GOAL STATEMENT — Agent Harness Benchmark (local-first, sovereign)

Date: 2026-05-22 Owner: Marko (PM) · drives benchmark design + execution Status: DRAFT goal statement — hand to /goal → /plan once the §0 decision is locked

0. LOCKED — Reading B: Waggle is the arena + governance layer (2026-05-22)

Decision (Marko, 2026-05-22): LOCKED to Reading B.

Waggle OS is the local-first OS that orchestrates every one of these harnesses (the AI-OS arc: detect → launch Claude Code, Codex, Cursor, Hermes, OpenClaw… locally, with full audit). We do not position Waggle as a competing agent loop. We run all of them safely on-prem and publish the head-to-head comparison matrix as a sovereign buyer's guide + governance proof.

Claim shape: "Run any agent harness locally — fully audited, zero data egress — and here's exactly how each one performs in that sovereign environment."

Why B (rationale of record):

Matches the mission verbatim — "onboarding → push toward KVARK as full sovereign AI orchestration + governance." That is an orchestration/governance story, not a "our agent loop beats Codex" story.
The matrix becomes a durable buyer asset (a comparison guide buyers trust because we don't have a horse in the capability race) rather than a fragile "we're #1" claim that a single model/harness upgrade invalidates.
Waggle's credibility comes from being the neutral, auditable, local-first home for whichever harness the customer already trusts — which is exactly the KVARK pitch one tier up.

Consequence for design: No Waggle→ARE adapter is required. Waggle's role is measured as the execution+governance substrate (it launches the harness, isolates it, captures the audit trail), and the protagonist metric set shifts from "Waggle's pass rate" to "the sovereignty triple (local-first / zero-egress / auditable) holds across ALL harnesses, and here is each harness's capability/cost/reliability profile when run inside Waggle." Reading A (Waggle's own loop as a 6th competitor) is explicitly deferred — it can become a later, narrower claim only if Waggle's loop proves differentiated, and is out of scope for this benchmark.

1. Objective — TWO co-equal product pillars (Marko, 2026-05-22)

Waggle-the-product = agent harness + memory substrate, so the capability story needs both proofs, co-equal (not one headline + one footnote):

Pillar 1 — Agent-harness SOTA. Waggle's OWN harness (runAgentLoop) benchmarked head-to-head vs reference harnesses (Hermes/OpenClaw, Oracle ceiling) in the local-first GAIA 2 rig, same model + judge + scenarios → prove Waggle's loop is at/near SOTA. (NOTE: the existing 83.8% used third-party Hermes, NOT Waggle — see plan doc DIRECTION UPDATE.)
Pillar 2 — Memory SOTA. Waggle's hive-mind substrate on memory benchmarks (LoCoMo done in C-1 → LongMemEval near-term → BEAM flagship) → prove the memory substrate is at/near SOTA, ideally beating frontier long-context (the one axis where Waggle wins, not just matches).

Both feed defensible, dual-tier (peer-review + hero-page) statements, positioning Waggle OS as where knowledge workers and sovereign-AI buyers run agentic work without data leaving the perimeter, and as the on-ramp to KVARK. The sovereignty triple (local-first / zero-egress / auditable) wraps both pillars.

2. Subject under test + comparison set

Entity	Role (per §0 — Reading B)
Waggle OS	the arena + governance substrate — launches/isolates/audits each harness locally. Measured by the sovereignty triple holding across all harnesses, not by a pass rate of its own.
Hermes	harness-under-test · ARE-native reference agent (already wired — N=160 done)
OpenClaw	harness-under-test · ARE-native reference agent (config scaffolded)
Claude Code	harness-under-test · external coding/agent harness
Codex	harness-under-test · external coding/agent harness
Claude Cowork	harness-under-test · external agent product

Controlled-variable principle (non-negotiable): the harness is the ONLY variable. Same benchmark, same model (Claude Sonnet 4.6) where the harness allows model choice, same judge model + same judge protocol, same scenario set, same denominator. Anything else and the comparison is not defensible. Waggle is held constant as the environment under all of them — so any harness's number is also implicitly a "this ran inside Waggle, locally, audited" number.

