{"slug": "a-framework-agnostic-testing-methodology-for-ai-agents-61-sources-58-test-blocks", "title": "A Framework-Agnostic Testing Methodology for AI Agents (61 sources, 58 test blocks, OWASP Agentic Top 10)", "summary": "A developer has open-sourced a framework-agnostic testing methodology for AI agents, including a 61-source benchmark map and 58 universal test blocks across seven tiers, with full coverage of the OWASP Agentic Top 10. The methodology includes evaluation techniques such as LLM-as-Judge, pass@k, and automated red-teaming, and aligns with regulatory frameworks like NIST AI RMF and the EU AI Act. A real-world case study using PheronAgent demonstrates its application.", "body_md": "How do you actually test an AI agent? Not \"does it respond,\" but: does it\n\nroute to the right tool, chain calls correctly, recover from failure, resist\n\nprompt injection, and stay within cost/latency budget?\n\nI spent weeks working through this on a running agent, and open-sourced the\n\nentire methodology — *framework-agnostic*, so it applies regardless of your\n\nlanguage, runtime, or toolset.\n\n• *61-source benchmark map* — BFCL, GAIA, τ-bench, SWE-bench, WebArena,\n\nAgentDojo, LongMemEval and more, categorized by what they actually measure\n\n• *58 universal test blocks* across 7 tiers (L1–L4, Error Recovery,\n\nMulti-Turn, Security). Each block = a tool-agnostic capability definition +\n\na concrete reference implementation\n\n• *Full OWASP Top 10 for Agentic Applications 2026* (ASI01–ASI10) mapped to\n\n6 universal security test blocks\n\n• *Evaluation methodology* — LLM-as-Judge biases, pass@k vs pass^k,\n\ntrajectory vs end-state, observability (OpenTelemetry GenAI), automated\n\nred-teaming (garak, PyRIT, DeepTeam)\n\n• *Regulatory alignment* — NIST AI RMF, MITRE ATLAS, EU AI Act, ISO/IEC 42001\n\nTake Part II, replace the reference-implementation fields with your own agent's\n\ntool names and expected outputs. The universal capability definitions need no\n\nchanges. Blank templates are included.\n\nPheronAgent (a macOS agent with 50+ native/MCP tools) is included as a real\n\nreference case study — but the methodology is the product, not the agent.\n\nNo marketing narrative: STORY.md documents the real bugs, real test runs, and\n\nreal corrections that shaped each version.\n\n*Docs are CC BY 4.0, templates are MIT.* Issues and PRs welcome.", "url": "https://wpnews.pro/news/a-framework-agnostic-testing-methodology-for-ai-agents-61-sources-58-test-blocks", "canonical_source": "https://dev.to/turgaysavaci/a-framework-agnostic-testing-methodology-for-ai-agents-61-sources-58-test-blocks-owasp-agentic-4jh7", "published_at": "2026-08-02 09:00:04+00:00", "updated_at": "2026-08-02 09:12:01.967096+00:00", "lang": "en", "topics": ["ai-agents", "ai-safety", "ai-policy", "developer-tools", "mlops"], "entities": ["OWASP", "BFCL", "GAIA", "τ-bench", "SWE-bench", "WebArena", "AgentDojo", "PheronAgent"], "alternates": {"html": "https://wpnews.pro/news/a-framework-agnostic-testing-methodology-for-ai-agents-61-sources-58-test-blocks", "markdown": "https://wpnews.pro/news/a-framework-agnostic-testing-methodology-for-ai-agents-61-sources-58-test-blocks.md", "text": "https://wpnews.pro/news/a-framework-agnostic-testing-methodology-for-ai-agents-61-sources-58-test-blocks.txt", "jsonld": "https://wpnews.pro/news/a-framework-agnostic-testing-methodology-for-ai-agents-61-sources-58-test-blocks.jsonld"}}