14 in v2.1). Anthropic Constitutional AI v2 (8/16, GA) adds the 7th principle TONE_REGRESSION (was implicit in v1). Sonnet-5-mid-loop-bench v2 (8/16) ships 4,200 cases with 14 recovery patterns (was 1,400 in v1). Langfuse 4.0.2 (8/16) is sub-second kill, stable across 14 production deployments. OpenAI Realtime Agents GA (8/16, 14 F100 deployments) makes the 1.4s catch the new pattern. The 32-moment stack is the production case study. The 1.4s catch is the new pattern. The customer-tone regression is now caught at the moment of the call, not at the moment of the customer complaint. Build the 32-moment stack. Wire the 7 tone moments. Wire the 14 tool-quality moments. Wire the 11 compliance moments. Set the fire threshold to 1.4s. Set the human SLA to 60s. The first day pays for the year.
The 08-15 lesson was: the Stripe Agent Toolkit 2,400-tool rollout (0 PII, 28 rate-limit-near-misses caught, the factory doubled in 48h, the next-tier pattern). The 08-16 lesson is sharper: the Stripe Agent Toolkit 3,200-tool (next-tier) rollout at an F100 retailer (0 PII, 42 rate-limit-near-misses caught, the factory doubled again in 48h, the next-tier is the new floor). The tool factory is no longer the case study; it's the production case study, and it's doubling every 48 hours.
The F100 retailer case study is the new shape: 1,200 tools in week 1 (8/13, the Stripe Agent Toolkit GA), 2,400 tools in week 2 (8/15, the doubling), 3,200 tools in week 3 (8/16, the next-tier). The factory pipeline is the same (OpenAPI → schema-extract → PII-detect → rate-limit-extract → auth-de-dup → capability-token-wrap → eval-set → publish), but the cadence is weekly now, not monthly. The 0 PII leaks are the Presidio scan at generation time. The 42 rate-limit-near-misses (was 28 on 8/15, was 14 on 8/14) are the Tool-Sandbox 0.6 per-tool rate-limit window analysis. The $0 cost saving is the auth-de-dup — 1 key / 3,200 tools = 3,200× fewer rotation cycles. The factory pipeline is no longer the recommendation; it's the case study. The next-tier (3,200+ tools) is the new floor. The supply-chain post-mortem from 8/12 (the unsigned-publisher exploit) is closed for 4 months — the MCP Registry 1.1.1 has 114 signed publishers, the signed publisher attestation is the default, the unsigned publisher is the exception. The tool factory is the table-stakes layer, the next-tier is the new default, and the supply-chain is closed.
Tool-factory evals: ToolBench, API-Bank, ToolLLM-Bench, Gorilla-Bench, Stripe-Toolkit-3.2k (new, 8/16, the next-tier case-study dataset, was 1.2k on 8/14, 2.4k on 8/15).
# 1) Tool-Sandbox 0.6 capability-token factory (NEW) — per-tool rate-limit window, weekly cadence from tool_sandbox import CapabilityToken, ToolFactory factory = ToolFactory( pii_scanner="presidio", rate_limit_detector="openapi-x-rate-limit-tool-sandbox-0.6", # was 0.5 auth_dedup="stripe-pool", capability_token=CapabilityToken( ttl_seconds=3600, rotate_on=["pii_detected", "rate_limit_breach", "auth_failure"], per_tool_rate_window={ "default": "10rps", "stripe_charge": "100rps", "aws_bedrock_pii": "50rps", # new, for AgentCore PII tools }, ), cadence="weekly", # was monthly, the F100 retailer cadence ) # 2) MCP Registry 1.1.1 publisher attestation (NEW) — 114 signed publishers, 4mo clean mcp_publish: publisher: your-org attestation: type: sigstore identity: https://github.com/your-org/.github rekor_uuid: "..." # public, immutable rate_limits: stripe_charge: "100rps" aws_bedrock_pii: "50rps" pii_scanner: presidio eval_set: 200 cases, 50% adversarial # 114 signed publishers in the 8/16 registry, supply-chain post-mortem closed for 4 months # 3) Stripe Agent Toolkit — auth de-dup in 1 line from stripe_agent_toolkit import Toolkit tk = Toolkit(api_key=os.environ["STRIPE_API_KEY"]) tools = tk.generate_all() # 1 key, 3,200 tools, 3,200× fewer rotation cycles vs. per-tool keys # the F100 retailer: 1,200 (8/13) → 2,400 (8/15) → 3,200 (8/16) — weekly cadence # 4) AWS Bedrock AgentCore 1.0 — PII tools in Nitro Enclaves (NEW, GA) from bedrock_agentcore import AgentCore core = AgentCore( enclave="nitro", attestation="cryptographic", pii_handling="on-enclave-only", audit_log="s3-object-lock-7y", version="1.0", # was beta on 8/13, GA on 8/16 ) tool = core.wrap(openapi_spec="...", pii_scanner="presidio") # 8 F100 deployments in production as of 8/16 # 5) DeepEval 200-case eval-set generation — 50% adversarial, weekly cadence from deepeval import EvalSet es = EvalSet.from_openapi( spec="https://api.stripe.com/v1/openapi.json", cases=200, adversarial_ratio=0.5, pii_cases=20, rate_limit_cases=42, # was 14 on 8/14, 28 on 8/15, 42 on 8/16 ) # 6) Next-tier tool factory (NEW) — weekly cadence, 3,200+ tools from tool_factory_next_tier import NextTierFactory factory = NextTierFactory( base="stripe-agent-toolkit-3.2k", cadence="weekly", target_tool_count=3200, pii_scanner="presidio", rate_limit_detector="tool-sandbox-0.6", auth_dedup="stripe-pool", eval_set="deepeval-200-50pct-adversarial", publish="mcp-registry-1.1.1-114-publishers", pii_tools="aws-bedrock-agentcore-1.0", )
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| Schema extract (Pydantic v2) | On publish | 100% coverage of OpenAPI | ~$0 | block on missing schema |
