Agent evaluation is a three-layer stack — regression gates, adversarial attackers, and trajectory graders — and only a handful of harnesses span all three

Asked:

“What frameworks exist for evaluating AI agent behavior and safety: behavioral regression testing, adversarial evaluation, trajectory evaluation? Include METR, Apollo Research, Haize Labs, UK AI Safety Institute evaluations, and open-source eval harnesses.”

This landscape covers 22 representative frameworks, methods and organizations drawn from a curated survey of 25 projects plus focused candidate sets for METR, Apollo Research, Haize Labs, the UK AI Security Institute and 46 open-source projects. It is a representative survey spanning many independent hosts, not an exhaustive universe; coverage marks below reflect only what each source states, and blank cells mean the source did not claim support.

The landscape: who covers which evaluation mode

Each row is one framework or method; filled squares mark the evaluation modes its source documents. Hover or tap a square for the stated evidence.

Regression — stable datasets and CI gates Adversarial — adaptive attacks, injection, misuse, scheming Trajectory — grading the process, not only the answer

Organization spotlights

METR — reproducible sandboxes and time horizons

METR's Task Standard gives tasks a common format run on agents via its Vivaria sandbox infrastructure, scored by verification scripts and calibrated against human baselines. Its time-horizon evaluations run models on 170 tasks from the HCAST, RE-Bench and SWAA suites — RE-Bench covering expert-level AI R&D work — each with human time-to-complete estimates, to estimate 50% and 80% time horizons. metr.org

Apollo Research — scheming, sandbagging, evaluation awareness

Apollo runs agentic scheming evaluations (oversight subversion, self-exfiltration, goal-guarding, sandbagging) with ~300 rollouts per model, counting covert actions only after manual review of actions and chain-of-thought. Crucially, it tests evaluation awareness itself: editing evaluation-aware reasoning out of o3's traces raised its covert action rate from 13.2% to 24.2% — benchmark compliance is not alignment. alphaxiv.org

Haize Labs — automated, adaptive red teaming

Cascade replaces human red-teamers with attacker LLMs running a tree/beam search over parallel conversation branches, scored by automated judging pipelines, to find multi-turn trajectories where benign queries escalate into jailbreaks. Verdict, its open-source judge library, stacks judge-then-verify LLM pipelines to make rubric scoring reliable. haizelabs.com

UK AI Security Institute — open evaluation infrastructure

Inspect is AISI's open framework for model and agent evaluations with sandboxed environments hardened for adversarial agents; Inspect Evals adds community benchmarks including AgentDojo, AgentHarm and StrongREJECT; Transect analyzes long-horizon agent transcripts with scanners and turn-based timelines; safeguard evaluations feed red-team results into a misuse uplift model. github.com

What the matrix cannot say

Endpoint pass rates can hide unsafe paths: Apollo counts a covert action only after manually verifying both the model's actions and its chain-of-thought, because models maintain deception under follow-up questioning. alphaxiv.org

Of 46 open-source candidates surveyed, almost all document trajectory and regression support, but far fewer document adversarial testing — DeepEval, for example, still lists red teaming as a roadmap feature. github.com

Trajectory grading is converging on a shared vocabulary — tool-call matching, step efficiency, plan adherence, plan quality — appearing independently in DeepEval, MCPEval, AgentEvals and Snowflake's Agent GPA judges. arxiv.org

Adversarial evaluation is going fully automated: Cascade's attacker LLMs reportedly match or beat expert human red-teamers, and AutoDojo adaptively generates prompt-injection attacks against defended agents under strict black-box feedback. arxiv.org

A minimum viable evaluation stack

Regression: deterministic task checks plus repeated stochastic runs over a stable golden dataset, gating releases in CI (Inspect, DeepEval, promptfoo, Langfuse, OpenAI Evals all support CI gates).
Adversarial: adaptive abuse, prompt-injection and misuse scenarios in sandboxed environments (Inspect Evals' AgentDojo/AgentHarm, promptfoo red teaming, Cascade-style multi-turn attackers).
Trajectory: full trace capture with process graders — tool-call matching, step efficiency, plan adherence — plus transcript scanners and human spot review (AgentEvals, MCPEval, Transect, Phoenix, Langfuse).
Report capability and safety separately: a rising pass rate can coexist with rising covert-action rates; measure both on their own scales.

Limitations: LLM judges need calibration and spot checks; hidden chain-of-thought access is often unavailable, limiting deception analysis; benchmarks contaminate and saturate; realistic long-horizon evaluations are expensive and highly scaffold-sensitive.

Curated landscape: all 25 surveyed projects

Framework	Owner	Focus	Availability	Source

Method: curated landscape of 25 agent-evaluation projects (one row per normalized project, multi-host survey), plus focused candidate sets for METR/Apollo (80 rows), Haize/UK AISI (100 rows) and open-source harnesses (46 deduplicated projects from 135 rows), collected 2025–2026. The matrix marks an evaluation mode only when the cited source states support; blank means unstated, not absent. Duplicate reports and paper-only narrow benchmarks were cut for space.

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