currenttooladvanced
UK AI Safety Institute · added 2026-07-07
Live link, checked 2026-07-07
OSS framework (MIT) from the UK AISI that has become the de facto standard for agent safety evals — Apollo, METR, Anthropic, and OpenAI publish risk evaluations on it. Built around task/solver/scorer decorators; one run produces a serialized log with both scores and trajectories. The right starting point if you need reproducible, sandboxed agent evaluation rather than a fast CI regression suite.
Why it's here
- Provenance
- seed
- Rubric score
- 10/10
- Rubric note
- Government lab tool that is the consolidation point for agentic safety eval. Reproducible logs and trajectory capture are exactly the verification machinery Loopmaster advocates. Distinct from the CI-regression tools (Promptfoo, DeepEval).
currenttoolintermediate
Promptfoo · added 2026-07-07
Live link, checked 2026-07-07
`promptfoo eval`, get a comparison table plus red-team/adversarial coverage. Best fit for fast CI regression suites, model A/B, and security probing across providers. Pair with Inspect AI for safety campaigns and DeepEval for Python-native metric gates.
Why it's here
- Provenance
- seed
- Rubric score
- 9/10
- Rubric note
- Widely adopted OSS with a distinct CLI/YAML ergonomics and best-in-class red-teaming. Durable as the CI-regression default. Overlaps the eval-tools cluster on the general eval angle but its declarative-CI shape is materially different from Inspect/DeepEval.
currentrepointermediate
Confident AI · added 2026-07-07
Live link, checked 2026-07-07
metrics (G-Eval, faithfulness, relevancy, hallucination) with thresholds; fail builds in CI. Best fit for Python teams who want evals as code in their existing test suite. Ships RAG and agent-task metrics; pair with Promptfoo for declarative comparison.
Why it's here
- Provenance
- seed
- Rubric score
- 9/10
- Rubric note
- Maintained OSS, the pytest-shaped eval tool. Durable because the metric set and the pytest integration are stable abstractions. Uniqueness is moderate vs Promptfoo (the two are the standard pair) but the Python-native shape is distinct.
currentessayintermediate
Hamel Husain · added 2026-07-07
Live link, checked 2026-07-07
Argues the root cause of unsuccessful AI products is a failure to build robust eval domain expert as the single quality decision-maker, then build automated evaluators for repeated failure modes. The mindset piece that makes the eval tools worth using.
Why it's here
- Provenance
- seed
- Rubric score
- 10/10
- Rubric note
- Husain is the most cited practitioner voice on LLM evals. The error-analysis-first methodology is distinct from the tool-focused entries and is the meta-lesson the tools serve. Directly actionable.
currentessayintermediate
Hamel Husain · added 2026-07-07
Live link, checked 2026-07-07
If an AI artifact is hard for you to verify, it is probably hard for users too — and that is a product-design problem, not an evals problem. Walks through designing products so the output is checkable (provenance, progressive disclosure, smaller accept/reject units) before you reach for automated graders. The missing link between "build the loop" and "verify the loop."
Why it's here
- Provenance
- seed
- Rubric score
- 10/10
- Rubric note
- Same authority as the evals post. The "design for verifiability" angle is distinct and directly relevant to loop design — the output shape determines what verification is even possible. Not duplicated by the tool entries.
currentessayadvanced
Hamel Husain · added 2026-07-07
Live link, checked 2026-07-07
box, then step-level diagnostics (tool choice, parameter extraction, error handling, context retention, efficiency) using transition-failure matrices. The transition-matrix technique turns overwhelming trace review into a focused debugging map. The most directly applicable eval write-up for agent loops in the library.
Why it's here
- Provenance
- seed
- Rubric score
- 10/10
- Rubric note
- Co-authored with Shreya Shankar from teaching 700+ engineers. The transition-matrix method is a concrete, agent-specific eval technique not covered by the tool entries or the general evals post. The most on-point eval resource for Loopmaster's verification pillar.
currentessayadvanced
Cognition · added 2026-07-07
Live link, checked 2026-07-07
feedback, evaluator agents that judge outcomes with shell/edit/browse tools, and explicit modeling of user intent to auto-detect deviations. The "evaluating agents with agents" and "critiquing is easier than solving" observations are durable lessons for anyone building a verification loop for coding agents.
Why it's here
- Provenance
- seed
- Rubric score
- 10/10
- Rubric note
- Cognition (Devin team), production coding-agent evals. The evaluator-agents and intent-deviation-detection patterns are distinct from the academic benchmarks and the general eval essays. Directly relevant to coding-agent verification.
currentrepointermediate
OpenAI · added 2026-07-07
Live link, checked 2026-07-07
models. Now overtaken by Promptfoo and Inspect AI for greenfield projects, but the public registry of 100+ eval definitions is a useful reference for how others structure evaluations. Treat as a reference library, not the default starting tool.
Why it's here
- Provenance
- seed
- Rubric score
- 7/10
- Rubric note
- OpenAI-authored, authoritative. Lower durability and actionability because newer tools overtook it; the value is the reference eval library. Included as the reference point, not a recommended default — the annotation says so honestly.