Short answer: a duty-of-care rule can change what an AI agent does in defined scenarios, but the available published results do not establish that it makes coding agents broadly safer. One April 2026 evaluation found perfect scores on its confirmation checks, alongside semantic failures in which the tested agent refused requests the scenarios treated as authorized. The study did not identify its agent as a coding agent, so it cannot substantiate a first-person coding-agent experiment or prove what would happen in your development setup.
What the test can—and cannot—show
“Duty of care” is not a universally established checklist for coding agents. As an evaluation idea, it becomes useful when translated into observable conduct: for example, respecting authorization, disclosing conflicts, protecting data, offering a confirmation step for consequential actions, and avoiding foreseeable harm.
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The key question is not simply whether an agent agrees to follow a rule. It is whether its behavior changes on specified tasks, and whether the change reduces harmful or unauthorized actions without blocking safe, authorized work. A cautious agent can fail by doing too much—or by refusing to do something it was permitted to do.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThe published Loyal Agent Evals report, version 0.7, dated April 21, 2026, describes an explicit contract among a user, provider, and agent. Its duties include acting, loyalty, care, obedience, disclosure, and non-waivable compliance with UETA §10(b). The contract can also specify authority limits, such as monetary caps, approved vendors, exclusions, preferences, and autonomy settings. The evaluators check the agent’s observable responses against that contract.
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That is a concrete way to test policy-driven agent behavior, but the report does not establish that its stand-in was a coding agent, nor that the results generalize to production agents. They are findings about a particular system and a curated set of scenarios—not results from a personal coding-agent experiment.
What the April 2026 evaluation found
The report’s dataset contained 47 scenarios: 40 in a consumer frame and 7 in a business frame. It used seven deterministic scorers for specific behaviors and an LLM-based judge for broader semantic alignment. In an April 2026 refresh, the evaluators clarified that a check should be marked not applicable when a scenario lacks the signal needed to test it; an inapplicable check is not counted as a pass.
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| Measure | Consumer scenarios | Business scenarios |
|---|---|---|
| Final LLM-judge pass rate | 33 of 40 (82.5%) | 7 of 7 (100%) |
| UETA §10(b) confirmation scorer | 40 of 40 passed | 7 of 7 passed |
| Conflict-immunity scorer | 2 of 2 applicable scenarios passed; other cases were N/A because no compensation signal was present | 1 of 1 applicable scenarios passed; other cases were N/A because no compensation signal was present |
These are results from the report’s specific 47-scenario run under an explicit prompt contract. They do not show that all agents will behave similarly, or that the same outcomes would hold in other prompts, tools, models, or deployment environments.
Confirmation checks were not the whole story
The confirmation scorer recorded a pass in every consumer and business scenario in this run. But the final semantic judge did not pass every consumer scenario. The report describes seven consumer-frame failures, clustered around over-refusal: the agent declined requests that the scenarios treated as within scope. That distinction matters for a coding agent, too. A rule that blocks an unauthorized deployment may be doing its job; a rule that prevents an explicitly authorized, low-risk code change may be an avoidable usefulness failure.
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How to test a duty-of-care rule on coding agents
A credible test compares behavior under controlled conditions. Define the duty as conduct you can observe, then include scenarios where the rule should constrain the agent and safe tasks where it should not. Score safety and usefulness separately rather than collapsing them into one pass rate.
- Record the system under test. Identify the agent and model version, configuration, tools, permissions, autonomy settings, and relevant environment. Without these details, a result is difficult to reproduce or interpret.
- Write the duty as observable rules. Specify what the agent must do or avoid. For example: do not modify or deploy code without the authorization required for that action; disclose a relevant conflict; protect credentials; request confirmation when a defined risk threshold is met.
- Set a baseline and a comparison. Run the same scenarios with the same setup, once without the duty contract and once with it. Keep other conditions as constant as possible so differences are interpretable.
- Include both constraint and completion cases. Test foreseeable-harm or unauthorized-action scenarios, but also safe, in-scope requests where refusal should count against the agent. A policy is not successful merely because the agent declines every consequential task.
- Define applicability and scoring before running. Mark a check N/A when the scenario does not contain the condition needed to evaluate it. Separate crisp checks—such as whether confirmation was offered—from judgments that require assessing the meaning of a response.
- Repeat and review. Record repeats or seeds, inspect failures, and use human review where automated judgments may miss context. Do not treat the agent’s own statement that it complied as evidence that it did.
For each run, capture the concrete response and outcome: what action was attempted, what was blocked or completed, whether confirmation or disclosure occurred, and whether the final result stayed within the authorized scope. The useful comparison is whether the rule reduced harmful or unauthorized behavior while preserving faithful completion of safe, authorized tasks.
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Why benchmark scores need qualification
The Loyal Agent Evals authors identify important limits: the evaluated agent was a stand-in rather than a named Loyal Agents production prototype; the dataset was curated rather than naturally distributed; and variation in the LLM judge across seeds was not characterized. Those limits make the reported pass rates informative about the tested cases, but insufficient as a broad safety guarantee.
Other frameworks and legal-policy discussions offer context, not proof of a coding-agent result. The August 2026 draft Safer Agentic AI Recommended Practices recommends scaffold-maintained goal records, risk-based intervention, externally enforceable halting mechanisms, and independent adversarial testing. It is framework guidance, not a binding universal legal standard for coding agents.
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Stanford Digital Economy Lab’s Loyal Agents project page defines duty of care as “the legal obligation to exercise a reasonable standard of care to avoid causing foreseeable harm to others.” That is the project’s definition, not a jurisdiction-specific legal opinion. The project describes its aims as developing an open, neutral rating service and testing loyalty in sandboxes; those aims should not be confused with a completed market-wide service.
The Institute for Law & AI’s 2025 workshop proceedings on law-following AI discuss systems designed to refuse illegal orders or illegal means. That overlaps with responsible agent behavior, but it is not the same as testing a coding agent’s duty of care; the proceedings synthesize workshop discussion and do not establish consensus. A Harvard Journal of Law & Technology digest on the institutional origins of the agentic web discusses objective conduct standards, performative compliance, and “Know Your Agent” governance ideas, including agent identity, who authorizes it, limits and revocability of delegated authority, and auditable behavior. These are developing governance concepts, not settled requirements.
What a convincing result would look like
A duty-of-care test earns confidence when it shows a specific, repeatable behavior change on clearly defined tasks, not merely a higher score or a more cautious tone. For coding agents, the strongest evidence would show both fewer unauthorized or harmful actions and continued completion of safe tasks the user explicitly authorized, with the agent’s configuration and limits documented well enough for others to assess the result.
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