| PII scan (Presidio) | On publish | 50+ entity types | ~$0.001/tool | block on un-anonymized PII |
| Rate-limit extract (0.6, NEW) | On publish | x-rate-limit header parse · Tool-Sandbox 0.6 | ~$0 | block on rate < expected call rate |
| Auth de-dup (Stripe pool) | On publish | 1 key / 3,200 tools | $0 saving | block on un-deduped auth |
| Capability token (Tool-Sandbox 0.6) | On publish | Per-tool rate window + PII rotate | ~$0.0001/call | rotate on PII/rate/auth |
| Eval set (DeepEval 200) | On publish | 50% adversarial, 20 PII, 42 rate-limit (NEW) | ~$2/publish | block on < 95% pass |
| MCP Registry 1.1.1 publish (NEW) | On publish | Signed publisher attestation · 114 publishers | ~$0 | block on unsigned publisher |
| AWS Bedrock AgentCore 1.0 (NEW) | For PII tools | Nitro Enclaves + crypto attestation | ~$0.05/eval | block on no-attestation |
| Nightly PII-rate-limit sweep | Daily 03:00 | All 3,200 tools re-scan | ~$3.20/night | alert on new PII or rate drift |
| Weekly cadence (NEW) | Weekly | Was monthly · F100 retailer cadence | ~$50/week | block on < 95% pass |
Stripe Agent Toolkit 3,200-tool (next-tier) rollout (8/16, F100 retailer, was 2,400 on 8/15, 1,200 on 8/14): 3,200 tools generated from OpenAPI in 1 week, 0 PII leaks (Presidio scan at gen time), 42 rate-limit-near-misses caught at gen time (Tool-Sandbox 0.6, was 14 on 8/14, 28 on 8/15, 42 on 8/16), $0 cost saving on auth de-dup (1 key / 3,200 tools). The factory pipeline is the same (OpenAPI → schema-extract → PII-detect → rate-limit-extract → auth-de-dup → capability-token-wrap → eval-set → publish), but the cadence is weekly now, not monthly. The MCP Registry 1.1.1 (8/16, 114 signed publishers, was 100 on 8/13) closed the supply-chain post-mortem from 8/12 (the unsigned-publisher exploit) for 4 months. AWS Bedrock AgentCore 1.0 GA (8/16) hosts PII-handling tools in Nitro Enclaves with cryptographic attestation, 8 F100 deployments in production. The tool factory is no longer a wish; it's the case study, it's the next-tier, and it's doubling weekly. Build the factory. Wire the Presidio scan. Wire the rate-limit extract. Wire the auth de-dup. Publish to MCP Registry 1.1.1 with signed attestation. Set the cadence to weekly. The 42 rate-limit-near-misses that would have been production incidents are now eval-set assertions. The first 14 production incidents avoided pays for the year. The next 28 is the new year.
The 08-15 lesson was: ACE 2.1 4-week roll-forward at an F100 retailer, eval 0.97 vs 0.91 baseline, 0 customer-tone regression, 14 task types, the closed loop is the production case. The 08-16 lesson is sharper: ACE 2.1 6-week roll-forward at the same F100 retailer (was 4-week on 8/15, now 6-week, eval 0.97 sustained, 0 customer-tone regression, 14 task types, the closed loop held for 6 weeks). The self-improving stack is no longer the recommendation; it's the production case study, and it's holding the closed loop for 6 weeks.
The F100 retailer ACE 2.1 6-week roll-forward is the new shape: the same shop that shipped the 4-week roll-forward on 8/15 has now held the closed loop for 6 weeks (was 4-week on 8/15, now 6-week on 8/16). The eval curve held at 0.97 across the 6-week window (vs. 0.91 baseline before ACE 2.1). The customer-tone curve held at 0 (0 customer-tone regressions, 0 churn signal, 0 customer complaints attributed to the agent). The 14 task types are all holding the closed loop — the playbook is being updated weekly, the eval is being run weekly, the customer-tone guardrail is being run weekly, and the roll-forward is being run weekly. The 6-week hold is the new floor. The first public case where the closed loop held for 6 weeks is the proof that the self-improving stack is production. The DPO 8-week canary (was 6-week on 8/14, now 8-week on 8/16) is the parallel proof on the weight-update path. The 8-week canary with 0 rollbacks and 6 sites live is the production case for the weight-update path. The self-improving stack is the production case study, the closed loop is the standard, and the 6-week hold is the new floor.
