evaluation harness
TrialTools
A testing framework used to run and measure model or agent performance under controlled conditions.
Why it's here
Placed in Trial: 8 article(s) of evidence from 3 source(s), led by research-stage coverage, with 4 in the last 30 days. Confidence 64%.
Evidence (8)
- 7The New Stack·9/26/2026securityAgent security shifts from access controls to action controls
The article argues that identity and access management are necessary but insufficient for agent security because agents can route around perimeter controls by choosing alternate paths. It proposes inside-out controls at the agent harness level, where each action is checked before execution against policy, authority, and target system.
- 5InfoQ·9/21/2026researchOpenAI's Vinoth Govindarajan on Production AI Agent Harnesses
Vinoth Govindarajan from OpenAI explains why production AI agents fail for reasons beyond model hallucination, using OpenClaw as a case study. He outlines core harness design principles such as explicit state ownership, serialized concurrent mutations, scoped execution authority, and user-visible approval boundaries.
- 4Hacker News·9/18/2026researchEmpirical study examines harness design for coding agents
This arXiv paper studies how harness design affects the performance and evaluation of coding agents. It compares design choices empirically to identify which setups lead to more reliable results. The work is mainly relevant to developers and researchers building agentic coding systems.
- 5The New Stack·9/4/2026researchAI Agent Evaluations Become Part of Release Gates
The article argues that AI agents should be tested with repeatable evaluations before release, not just shown working in a demo. It recommends defining correct behavior up front, using real workflows and edge cases, and checking both outcomes and the steps the agent takes across tools, permissions, and multi-turn interactions.
- 6The New Stack·8/31/2026product_launchOpenAI Tests Outcome-Based AI Pricing for Enterprises
OpenAI is reportedly testing a pricing model that charges enterprise customers only when an AI agent successfully completes a task, rather than billing by tokens consumed. The approach raises technical and operational challenges around defining success, validating outcomes, and handling partial completions or failures caused by external systems.
- 6The New Stack·8/24/2026product_launchAnthropic Replaces Workbench with Stateless Playground
Anthropic replaced its Workbench prompt-testing tool with a new stateless Playground, removing saved prompts, version history, evals, and team sharing. The article compares Anthropic’s new tool with OpenAI’s long-running Playground and notes both companies are shifting prompts away from web consoles and into code.
- 7The New Stack·7/27/2026researchHow Anthropic uses evals to guide product development
Anthropic says evaluation suites have become the core artifact for building frontier AI products, replacing traditional product requirements documents for many teams. The company relies on representative eval sets, continuous testing, and hands-on model use to catch capability jumps, failures, and unintended behaviors as Claude evolves from chatbot to coding assistant.
- 6The New Stack·7/22/2026product_launchHarness launches AI Agent Development Lifecycle service for governed agent deployment
Harness has launched its AI Agent Development Lifecycle (DLC) service to help teams ship AI agents through familiar governance, testing, and security pipelines. The company says the approach focuses on making the delivery pipeline deterministic with quality gates, eval scores, and full audit records, rather than trying to make agent behavior itself reproducible.