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AI Agents in HR: A Practical Guide

AI Agents in HR: A Practical Guide

Enterprise Agents

A practical guide to deploying AI agents in HR, covering reliable use cases, hiring decision risks, and sensitive data handling.

HR is a genuinely promising but genuinely sensitive area for AI agents, since the same automation that saves real time can also introduce real fairness and compliance risk if deployed carelessly.

Initial resume screening for basic qualifications, answering routine policy questions from an internal knowledge base, scheduling and coordination for interviews and onboarding, these are the areas AI agents help HR teams reliably today.

AI systems used in hiring decisions face real, specific legal scrutiny in multiple jurisdictions around bias and fairness. Any AI involvement in screening or ranking candidates needs a human decision-maker in the loop and should be auditable for disparate impact, not run as an unsupervised black box.

HR systems hold genuinely sensitive personal data, and any AI tool touching it needs clear data handling policies. Our vendor selection checklist covers the specific data questions worth asking before adopting any HR AI tool.

AI agents genuinely help with routine HR administration, but hiring decisions and sensitive data both need real human oversight built in from the start. See McKinsey’s own research on enterprise AI adoption.

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How Coding Agents Handle Testing

How Coding Agents Handle Testing

Coding Agents

An explainer on how AI coding agents handle testing, covering reliable use cases, feedback loops, and where human review remains necessary.

Testing is where coding agents genuinely earn their keep, since a good test suite gives the agent a way to verify its own work rather than you having to catch every issue manually.

This is one of the most genuinely reliable use cases for coding agents today: generating test coverage for code whose intended behavior is already clear. The agent has a well-defined target to test against.

More advanced agent workflows run tests automatically after generating code, then use failures to inform the next revision, closer to how a human developer works iteratively. This loop is part of why multi-step multi-agent systems often assign a dedicated testing subagent.

An agent can write tests that pass while still missing genuinely important edge cases it didn’t think to consider, since it’s testing against its own understanding of the requirements. Existing test coverage for the surrounding code, and human review of what the generated tests actually check, remain necessary safeguards.

Coding agents strengthen their own reliability when they can run and act on real test results, but the tests they generate still need human review. See Terminal-Bench for coding-agent results.