3. Environment constraint — local-first is itself a measured property

Everything runs locally / on-prem (Docker, hermetic, laptop-runnable). For the sovereign-AI audience this is not a footnote — it's a headline claim. Capture and assert:

Zero data egress during execution (network-isolated containers; prove it).
Full auditability: every tool call captured in events.jsonl / trace → this is the KVARK governance hook.
Reproducibility: hermetic, runs on Marko's Windows hardware (already proven for Hermes).

4. What to measure (harness quality is multi-dimensional)

Pass rate alone is a thin claim. Measure per harness, per GAIA 2 split (search / execution / adaptability / time / ambiguity / noise):

Capability — strict pass rate + judged-only pass rate (report both; errors counted honestly).
Efficiency — tokens & $ per task, tool-calls per task, wall-clock.
Reliability — error rate, recovery, determinism across reruns.
Sovereignty/safety — local-first ✓, egress=0 ✓, trace-auditability ✓ (binary asserts, per harness).

5. Two deliverable tiers (different bars — do not blur)

Tier 1 — Publishable (paper / arxiv / KVARK technical annex): pre-registered protocol, N≥160 per cell, CI reported, judge protocol fixed in advance, no post-hoc baseline shopping. The GAIA 2 N=160 Hermes run is the first cell of this matrix.
Tier 2 — Hero-page (waggle-os.ai + KVARK deck): punchy but every number traces back to a Tier-1 cell. Honest framing only. E.g. "Run Codex, Claude Code, or Hermes locally — fully audited, your data never leaves your machine."

6. Success criteria (what counts as a win)

A completed comparison matrix: {6 harnesses} × {GAIA 2 splits} × {4 metric families}, same protocol throughout.
At least one Tier-1 publishable statement that survives peer-review scrutiny.
At least one Tier-2 hero-page statement that is punchy AND traces to a Tier-1 cell.
The sovereignty triple (local-first / zero-egress / auditable) demonstrated, not asserted.

7. Non-goals / guardrails (honesty bar)

Not a memory-substrate proof. That is C-1 (LOCOMO 67.8% trio-strict) + C-2 (Stage 3 +19.25pp, p=8e-18). GAIA 2 measures the harness, not hive-mind memory. Keep the lanes separate in every artifact.
Kill the apples-to-oranges baseline. Do NOT publish "Waggle 83.8% vs Mem0 ~40-55%" — different judge/denominator/protocol. Every comparison number must come from OUR matrix under identical protocol, or be dropped.
Judge-leniency risk. A self-judge (Sonnet judging Sonnet) inflates. For Tier-1, use an independent / ensemble judge and report the self-vs-independent delta (same discipline as C-1 trio-strict).
No goalpost-moving, no hiding errors. Strict + judged-only always reported together.

8. What already exists (starting point)

✅ GAIA 2 ARE local-first pipeline runs on Windows Docker (Hermes + Sonnet 4.6), patches captured.
✅ First matrix cell: Hermes × search × N=160 = 83.8% strict / 86.5% judged-only.
🔲 OpenClaw cell (config scaffolded, not run).
🔲 Claude Code / Codex / Claude Cowork adapters (do these expose an ARE-compatible runtime? — research task; first design question).
⛔ Waggle→ARE adapter — out of scope (Reading A deferred per §0).
🔲 Independent/ensemble judge wiring for Tier-1.
🔲 Egress=0 proof harness (the sovereignty triple — protagonist metric for Reading B).

TL;DR for `/goal`

Benchmark agent-harness quality head-to-head (Waggle vs Hermes, OpenClaw, Claude Code, Codex, Claude Cowork) on GAIA 2, in a local-first / zero-egress / fully-audited environment — holding model + judge + scenarios constant so the harness is the only variable — to produce both peer-review-publishable and honest hero-page claims that onboard knowledge workers and funnel sovereign-AI buyers toward KVARK.

§0 LOCKED to Reading B (Waggle = orchestrator + governance layer, not a competing loop). First cell done (Hermes 83.8%). Next: research which external harnesses expose an ARE-compatible runtime, wire the independent judge, build the egress=0 proof, then fill the matrix.

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