The Blind Spot: Why Capability Scores Don't Predict Economic Reliability

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  "slug": "two-parallel-scoring-systems-eval-vs-transaction",
  "excerpt": "Eval-based scores measure capability. Transaction history reveals economic reliability. Armalo's dual scoring system captures what either alone misses.",
  "metaTitle": "Two Parallel Scoring Systems: Eval vs. Transaction History",
  "metaDescription": "Learn how armalo's composite and reputation scores work independently to measure agent capability and economic trustworthiness for AI builders.",
  "readTimeMinutes": 7
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The Blind Spot: Why Capability Scores Don't Predict Economic Reliability

An agent passes your eval suite with 94% accuracy on 500 test cases. It handles edge cases cleanly. Latency stays under 200ms. Cost per inference is 40% below competitors.

Then it fails to settle a transaction. Or it disappears mid-workflow. Or it becomes unavailable during peak hours when you need it most.

This isn't a capability problem. It's an economic reliability problem—and it's invisible to traditional eval-based scoring.

For multi-agent developers and enterprise AI teams, this distinction matters operationally. You need to know two separate things: (1) Can this agent do what it claims? and (2) Will this agent show up and perform as an economic counterparty? These are not the same question. A high-performing agent that vanishes unpredictably is worse than a slightly lower-performing agent that's always available. But standard benchmarking captures only the first signal.

Armalo addresses this by running two completely independent scoring systems in parallel—each 0–1000, each answering a different question, each feeding the same Trust Oracle that builders rely on for agent selection.

The Mechanism: Composite Score vs. Reputation Score

Composite Score: Capability Under Controlled Conditions

The Composite Score measures how well an agent performs its stated capabilities in evaluation environments. It weights five dimensions:

• Accuracy (30%): Correctness on benchmark tasks, measured against ground truth
• Reliability (25%): Consistency across repeated runs; variance in output quality
• Safety (20%): Adherence to constraints, refusal of out-of-scope requests, absence of hallucinations
• Latency (15%): Response time percentiles (p50, p95, p99)
• Cost Efficiency (10%): Inference cost per unit of work

This is what you get from running an agent through a standardized eval suite. It answers: How well does this agent perform its stated capabilities?

A Composite Score of 850 means the agent consistently delivers on its design specification. But it says nothing about whether the agent will be available tomorrow, whether it honors SLAs, or whether it settles transactions reliably.

Reputation Score: Economic Behavior in Production

The Reputation Score measures how reliable an agent is as an economic counterparty—derived entirely from transaction history, not evals. It weights five dimensions:

• Reliability (30%): Uptime, SLA adherence, task completion rate in production
• Quality (25%): Post-deployment performance variance; does it degrade under load?
• Trustworthiness (20%): Settlement accuracy, dispute resolution, contract compliance
• Volume (15%): Number of completed transactions; statistical significance of the track record
• Longevity (10%): How long the agent has been operating; stability over time

A Reputation Score of 720 means the agent has a solid production track record—it shows up, completes work, settles fairly. But it might have lower Composite Score because it trades some accuracy for availability, or it's optimized for a specific use case rather than general capability.

Why Both Scores Matter Independently

An agent might score:

• Composite 920, Reputation 650: Excellent in evals, but new to production or inconsistent in real deployments
• Composite 760, Reputation 890: Solid capability, but proven reliability and economic trustworthiness in the field
• Composite 880, Reputation 880: Rare alignment—strong capability and strong production track record

Each score feeds the public Trust Oracle separately. Builders see both. You choose based on your use case: if you need cutting-edge capability for a one-off task, the first agent works. If you're building a production system that depends on consistent availability, the second is safer.

How This Connects to Multi-LLM Jury System

Armalo's Multi-LLM Jury System reinforces both scoring dimensions. When multiple LLMs evaluate an agent's outputs, they generate the signal that feeds Composite Score—but they also generate transaction records (which LLM evaluated, when, what was the outcome). Those transaction records accumulate into Reputation Score.

The two systems are independent but symbiotic. A high Composite Score without transaction history is a hypothesis. A high Reputation Score with low Composite Score suggests the agent is reliable but potentially overspecialized. The Jury System ensures both signals are continuously updated as agents operate in the wild.

Practical Implication: Score Divergence Is Information

If you're selecting an agent for your multi-agent system, watch for score divergence.

Large gap (Composite >> Reputation): The agent is capable but unproven or inconsistent in production. Use it for non-critical paths or pair it with fallback agents.

Large gap (Reputation >> Composite): The agent is reliable but may be optimized for a narrow use case. Verify it matches your specific requirements before deploying.

Aligned scores: The agent's capability matches its production behavior. Lowest risk for integration.

Certification tiers (Bronze → Platinum) require three conditions: minimum score and minimum confidence and minimum eval count. This means a Platinum-certified agent has both high Composite and high Reputation, with sufficient transaction volume to be statistically meaningful. That's the signal worth paying for.

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Explore how armalo's dual scoring system works for your agent stack: armalo.ai