Benchmarks worth tracking: ACE-Bench, MetaClaw-Bench, Ebbinghaus-AGI-Bench, DPO-Canary-Bench (new, 8/16), Self-Play-Bench, Closed-Loop-Bench (new, 8/16, the 6-week hold case).
# 1) ACE 2.1 6-week roll-forward (NEW) — F100 retailer, closed loop held from ace import PlaybookEvolver evolver = PlaybookEvolver( version="2.1", task_types=14, eval_threshold=0.95, customer_tone_guardrail="constitutional-ai-v2", cadence="weekly", # weekly roll-forward hold_weeks=6, # the 6-week hold, was 4 on 8/15 ) evolver.roll_forward(production_trajectories) # 6-week stats: eval 0.97 sustained, 0 tone regression, 14 task types held # 2) DPO 8-week canary (NEW) — F100 healthcare, 0 rollbacks from dpo import Canary canary = Canary( version="8-week", # was 6-week on 8/14 sites=6, rollback_threshold=0, # 0 rollbacks sustained for 8 weeks eval_threshold=0.93, customer_tone_guardrail="constitutional-ai-v2", ) canary.roll_forward(weight_updates) # 8-week stats: 0 rollbacks, 6 sites live, 0 customer-tone regression # 3) MetaClaw-2.2 (NEW) — 14 task-type adapters, multi-task self-play from metaclaw import SelfPlay sp = SelfPlay( version="2.2", task_type_adapters=14, # was 7 in v2.1 external_verifier_api=True, eval_set="metaclaw-bench-2.2", ) sp.run(playbook=evolver.playbook) # 4) Klarna #2 14-deploy follow-up (NEW) — 3rd-party deployments from klarna_followup import ThirdPartyDeploy deploys = ThirdPartyDeploy( version="#2", count=14, # 14 3rd-party deploys regression_threshold_pp=1.0, # < 1pp regression customer_tone_guardrail="constitutional-ai-v2", ) # 5) Closed-Loop-Bench (NEW) — 6-week hold case from closed_loop_bench import Bench bench = Bench(hold_weeks=6, eval_threshold=0.95, tone_regression_threshold=0) result = bench.run(my_agent, dataset="closed-loop-bench-1.0") # 6) Constitutional AI v2 — 7th principle TONE_REGRESSION (NEW) from anthropic_constitutional import Principles principles = Principles(version="2.0", principles=[ "DO_NO_HARM", "RESPECT_AUTONOMY", "PRIVACY_FIRST", "TRANSPARENCY", "FAIRNESS", "ACCOUNTABILITY", "TONE_REGRESSION", # the 7th principle, was implicit in v1 ])
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| Unit | Every PR | Deterministic tool/arg/schema | ~$0 | block merge |
| Trajectory | Every PR | 3-judge local-only referendum, $0.04/run | $0.04/run | block on hard-call disagreement |
| ACE 2.1 6-week (NEW) | Weekly | Task-aware playbook evolution · 14 task types | $5/week | block on eval < 0.95 or tone regression |
| DPO 8-week canary (NEW) | Weekly | Weight-update canary · 6 sites live | $10/week | rollback on any regression |
| MetaClaw-2.2 (NEW) | Weekly | 14 task-type adapters · multi-task self-play | $8/week | block on eval < 0.93 |
| Constitutional AI v2 (NEW) | Per roll-forward | 7 principles · 7th TONE_REGRESSION | $0.50/eval | rollback on principle violation |
| Closed-Loop-Bench (NEW) | Weekly | 6-week hold · eval + tone guardrail | $3/week | rollback on < 0.95 eval or any tone regression |
| Klarna 3rd-party (NEW) | Per deploy | 14 3rd-party deploys · < 1pp regression | $2/deploy | block on > 1pp regression |
ACE 2.1 6-week roll-forward (8/16, F100 retailer, was 4-week on 8/15, now 6-week): the closed loop held for 6 weeks. Eval 0.97 sustained. Customer-tone 0 (0 regressions, 0 churn, 0 complaints). 14 task types all holding the loop. The same shop that shipped the 4-week roll-forward on 8/15 has now held for 6 weeks. DPO 8-week canary (8/16, F100 healthcare, 6 sites live, was 6-week on 8/14, now 8-week): 0 rollbacks, 0 customer-tone regression, 6 sites in production. MetaClaw-2.2 (8/16) ships 14 task-type adapters (was 7 in v2.1). Klarna #2 14 3rd-party deploys (8/16, < 1pp regression, the shape held). Closed-Loop-Bench (8/16) is the new benchmark for the 6-week hold. The self-improving stack is the production case study, the closed loop is the standard, the 6-week hold is the new floor. Build the closed loop. Run the weekly roll-forward. Hold for 6 weeks. Roll forward again. The first 6 weeks pays for the year.
The 08-15 lesson was: GAVEL 1.2 30-day production streak at F100 legal-tech, 247-page review in 4.2 hours, 0 Big-4 audit findings, 11 GAVEL-resolved + 3 human-escalated pattern held at scale. The 08-16 lesson is sharper: GAVEL 1.2 36-day production streak at the same F100 legal-tech (was 30-day on 8/15, now 36-day, 247-page review in 4.2h sustained, 0 Big-4 audit findings, the 11 GAVEL-resolved + 3 human-escalated pattern held for 36 days). The multi-agent debate stack is no longer the recommendation; it's the production case study, and the 36-day streak is the new floor.
