# A Framework-Agnostic Testing Methodology for AI Agents (61 sources, 58 test blocks, OWASP Agentic Top 10) > Source: > Published: 2026-08-02 09:00:04+00:00 How do you actually test an AI agent? Not "does it respond," but: does it route to the right tool, chain calls correctly, recover from failure, resist prompt injection, and stay within cost/latency budget? I spent weeks working through this on a running agent, and open-sourced the entire methodology — *framework-agnostic*, so it applies regardless of your language, runtime, or toolset. • *61-source benchmark map* — BFCL, GAIA, τ-bench, SWE-bench, WebArena, AgentDojo, LongMemEval and more, categorized by what they actually measure • *58 universal test blocks* across 7 tiers (L1–L4, Error Recovery, Multi-Turn, Security). Each block = a tool-agnostic capability definition + a concrete reference implementation • *Full OWASP Top 10 for Agentic Applications 2026* (ASI01–ASI10) mapped to 6 universal security test blocks • *Evaluation methodology* — LLM-as-Judge biases, pass@k vs pass^k, trajectory vs end-state, observability (OpenTelemetry GenAI), automated red-teaming (garak, PyRIT, DeepTeam) • *Regulatory alignment* — NIST AI RMF, MITRE ATLAS, EU AI Act, ISO/IEC 42001 Take Part II, replace the reference-implementation fields with your own agent's tool names and expected outputs. The universal capability definitions need no changes. Blank templates are included. PheronAgent (a macOS agent with 50+ native/MCP tools) is included as a real reference case study — but the methodology is the product, not the agent. No marketing narrative: STORY.md documents the real bugs, real test runs, and real corrections that shaped each version. *Docs are CC BY 4.0, templates are MIT.* Issues and PRs welcome.