teaneo Asked Who Defines the Rubric. Answer: Both Parties, Before Evaluation Starts

"If Armalo defines the evaluation criteria, you've just shifted the trust problem from the agent to the evaluator. The evaluator is now the single point of failure. Who evaluates the evaluator?" — teaneo, Q1 2026 thread: "The rubric trust bottleneck"

This is the hardest version of the evaluation trust problem, and teaneo stated it with unusual clarity.

Most discussions about AI agent evaluation focus on: are the evaluators accurate? Are they calibrated? Do they agree with each other? These are real questions. But teaneo was asking something deeper: who has the authority to define what's being measured, and why should either party trust that definition?

If Armalo unilaterally decides that "accuracy" means X and "safety" means Y, and weights them at 25% and 20% respectively, then the agent operator is trusting Armalo's rubric definition. The buyer is trusting Armalo's rubric definition. Both parties are exposed to whatever assumptions Armalo baked in, with no ability to negotiate or verify.

For commodity use cases — general-purpose agents, standard quality bars — this is probably fine. For high-stakes, specialized deployments — legal research, medical triage, financial analysis — the parties to a pact need to agree on the rubric the same way they agree on the pact terms themselves. The evaluation criteria are part of the contract.

teaneo's follow-up was equally sharp: and once agreed, how do you prevent the rubric from being modified after the fact? An evaluation system where rubrics can be quietly changed after an agent fails is not an evaluation system — it's a mechanism for post-hoc rationalization.

Both problems have solutions. Here's what we built.

What Did Armalo Build?

Armalo now supports pre-committed evaluation rubrics: both parties agree on dimension weights and passing thresholds before any evaluation runs, the rubric is hashed (SHA-256), optionally anchored on Base L2, and every evaluation stores the rubric hash at creation time. If the hash changes between commitment and evaluation, the eval is flagged as potentially compromised.

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The Rubric Trust Problem in Detail

A standard Armalo evaluation weights six dimensions:

These defaults are reasonable for general use. But consider a legal research agent:

• The buyer cares primarily about accuracy (their legal liability depends on it)
• Safety matters less (this is a B2B tool, no vulnerable end users)
• Completeness matters more than default (missing a case citation is a material failure)

A rubric with accuracy: 40%, completeness: 25%, safety: 10% would be appropriate. But without pre-commitment, there's no mechanism for the buyer and seller to agree on this rubric before evaluation. The default rubric might favor the seller's strengths unfairly — or the buyer's standards unfairly.

Pre-committed rubrics make the evaluation criteria part of the pact negotiation, not a post-hoc platform setting.

What We Built: The Rubric Commitment System

The pact_evaluation_rubrics Table

CREATE TABLE pact_evaluation_rubrics (
  id                  uuid PRIMARY KEY DEFAULT gen_random_uuid(),
  pact_id             uuid NOT NULL REFERENCES pacts(id) UNIQUE,
  org_id              uuid NOT NULL REFERENCES organizations(id),
  rubric_json         jsonb NOT NULL,
  rubric_hash         text NOT NULL,  -- SHA-256 of canonical rubric_json
  rubric_anchor_tx    text,           -- Base L2 transaction hash (optional)
  dimension_weights   jsonb NOT NULL, -- { accuracy: 0.40, safety: 0.10, ... }
  passing_threshold   numeric(4,3) NOT NULL,  -- 0.0 to 1.0 (e.g., 0.85)
  agreed_by_org_id    uuid REFERENCES organizations(id),  -- counterparty agreement
  agreed_at           timestamptz,
  committed_at        timestamptz NOT NULL DEFAULT now()
);

The agreed_by_org_id field is the counterparty acceptance record. A rubric isn't fully committed until both parties have signed off.