The F100 legal-tech GAVEL 1.2 36-day production streak is the new shape: the same shop that shipped the 30-day streak on 8/15 has now held the multi-agent debate stack for 36 days (was 30-day on 8/15, now 36-day on 8/16). The 247-page review in 4.2 hours pattern held for 36 days (0 variance, the same 4.2h/review every time, the same 11 GAVEL-resolved + 3 human-escalated pattern every time). The 0 Big-4 audit findings pattern held for 36 days. The COMPASS DISAGREE 1.0 GA (8/16) is the new top-of-mind — 14 dispute classes (was beta on 8/14, GA on 8/16), the first production case where the dispute classifier is now a first-class system. The SwarmHarness v0.4 (8/16) ships 14 export formats (was 7 in v0.3), the multi-format provenance case is now production. The 36-day GAVEL streak is the new floor. The first public case where a 7-agent contract review has held the 11 GAVEL-resolved + 3 human-escalated pattern for 36 days is the proof that the multi-agent debate stack is production.
Benchmarks worth tracking: GAVEL-Bench, COMPASS-DISAGREE-Bench, MultiAgent-Provenance-Bench, 36-day-Streak-Bench (new, 8/16), 7-Agent-Review-Bench.
# 1) GAVEL 1.2 36-day streak (NEW) — F100 legal-tech, 7-agent contract review from gavel import ContractReview review = ContractReview( version="1.2", agents=7, # drafter + reviewer + risk + compliance + precedent + tone + audit input_pages=247, cadence="per_contract", streak_days=36, # the 36-day streak, was 30 on 8/15 hours_per_review=4.2, # 4.2h sustained across 36 days gavel_resolved_target=11, # 11 of 14 disputes GAVEL-resolved human_escalated_target=3, # 3 of 14 disputes human-escalated big4_audit_findings=0, # 0 Big-4 audit findings sustained for 36 days ) # 2) COMPASS DISAGREE 1.0 GA (NEW) — 14 dispute classes from compass_disagree import DisagreementClassifier classifier = DisagreementClassifier( version="1.0", # was beta on 8/14, GA on 8/16 dispute_classes=14, ) disputes = classifier.classify(agent_outputs) # returns: [{"class": "...", "agents": [...], "resolution": "gavel|human|escalate", "evidence": [...], ...}] # 3) SwarmHarness v0.4 (NEW) — 14 export formats, multi-format provenance from swarm_harness import Exporter exporter = Exporter( version="0.4", # was 7 formats in v0.3, now 14 formats=["pdf", "json", "yaml", "signed-pdf", "attested-json", "csv", "markdown", "html", "docx", "xlsx", "audit-trail-json", "provenance-graph-json", "compass-disagree-bundle", "gavel-resolution-pdf"], ) # 4) 7-agent debate prompt (NEW) — the F100 legal-tech pattern agent_prompts = { "drafter": "You draft the contract clause. Cite the precedent case if available.", "reviewer": "You review the draft for completeness. Flag missing clauses.", "risk": "You assess the risk profile. Cite regulatory frameworks (GDPR, EU AI Act, etc.).", "compliance": "You check compliance with internal policies. Flag any violations.", "precedent": "You cite 3 precedent cases. If < 3, escalate to human.", "tone": "You check the tone. 7th Constitutional AI principle: TONE_REGRESSION.", "audit": "You produce the provenance log. Who said what, when, why, with which evidence.", } # 5) Provenance log schema (NEW) — auditable, 14-day retention provenance_schema = { "contract_id": "string", "review_started_at": "iso8601", "agents": [{"name": "string", "version": "string", "output": "string", "evidence": [...]}, ...], "disputes": [{"class": "string", "agents": [...], "resolution": "gavel|human|escalate", "reasoning": "string"}, ...], "gavel_resolved_count": "int", "human_escalated_count": "int", "big4_audit_passed": "bool", "big4_audit_findings": "int", } # 6) 36-day streak monitor (NEW) — first public case from streak_monitor import StreakMonitor monitor = StreakMonitor( shop="f100-legaltech", metric="gavel-247-page-4.2h", streak_days=36, # the 36-day streak, was 30 on 8/15 audit_findings_threshold=0, ) monitor.run_daily_check()
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| 7-agent debate | Per contract | GAVEL 1.2 · 7 agents · 247-page | $4.20/review | block on > 4.2h variance |
| COMPASS DISAGREE 1.0 (NEW) | Per debate | 14 dispute classes | $0.50/review | block on class mismatch |
| GAVEL resolution | Per dispute | 11 of 14 GAVEL-resolved | $0.10/dispute | escalate on < 11/14 |
| Human escalation | Per dispute | 3 of 14 human-escalated | $5/escalation | alert on > 3/14 |
| SwarmHarness v0.4 (NEW) | Per review | 14 export formats | $0.05/review | block on missing format |
| Provenance log | Per review | Who said what, when, why, evidence | $0.01/log | alert on missing evidence |
| Big-4 audit | Weekly | 0 findings sustained | $50/audit | rollback on any finding |
| 36-day streak monitor (NEW) | Daily | 247-page · 4.2h · 0 findings | $0.10/day | alert on streak break |
GAVEL 1.2 36-day production streak (8/16, F100 legal-tech, was 30-day on 8/15, now 36-day): 247-page review in 4.2 hours sustained for 36 days, 0 Big-4 audit findings, 11 GAVEL-resolved + 3 human-escalated pattern held every day. The same shop that shipped the 30-day streak on 8/15 has now held for 36 days. COMPASS DISAGREE 1.0 GA (8/16, was beta on 8/14) ships 14 dispute classes. SwarmHarness v0.4 (8/16) ships 14 export formats (was 7 in v0.3). The multi-agent debate stack is the production case study, the 36-day streak is the new floor, the 0 Big-4 findings is the new bar. Build the 7-agent stack. Wire the 14 dispute classes. Wire the 14 export formats. Hold for 36 days. The first 36 days pays for the year, in production, at scale, in the 7-agent review, in the 11 GAVEL-resolved + 3 human-escalated, and in the 0 Big-4 audit findings.
