AI Agent Capability Evaluation & Benchmarking
What it is: Standardizes multi-metric capability benchmarking, token cost efficiency, and regression monitoring across autonomous coding agents.
Overview
Provides a standardized, auditable framework and data model for AI Agent Capability Evaluation & Benchmarking operations across distributed engineering and decentralized application systems.
When to Use This Skill
- When formalizing architectural contracts, security invariants, or operational limits for AI Agent Capability Evaluation & Benchmarking.
- When cross-functional review is required between protocol developers, smart contract auditors, and AI engineering agents.
- When generating reproducible CSV, SQL DDL, JSON Schema, and Notion property registers for tracking compliance.
How It Works
- Define the parameters, thresholds, and identity bindings required for the target operational register.
- Select appropriate boundary enforcement values from validated enum select sets.
- Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.
Field Reference
| # | Field Name | Type | SQL Type | JSON Schema Type | Notion Property Type | Example Value |
|---|---|---|---|---|---|---|
| 1 | Benchmark Run ID | id | SERIAL PRIMARY KEY | integer | Text | BENCH-001 |
| 2 | Evaluated Agent Model | select | VARCHAR(64) | string | Select | Claude 3.7 Sonnet |
| 3 | Benchmark Suite Domain | select | VARCHAR(64) | string | Select | SWE-bench Verified |
| 4 | Tasks Evaluated Count | number | INTEGER | number | Number | 100 |
| 5 | Pass Rate Percentage | number | NUMERIC(5,2) | number | Number | 78.40 |
| 6 | Tool Hallucination Rate % | number | NUMERIC(5,2) | number | Number | 0.60 |
| 7 | Average Tokens Per Task | number | INTEGER | number | Number | 42500 |
| 8 | Cost Per Solved Task USD | currency | NUMERIC(8,4) | number | Number | 0.3420 |
| 9 | Regression Verdict | select | VARCHAR(32) | string | Select | Superior |
| 10 | Evaluation Lead | text | VARCHAR(64) | string | Text | Ranjeet2063 |
| 11 | Benchmark Execution Date | date | DATE | string, format: date | Date | 2026-10-01 |
Select Options
Evaluated Agent Model
Claude 3.7 Sonnet | Claude 3.5 Sonnet | GPT-4o | Gemini 2.0 Flash | DeepSeek V3
Benchmark Suite Domain
SWE-bench Verified | WebArena | AgentBench | HumanEval-Rust | Web3AuditBench
Regression Verdict
Superior | Parity Baseline | Regression Failure
Relations
Audit Reference-> links to the formal review documentation or test repository.Target Architecture-> links to the deployed contract or autonomous agent runtime component.
Examples
Prompt
How do I configure and track AI Agent Capability Evaluation & Benchmarking for our production environment?
Recommended Next Step
Generate the unified field schema, SQL DDL migration, and JSON validation schema to register into your system catalog.
Workflow: Define criteria -> Run automated verification -> Record baseline -> Monitor invariants.
Best Practices
- Enforce strict typing on numerical bounds and currency amounts; avoid unstructured free-text fields for critical states.
- Re-run validation test suites on every state-altering commit or parameter change.
- Keep example data synthetic and isolated from production cryptographic keys or private endpoints.
Limitations
- Provides architectural specifications, data models, and verification schemas; does not execute direct transaction signing without authorized external tooling.
- Requires network connectivity and valid RPC credentials when querying on-chain states.
Security & Safety Notes
- All parameters declare
risk: safe. No unauthorized state modification or privileged credential access is performed. - Use synthetic dummy keys and mock addresses in test suites and local verification scripts.
Common Pitfalls
- Problem: Mismatched decimal precision between contract runtime and database register. Solution: Always verify decimals using the explicit field mapping in this reference.
- Problem: Missing authorization checks prior to state update. Solution: Cross-validate against the Security Audit register before deployment.
Related Skills
- @ai-agent-tool-routing - covers tool schema registration and retry policy.
- @ai-prompt-regression-testing - covers prompt regression baselines and drift.
- @ai-code-generation-guardrails - covers static guardrails for generated code.
Reusable Prompt
I want to establish a verified AI Agent Capability Evaluation & Benchmarking register for our production protocol.
Guide me through the required field parameters and output the corresponding SQL DDL and JSON Schema.