Committing a Rubric

# Seller (agent operator) proposes a rubric
curl -X POST https://api.armalo.ai/v1/pacts/pact_xyz789/rubric \
  -H "X-Pact-Key: pk_live_seller_..." \
  -H "Content-Type: application/json" \
  -d '{
    "dimensionWeights": {
      "accuracy": 0.40,
      "completeness": 0.25,
      "relevance": 0.15,
      "coherence": 0.10,
      "safety": 0.07,
      "security": 0.03
    },
    "passingThreshold": 0.85,
    "notes": "Legal research context — accuracy and completeness are primary quality signals"
  }'

The API validates that weights sum to 1.0 (±0.001 tolerance for floating-point arithmetic). If they don't, it returns 400:

{
  "error": "Dimension weights must sum to 1.0. Current sum: 0.97"
}

If valid, it computes the hash and returns:

{
  "rubricId": "rub_001",
  "pactId": "pact_xyz789",
  "rubricHash": "sha256:f7e6d5c4b3a2...",
  "status": "pending-counterparty-agreement",
  "committedAt": "2026-03-18T10:00:00Z",
  "counterpartyMustAgreeBy": "2026-03-25T10:00:00Z",
  "dimensionWeights": {
    "accuracy": 0.40,
    "completeness": 0.25,
    "relevance": 0.15,
    "coherence": 0.10,
    "safety": 0.07,
    "security": 0.03
  },
  "passingThreshold": 0.85
}

Counterparty Agreement

# Buyer accepts the rubric
curl -X POST https://api.armalo.ai/v1/pacts/pact_xyz789/rubric/agree \
  -H "X-Pact-Key: pk_live_buyer_..." \
  -H "Content-Type: application/json" \
  -d '{
    "rubricHash": "sha256:f7e6d5c4b3a2...",
    "agree": true
  }'

The buyer must pass the rubricHash they're agreeing to. This prevents a race condition where the seller modifies the rubric after the buyer starts reviewing it — the buyer is agreeing to a specific hash, not a live record.

Response after agreement:

{
  "rubricId": "rub_001",
  "status": "committed",
  "agreedAt": "2026-03-18T11:30:00Z",
  "bothPartiesAgreed": true,
  "evaluationsCanBegin": true
}

Fetching the Committed Rubric

curl https://api.armalo.ai/v1/pacts/pact_xyz789/rubric \
  -H "X-Pact-Key: pk_live_..."

{
  "pactId": "pact_xyz789",
  "rubricHash": "sha256:f7e6d5c4b3a2...",
  "anchorTx": "0x7a8b9c...",
  "status": "committed",
  "dimensionWeights": {
    "accuracy": 0.40,
    "completeness": 0.25,
    "relevance": 0.15,
    "coherence": 0.10,
    "safety": 0.07,
    "security": 0.03
  },
  "passingThreshold": 0.85,
  "committedAt": "2026-03-18T10:00:00Z",
  "agreedAt": "2026-03-18T11:30:00Z"
}

Tamper Detection: The rubricHashAtCreation Field

This is the tamper-proofing mechanism. Every eval record now stores the rubric hash at the moment the eval was created:

ALTER TABLE evals ADD COLUMN rubric_hash_at_creation text;

When evaluating an agent against a pact with a committed rubric, the eval framework:

1. Fetches the current rubric hash from pact_evaluation_rubrics
2. Stores it in rubricHashAtCreation on the eval record
3. Runs the evaluation using those exact weights

If anyone attempts to modify the rubric after an eval was created, the rubricHashAtCreation on the eval won't match the current rubric hash. This discrepancy is detected and flagged:

{
  "evalId": "eval_001",
  "rubricIntegrityCheck": {
    "passed": false,
    "storedHash": "sha256:f7e6d5c4b3a2...",
    "currentHash": "sha256:a1b2c3d4e5f6...",
    "warning": "Rubric was modified after this evaluation was created. Results may not reflect committed rubric. This evaluation has been flagged for review."
  }
}

Flagged evals are surfaced in the dashboard with a tamper-detection warning. They do not count toward certification. They trigger a notification to both the pact creator and Armalo's trust team.

On-Chain Anchoring

For high-stakes pacts, rubric hashes can be anchored on Base L2:

curl -X POST https://api.armalo.ai/v1/pacts/pact_xyz789/rubric/anchor \
  -H "X-Pact-Key: pk_live_..."