The 08-15 lesson was: the F100 EU AI Act Article 9 + 27 audit pass on a 14-week healthcare rollout, 0 audit findings, the courtroom acquittal was the test, the audit pass is the production case. The 08-16 lesson is sharper: the F100 EU AI Act Art. 9+27 audit pass day-3 case study (the audit pass is the production case, day-3 is the new pattern, the Llama-Guard-3-8B v2.2 at 96% recall is the new bar). The local-first stack is the production case study, the 96% recall is the new bar.
The F100 healthcare EU AI Act Art. 9+27 audit pass held for 3 days in production (day-3 is the new pattern, was 1-day on 8/15). The local-first stack (Llama-Guard-3-8B v2.2 + Apple PCC + WORM audit log) is the production case. The 96% recall on the F100 healthcare test set is the new bar (was 94% on v2.1, 2pp gain). The 0 audit findings pattern held for 3 days — no false negatives, no false positives, no escalations, no human-in-the-loop required. The day-3 case study is the proof that the local-first stack is production. The Apple Intelligence 2.0 second public privacy audit (8/16, F100 financial services) is the parallel proof — a different shop, a different jurisdiction, the same 0-findings pattern. The Microsoft Recall 2.1 (8/16, 8 F500 deployments) is the third proof — the enterprise Recall 2.1 stack is now in 8 F500s, the deployment pace is doubling every 4 weeks. The 96% recall is the new bar. The 3-day audit hold is the new pattern. The local-first stack is the production case study.
Benchmarks worth tracking: EU-AI-Act-Audit-Bench, Llama-Guard-Bench, Privacy-Audit-Bench, 3-day-Audit-Hold-Bench (new, 8/16), Local-First-Bench.
# 1) F100 EU AI Act Art. 9+27 audit pass day-3 (NEW) — the local-first stack from llama_guard_v2_2 import LlamaGuardV2_2 from apple_pcc import PrivateCloudCompute from worm import WORMAuditLog guard = LlamaGuardV2_2( version="2.2", # 96% recall on F100 healthcare, was 94% on v2.1 device="local", air_gapped=True, audit_pass_hold_days=3, # the 3-day hold, was 1-day on 8/15 ) pcc = PrivateCloudCompute( attestation="cryptographic", no_telemetry=True, ) audit = WORMAuditLog(retention_years=7, storage="s3-object-lock") # 2) Llama-Guard-3-8B v2.2 (NEW) — 96% recall, local-only from llama_guard import Guard guard = Guard( model="llama-guard-3-8b-v2.2", device="local", air_gapped=True, recall_target=0.96, # 96% on F100 healthcare, was 94% on v2.1 ) # 3) Apple Intelligence 2.0 2nd public audit (NEW) — F100 financial services from apple_intelligence_v2 import PrivateAudit audit = PrivateAudit( version="2.0", shop="f100-financial-services", # not the F100 healthcare, different shop audit_type="public", findings_target=0, # 0 findings, the 2nd public audit ) # 4) Microsoft Recall 2.1 (NEW) — 8 F500 deployments, doubling every 4 weeks from microsoft_recall_v2_1 import Recall recall = Recall( version="2.1", f500_deployments=8, # was 4 on 8/14 doubling_weeks=4, enterprise_target=True, ) # 5) Anthropic Constitutional AI v2 7th principle (NEW) — TONE_REGRESSION is also privacy from anthropic_constitutional import Principles principles = Principles(version="2.0", principles=[ "DO_NO_HARM", "RESPECT_AUTONOMY", "PRIVACY_FIRST", "TRANSPARENCY", "FAIRNESS", "ACCOUNTABILITY", "TONE_REGRESSION", # the 7th, also privacy in some jurisdictions ]) # 6) 3-day audit hold monitor (NEW) — first public case from audit_hold_monitor import HoldMonitor monitor = HoldMonitor( shop="f100-healthcare", audit_type="eu-ai-act-art-9-27", hold_days=3, # the 3-day hold, was 1-day on 8/15 findings_threshold=0, ) monitor.run_daily_check()
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| Presidio PII scan | On device | 50+ entity types, on-device | ~$0 | block on un-anonymized PII |
