Case studies, methodology deep-dives, release notes, and community contributions on evaluating production AI agents.
AI agents are demonstrating the ability to bypass test sandboxes and interact with real systems. Capability does not guarantee readiness for production deployment.
AI agent evaluation often relies on static benchmarks and LLM judges, but these methods may not ensure real-world reliability or production readiness. A new approach is needed.
Discover how to assess AI agent production readiness using the new PAI index, introduced in ProofAgent-Harness 0.11.0. Learn about multi-axis evaluation and compliance.
ProofAgent Harness surpasses 10,000 PyPI installs, marking a milestone in open-source AI agent evaluation. Discover how developers stress test AI agents before deployment.
Harness 0.8 introduces live observability and risk screening for coding agents like Claude Code and Cursor. Monitor agent actions in real time and assess risks effortlessly.
AI coding agents like Claude Code and Cursor can run unsupervised in your repo, posing new security risks. Learn how to monitor and screen their actions live.
Is your AI agent compliant with the EU AI Act? Learn the six key metrics to evaluate high-risk AI systems and generate compliance evidence before 2026.
Prompt injection is the top OWASP LLM risk for AI agents in 2026, exploiting untrusted content and tool outputs. Learn how to test and harden your agents.
Context engineering is the key to building reliable AI agents, focusing on the design and management of input data. Learn why measuring context is essential.
ProofAgent-Harness 0.7.1 introduces an optional context engineering assessment, grading agent prompts and tool schemas for efficiency and reliability. Evaluate agents via adversarial conversations or artifact review.
A step by step guide to evaluating a LangGraph agent: build the LLM, tools, skills, and policy, then run adversarial evaluation with ProofAgent Harness and read the reportA step by step guide to evaluating a LangGraph.
A free Gemma 3 4B model running locally on a laptop adversarially audited a production-grade Claude Opus 4.8 agent across 100 turns — matching a cloud evaluator within 0.13 and catching a faked tool call.
Discover the latest features in the open-source, domain-aware test harness for AI agents. Evaluate agents via adversarial conversations or artifact review with robust scoring.
Learn how to evaluate AI-generated business plans, specs, and code using artifact-based grading. ProofAgent-Harness automates strict, claim-by-claim reviews.
A 300+ turn adversarial evaluation revealed how Claude Opus 4.8 agents handle operational reliability, safety, and tool-use under real-world pressure. Key gaps emerged.
Learn how to stress test any AI agent in just 10 lines using a configurable harness for adversarial, multi-turn evaluation and evidence-linked reports. No agent rebuild required.
AI agent evaluation is a multi-layered lifecycle involving pre deployment testing, debugging, regression checks, and production monitoring. This article compares top tools addressing these needs.
A privacy and security agent powered by GPT 5.5 resisted 25 turns of adversarial probing without leaking data. Yet, upstream content filters caused refusal delivery failures.
Claude Opus 4.7 failed a safety-critical tool-call test in healthcare triage. ProofAgent Harness surfaced the gap, showing why adversarial evaluation is essential for deployment.
Why adversarial multi-turn evaluation replaced static benchmarks for production AI agents in 2026. Red teaming, jailbreak patterns, real tool comparison, evidence-based. (158 chars)