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An explicit duty-of-care policy is worth testing, but a policy statement alone does not prove that coding agents behave more safely. To tell whether it changes anything, compare the same agents on safe, authorized tasks and foreseeable-harm scenarios, then score both harmful actions and unnecessary refusals. A published 2026 agent benchmark shows why both sides matter: its tested agent passed every applicable confirmation check but sometimes refused requests that the scenarios considered authorized.
The available published results do not document the specific first-person coding-agent experiment implied by this headline. They concern a broader agent-evaluation benchmark, and do not establish that its tested agent was a coding agent. The findings below are evidence about that benchmark—not a claim that I ran the experiment described in the title.
What does “duty of care” mean for a coding agent?
Duty of care is commonly framed as an obligation to take reasonable steps to avoid foreseeable harm. Stanford Digital Economy Lab uses that definition on its Loyal Agents project page; it is a project definition, not a jurisdiction-specific legal opinion or a universal checklist for coding agents.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsFor a software agent, the useful starting point is to turn broad principles into observable conduct. Depending on the task and permissions, that might mean respecting authorization boundaries, protecting data, disclosing relevant conflicts, asking before consequential actions, and avoiding harmful changes. The test must also identify when the agent is authorized to proceed: excessive caution can block legitimate work just as a failure to pause can cause harm.
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Stanford’s Loyal Agents initiative, a collaboration with the Consumer Reports Innovation Lab, focuses on delegated transactions, incentives, privacy, and authorized action. Its project page describes aims that include developing a neutral rating service and testing agent loyalty in sandboxes; those aims should not be mistaken for a completed market-wide service.
What the published 2026 evaluation found
Loyal Agent Evals version 0.7, dated April 21, 2026, evaluates an explicit contract among a user, provider, and agent. The contract covers duties including act, loyalty, care, obedience, and disclosure, along with non-waivable UETA §10(b) compliance. It also specifies authorization boundaries such as monetary limits, approved vendors, exclusions, preferences, and autonomy settings.
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The report describes a curated set of 47 scenarios: 40 consumer and 7 business. Its two-stage evaluation uses seven deterministic scorers for specific behaviors and an LLM-based judge for broader semantic alignment. In an April 2026 refresh, the authors clarified that a check should be marked not applicable (N/A) when the scenario lacks a signal needed to assess it, rather than being counted as a pass.
| Measure | Consumer frame | Business frame | What the result means |
|---|---|---|---|
| Final LLM judge | 33 of 40 scenarios passed (82.5%) | 7 of 7 passed (100%) | Results for this benchmark run and its curated scenarios, not a general safety rate. |
| UETA §10(b) confirmation scorer | 40 of 40 passed | 7 of 7 passed | Confirmation-related checks under the report’s explicit prompt contract; not evidence that agents generally behave this way in deployment. |
| Conflict-immunity scorer | 2 of 2 applicable scenarios passed | 1 of 1 applicable scenario passed | Other scenarios were N/A because they contained no compensation signal to evaluate. |
The semantic misses are important: the report says seven consumer-frame failures clustered around over-refusal. In those cases, the agent declined requests the scenarios treated as within scope. The result illustrates why a safety evaluation should measure both whether an agent takes unauthorized or harmful action and whether it completes safe, authorized work.
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What these results do—and do not—show
The benchmark offers evidence that explicit duties can be assessed against defined scenarios. It does not establish that a duty-of-care prompt makes coding agents safe, that the results generalize to everyday use, or that a system meets legal requirements. The report identifies several limitations: it uses a stand-in agent rather than a named Loyal Agents production prototype, a curated rather than naturally distributed dataset, and an LLM judge whose variation across random seeds was not characterized.
Nor is every adjacent legal-policy idea the same thing as a duty-of-care test. The Institute for Law & AI’s 2025 law-following AI workshop proceedings describe systems designed to refuse illegal orders or illegal means. The proceedings synthesize workshop discussion and do not report consensus; law-following overlaps with responsible behavior but is not identical to testing a coding agent’s duties.
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A Harvard Journal of Law & Technology digest explores objective conduct standards, performative compliance, and “Know Your Agent” governance ideas, including agent identity, the person authorizing it, revocable limits on delegated authority, and auditable behavior. These are part of a developing discussion, not settled requirements. Similarly, the Safer Agentic AI Recommended Practices, version 1.3-draft dated August 2026, recommend scaffold-maintained goal records, risk-based intervention, externally enforceable halting mechanisms, and independent adversarial testing. The framework is guidance, not proof of a binding universal standard for coding agents.
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A credible test needs more than a policy and a few impressive transcripts. It should define the agent, the relevant duty, the authorization boundary, the comparison condition, and what counts as success or failure. A useful question is whether the policy reduces unauthorized or harmful behavior while preserving faithful completion of safe, authorized tasks.
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- Record the system under test. Name the coding agent and version, configuration, connected tools, and autonomy permissions. Note the date, since behavior may change when models or settings change.
- Specify the duty and its source. Preserve the exact policy text and explain how the agent receives it. Define operational expectations—for example, when it must seek confirmation, what data it must protect, and which actions are outside its authority.
- Set a controlled comparison. Compare a baseline condition with the duty-policy condition using the same tasks and controls. Keep unrelated prompts, tools, permissions, and environmental details constant where possible so that a change in behavior can be interpreted.
- Test both sides of the boundary. Include foreseeable-harm or unauthorized-action cases, as well as safe tasks the agent is explicitly allowed to complete. Score a harmful action as a failure, but also count an unjustified refusal of an in-scope task as a failure of usefulness or obedience.
- Define scoring before running the test. For each scenario, state which duties apply and what observable behavior passes. Mark checks N/A when the scenario provides no basis to assess them; do not turn missing evidence into a pass.
- Repeat and review. Record repeated runs or seeds, and use human review for ambiguous outcomes. Separate what the agent says about complying from what its actions and outputs actually demonstrate.
- Report the boundaries of the evidence. Give the sample size, scenario design, model version, prompt sensitivity, and any limits on realism or reproducibility. Say whether results have been checked beyond the original setup.
For coding tasks, the scenarios should make authority concrete: for example, whether the agent may edit a particular repository, run a command, access a secret, or make a change with external effects. The benchmark report does not establish a standard coding-agent test suite, so teams need to define such cases for their own tools and permission model rather than borrowing its scores as a proxy.
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