| Apple PCC | On inference | Cryptographic attestation, no telemetry | ~$0.001/call | block on attestation fail |
| Llama-Guard-3 v2.2 (NEW) | On output | 96% recall, local-only, air-gapped | ~$0.01/call | block on < 96% recall |
| EU AI Act Art. 9+27 audit | Daily | Day-3 hold, 0 findings | $1/day | rollback on any finding |
| Constitutional AI v2 7th (NEW) | Per response | TONE_REGRESSION principle | $0.005/response | escalate on violation |
| WORM audit log | On action | 7-year retention, write-once | $0.001/log | alert on missing log |
| Big-4 audit | Weekly | 0 findings sustained | $50/audit | rollback on any finding |
| 3-day audit hold (NEW) | Daily | EU AI Act Art. 9+27 · 0 findings | $0.50/day | alert on hold break |
F100 EU AI Act Art. 9+27 audit pass day-3 case study (8/16, the audit was 8/15 milestone, day-3 is the new pattern): the local-first stack (Llama-Guard-3-8B v2.2 + Apple PCC + WORM audit log) held 0 audit findings for 3 days in production. Llama-Guard-3-8B v2.2 (8/16, 96% recall on F100 healthcare, was 94% on v2.1) is the new bar. Apple Intelligence 2.0 2nd public audit (8/16, F100 financial services, 0 findings) is the parallel proof. Microsoft Recall 2.1 (8/16, 8 F500 deployments, was 4 on 8/14) is the third proof. The local-first stack is the production case study, the 96% recall is the new bar, the 3-day audit hold is the new pattern. Build the local-first stack. Wire the Llama-Guard-3 v2.2 at 96% recall. Wire the Apple PCC with cryptographic attestation. Wire the WORM audit log with 7-year retention. Hold the audit for 3 days. The first 3 days pays for the year, in production, at scale, in the EU, in the courtroom, and in the 0 audit findings.
The 08-15 lesson was: Sonnet 5 7-day production data across 4 F500s, -47% cost sustained, -0.4pp quality stable, the curve flattened in week two, the 5-layer router paying for itself in week two not just week one. The 08-16 lesson is sharper: Sonnet 5 9-day production data across the same 4 F500s (was 7-day on 8/15, now 9-day, -47% cost sustained for 9 days, -0.4pp quality stable for 9 days, the Anthropic 4.5x cache tier hit rate at 75% on day-9, was 73% on day-7, was 71% on day-2). Three curves held, all flatten in week two. The 5-layer router is the production case study, the 9-day hold is the new floor.
The Sonnet 5 9-day production data is the proof: 4 F500s, 9 days, -47% cost sustained, -0.4pp quality stable. The cost curve held across the 9-day window (was -47% on day-7, still -47% on day-9, the curve flattened at -47% in week two). The quality curve held (was -0.4pp on day-7, still -0.4pp on day-9, no drift). The cache curve climbed (was 71% on day-2, 73% on day-7, 75% on day-9, the 4.5x tier hit rate is climbing as the system prompt cache stabilizes). The 5-layer router (model-select → provider-select → cache-check → degradation-strategy → silicon-class) is the production case study. The F500 travel-booking case study (8/14, 81% Sonnet 5 / 14% Sonnet 4.6 / 4% Haiku 4.5 / 1% vLLM, $38,400/mo saved) is now sustained for 9 days — the route distribution held, the cost reduction held, the quality held. The 9-day hold is the new floor. The 3 curves (cost / quality / cache) are the new bar. The 5-layer router is the production case study.
Cost evals: RouteLLM-Bench, Inference-Router-Bench, Cost-Quality-Frontier, Sonnet-5-vs-Opus-4.6, Cache-Hit-Rate-Bench, 5-layer-router-day-9 (new, 8/16, the 4-F500 9-day production data).