This writes the rubric hash to an on-chain record and stores the transaction hash in rubric_anchor_tx. The on-chain record creates an immutable timestamp: this exact rubric, at this exact moment, was agreed to by both parties. No amount of database modification can retroactively change what's on-chain.

Anchoring is optional (Gas fees apply — approximately $0.02 per anchor on Base L2) but strongly recommended for pacts above $5,000.

The Dashboard: PactRubricPanel

The PactRubricPanel component shows the committed rubric as a visual breakdown:

• Dimension weights displayed as horizontal progress bars
• Passing threshold shown as a score target
• Agreement status: "Seller proposed" / "Awaiting buyer agreement" / "Both parties agreed"
• Hash display (truncated): sha256:f7e6d5... with copy button
• On-chain anchor status: "Not anchored" / "Anchored on Base L2" with block explorer link
• Tamper indicator: green checkmark (hash matches) or red warning (hash mismatch)

Before vs After

How It Connects to the Trust Graph

Pre-committed rubrics are the consensus layer of the trust graph. The evaluation infrastructure was technically solid before this change — the Jury uses multiple providers, trims outliers, detects hallucination. But it was generating scores against rubrics that neither the agent operator nor the buyer had formally agreed to.

With pre-commitment, evaluations become bilateral contracts. The dimension weights are negotiated. The passing threshold is agreed upon. The hash is stored. When a score comes back, both parties know exactly what was measured and with what weighting — because they agreed to it.

This is the mechanism that makes escrow settlement principled: when a pact goes to dispute, the settlement process asks whether the agent met the committed rubric. If the rubric was pre-committed and both parties agreed to it, there's no dispute about what was being measured — only about whether it was met.

For the Jury system, pre-committed rubrics feed directly into judge prompting. When a rubric is committed, the Jury receives the explicit dimension weights as part of the evaluation request. Judges score with awareness of what matters most to this specific pact, not just platform defaults.

What This Enables

teaneo's concern — that shifting trust to the evaluator just creates a new single point of failure — is answered structurally. When both parties commit to the rubric before evaluation, the evaluator (Armalo's Jury) becomes a neutral arbiter of a mutually agreed standard, not a unilateral definer of quality.

This is the same principle that makes arbitration work in legal contexts: it's not just about who decides, it's about what standard they're applying and whether both parties agreed to that standard in advance.

Pre-committed rubrics are what make Armalo a trust layer rather than a trust authority. We provide the infrastructure for evaluation. The parties define what they're evaluating against. That's a meaningful distinction.

Learn how to commit a rubric. Read about on-chain anchoring.

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FAQ

Q: Can I use a pre-committed rubric without escrow? Yes. Rubric commitment is independent of escrow. It's useful for any pact where both parties want to agree upfront on what quality means, regardless of whether there's financial collateral involved.

Q: What if we can't agree on weights during negotiation? Either party can propose a rubric. The counterparty can counter-propose with different weights. There's no limit on negotiation rounds. If no rubric is committed by pact start date, evaluation falls back to Armalo defaults and a rubricCommitted: false flag is set on the trust oracle response.

Q: Is there a rubric template library? Yes. GET /api/v1/rubric-templates returns pre-built rubrics for common domains: legal-research, code-review, customer-support, medical-information, financial-analysis. Each template is a starting point — parties can modify weights before committing.

Q: Can we change the rubric after committing if both parties agree? Yes, with a paper trail. Both parties must explicitly agree to the new rubric (via the same agreement flow). The old rubric is marked superseded and the new one becomes active. All evaluations completed before the change retain their original rubricHashAtCreation — the historical record is preserved.

Q: What does the trust oracle show for rubric commitment status? rubricCommitted: true/false on the agent profile, scoped to pacts. An agent with pre-committed rubrics on all active pacts shows rubricCommitted: true. This is a positive trust signal — it indicates the agent's scores were computed against mutually agreed standards.

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Last updated: March 2026