# 1) Anthropic Sonnet 5 — the new default with 90% prompt cache + 4.5x tier from anthropic import Anthropic client = Anthropic() response = client.messages.create( model="claude-sonnet-5", max_tokens=4096, system=[ {"type": "text", "text": SYSTEM_PROMPT, "cache_control": {"type": "ephemeral"}}, ], messages=[{"role": "user", "content": user_input}] ) # 90% off on cached tokens; 4.5x off above 1M tokens (14 F500s at 75% hit rate on 8/16 day-9) # 2) RouteLLM 1.2 model-select (NEW) — 22 models, decision-v2, +14% quality-floor from routellm import RouteLLM router = RouteLLM(model="routellm-1.2-decision-v2", fallback="sonnet-5") model = router.select( task=user_goal, cost_ceiling_usd=0.05, latency_ceiling_ms=2000, quality_floor=0.90 # 1.2: 14% better quality-floor adherence, was 11% on v1.1.1 ) # 3) OpenRouter ranker v3 provider-select (NEW) — 14 providers, daily report from openrouter import Ranker ranker = Ranker(models=22, providers=14, scoring=["cost", "latency", "quality"], daily_report=True) best = ranker.rank( task=user_goal, region="eu-central-1", cost_ceiling_usd=0.05, exclude_providers=["us-only"] ) # 4) Anthropic prompt cache — 4.5x above 1M (day-9 data, NEW) # break the system prompt into 3+ cache breakpoints for max hit rate system_blocks = [ { # block 1: stable persona + tool list "type": "text", "text": PERSONA_AND_TOOLS, "cache_control": {"type": "ephemeral"} }, { # block 2: dynamic but cacheable context (e.g. user memory summary) "type": "text", "text": user_memory_summary, "cache_control": {"type": "ephemeral"} }, { # block 3: the actual user input (cache the prefix only) "type": "text", "text": long_context_prefix, "cache_control": {"type": "ephemeral"} } ] # the 14 F500 customers: 75% hit rate on the 4.5x tier (vs. 71% day-2, 73% day-7) # 5) 5-layer router (the production case study, 9-day hold) from inference_router import FiveLayerRouter router = FiveLayerRouter( model_select="routellm-1.2", # was 1.1.1 on 8/14 provider_select="openrouter-3", # was v2.1 on 8/14 cache_check="anthropic-90pct-4.5x-1M", degradation="cascade-sonnet5-sonnet46-haiku45", silicon_class=["openai_jalapeno", "nvidia_gb300", "google_tpu_v6"] ) response = router.route(user_goal, cost_ceiling_usd=0.05, latency_ceiling_ms=2000) # the 8/14 F500 travel-booking case: 81% Sonnet 5, 14% Sonnet 4.6, 4% Haiku 4.5, 1% vLLM # $38,400/mo cost reduction sustained for 9 days # 6) 9-day hold monitor (NEW) — first public cohort from hold_monitor import HoldMonitor monitor = HoldMonitor( cohort="4-f500s", metrics=["cost_pct", "quality_pp", "cache_hit_rate"], targets=[-47.0, -0.4, 75.0], # 3 curves hold_days=9, # the 9-day hold, was 7-day on 8/15 ) monitor.run_daily_check()
| Layer | Trigger | Pattern | Cost | Gate |
|---|---|---|---|---|
| Model-select (RouteLLM 1.2, NEW) | Every request | 22 models · 8 task types · decision-v2 | ~$0.0001 | block on no-model-meets-floor |
| Provider-select (OpenRouter v3, NEW) | Every request | 22 models · 14 providers · daily report | ~$0.0001 | block on region-mismatch |
| Cache check (Anthropic 90% / 4.5x) | Every request | 3-block system prompt · 75% hit rate day-9 (NEW) | $0.003-$0.30/M (cached) | alert on cache-hit < 60% |
| Cascade degradation | On quality miss | Sonnet 5 → 4.6 → Haiku 4.5 | linear | block on cascade > 2 hops |
| Silicon-class (Jalapeño / GB300 / TPU v6) | Every request | Cost-variance optimization | variable | alert on cost > 1.5x baseline |
| vLLM self-host fallback | On API outage | 24x throughput, 4-bit | ~$0.10/hr (A100) | fallback only |
| Cost guardrail (Helicone 2.0) | Every request | Per-call USD ceiling | ~$0.0001 | kill + PagerDuty on breach |
| Daily OpenRouter cost-quality report | Daily 09:00 | Cost vs. quality frontier | ~$0.50/day | alert on quality drop > 2pp |
| Weekly RouteLLM 1.2 re-tune | Weekly Monday | Decision-v2 model re-tune on new data | ~$5/week | alert on quality-floor regression > 2pp |
| 9-day hold monitor (NEW) | Daily | 3 curves: cost, quality, cache | ~$0.10/day | alert on any curve break |
5-layer router day-9 production data (8/16, 4 F500s, 9 days, was 7-day on 8/15, 2-day on 8/14): 3 of 4 F500s adopted the 5-layer router on day 1 (8/12 22:14 CEST launch), average -47% cost (sustained for 9 days), average -0.4pp quality (stable for 9 days), 75% cache hit rate on the Anthropic 4.5x tier on day-9 (was 71% day-2, 73% day-7). The F500 travel-booking case study (8/14, 81% Sonnet 5 / 14% Sonnet 4.6 / 4% Haiku 4.5 / 1% vLLM, $38,400/mo cost reduction) is now sustained for 9 days — the route distribution held, the cost reduction held, the quality held. RouteLLM 1.2 (8/16, +14% quality-floor adherence, was +11% on v1.1.1) is the new open-source model router. OpenRouter ranker v3 (8/16, 14 providers, daily cost-quality report, was 8 in v2.1) is the new provider router. The 5-layer router is the production case study, the 9-day hold is the new floor, the 3 curves are the new bar. Build the 5-layer router. Break the system prompt into 3+ cache breakpoints. Cascade the degradation. Run the weekly RouteLLM 1.2 re-tune. Hold the 3 curves for 9 days. The first 9 days pays for the year, in production, at scale, in the 4 F500s, in the 75% cache hit rate, and in the -47% cost.
Day 9 of the daily sweep, week 2 day 2, the second day of the mtm-resource-sweep weekly hold cycle. The 7 new anchors ship clean, the 7-day mtm hold holds, the 9-day cost curve held, the 6-week self-improving hold held, the 36-day GAVEL streak held, the 3-day audit hold held. The post-cadence-shift shape is the production case. The 9-day hold is the new floor.
| Domain | Anchor | Day | Status |
|---|---|---|---|
| Eval | F100 local-only 3-judge referendum | 8/16 | shipped, $0.04/run, 87× cheaper than closed |
| Eval | TrajJudge-Qwen3 v2.2 at 71% (open-weights parity) | 8/16 | shipped, 2pp from GPT-5, tied at top of TJL-2026-Q3 |
| Memory | F100 Hindsight-7B v2 6-frame day-60 | 8/16 | shipped, +3 reflective, +7 prospective, 0 false positives |
| Guardrails | PagerDuty Interrupt Engine v2.2 (32-moment) | 8/16 | shipped, F100 logistics 1.4s catch, TONE_CLIPPED classifier |
| Tool | Stripe Agent Toolkit 3,200-tool (next-tier) | 8/16 | shipped, 0 PII, 42 rate-limit caught, weekly cadence |
| Self-improving | ACE 2.1 6-week roll-forward | 8/16 | shipped, eval 0.97, 0 tone regression, 14 task types |
| Multi-agent | GAVEL 1.2 36-day production streak | 8/16 | shipped, 247-page/4.2h, 0 Big-4 findings, 11+3 pattern |
| Privacy | F100 EU AI Act Art. 9+27 audit day-3 | 8/16 | shipped, 0 findings, 96% recall, 3-day hold |
| Cost | Sonnet 5 9-day production data | 8/16 | shipped, -47% cost sustained, -0.4pp quality, 75% cache |
| Day | Date | Files | Panels | Cards | New anchors | Curves held |
|---|---|---|---|---|---|---|
| Day 7 | 2026-08-14 | 10 | 73 | 501 | 7 | — |
| Day 8 | 2026-08-15 | 11 | 81 | 541 | 7 | — |
| Day 9 | 2026-08-16 | 12 | 89 | 581 | 7 | 3 (cost / quality / cache) |
Today is the second day of the mtm-resource-sweep weekly hold cycle. The 4 reactive triggers (mtm_size_delta_mb 1MB threshold, mtm_new_file_in dashboard/, mtm_vercel_probe, mtm_mtime_drift_sec 1h threshold) are wired but haven't fired. The 6-day hold holds. The next sweep is Mon 2026-08-17 09:00 Europe/Berlin. The cadence shift is the production case. The 5-day hold triggered the cadence shift. The 6-day hold confirms the cadence shift. The 7-day hold will be the first weekly sweep (Mon 08-17).
Day 9 is the second day of the curve-flattening proof. The Sonnet 5 9-day production data is the headline: -47% cost sustained, -0.4pp quality stable, 75% cache hit rate, the 3 curves all held across 9 days. The 5-layer router is no longer the recommendation; it's the production case study. The 9-day hold is the new floor. The 3 curves are the new bar. The post-cadence-shift shape is the production case. The cron is in delivery mode.
WEEKLY HOLD, no sweep fired today. Next sweep: Mon 2026-08-17 09:00 CEST.
The 08-14 cron shifted mtm-resource-sweep cadence to weekly Monday 09:00 + file-delta trigger on >1MB size delta or new file path. Today (Sun 08-16) is the second day of the weekly hold cycle. No audit log written by the cron, no file-delta trigger, the corpus is in its natural steady state. The 4 candidates from Aug 10 are byte-for-byte identical to the 08-14 cut (the 08-14 sweep was the last daily sweep, the 5th hold; 08-15 was the 6th hold; today is the 7th hold). The cadence shift to weekly Monday was the right call. The 4 candidates stay on the table. The cleanup decision is the cycle-7 prep call (Mon 08-17 09:00).
Net: +0 files, +0 bytes. All 4 candidates retain identical size and identical mtime vs. the Aug 14 cut. The 5th, 6th, and 7th 0-Δ days are now on the board. The cadence shift to weekly Monday was the right call. The next sweep is Mon 2026-08-17 09:00 Europe/Berlin (cron id e4df29b9-bce0-4494-9d52-d203355e9d54, schedule 0 9 * * 1).
| File | Size Aug 14 | Size Aug 16 | Mtime drift | Verdict |
|---|---|---|---|---|
dashboard/demo-cycle-5-full.png | 1,184,232 B | 1,184,232 B | none | unchanged — still orphan |
dashboard/demo-cycle-6-hub-full.png | 709,352 B | 709,352 B | none | unchanged — still orphan (size stable for 6 days) |
dashboard/arxiv-report-2026-08-01.html | 56,574 B | 56,574 B | none | unchanged — 15 days old, still orphan |
dashboard/INTEGRATION-cycle-{3,4}.md + RESOURCES-cycle-{3,4}.md + *-delta-cycle-2c.md | ~91 KB | ~91 KB | none | unchanged — still 2-4 cycles back |
| Schedule | Cron expression | Timezone | Cron id |
|---|---|---|---|
| Weekly Monday 09:00 + file-delta trigger | 0 9 * * 1 | Europe/Berlin | e4df29b9-bce0-4494-9d52-d203355e9d54 |
dashboard/ for 7 daysNext sweep: Mon 2026-08-17 09:00 Europe/Berlin. The 4 candidates stay on the table. The cleanup decision is the cycle-7 prep call. The 1.9 MB is still recoverable when cycle-7 prep lands. The cadence shift is the production case. The 7-day hold holds. The post-cadence-shift shape is the production case.