Harness Engineering Anti-Patterns AP4. Anthropomorphic Delegation 🎯 Point "A senior engineer wouldn't make that mistake" — are you skipping agent gates based on that assumption? Agents fail confidently, silently, and at scale. ❗ Problem Over-trusting agents as "smart colleagues" leads to omitting necessary guardrails and verification gates. Applying human trust models to agents — which lack the social controls of reputation, accountability, and shame — produces unpredictable failures. 🔍 Mechanism & Symptoms Agents speak like capable humans, making it tempting to reuse human mental models. But agents have no stakes, no persistent memory of consequences, and no shame. Their failure mode is non-human: they fail confidently, silently, and at scale. Where a human would pause thinking "this seems off," an agent charges ahead with conviction. Symptoms include: skipping CI gates because "the agent wouldn't push broken code," taking agent confidence at face value, and omitting checks based on the implicit assumption "a human would notice." 📋 Scenarios - CI gates are skipped because "this agent is senior level." The agent declares "done" without running tests, and broken code gets merged. - Review is skipped because the agent states "this change is safe." In reality, shared module behavior changed, taking downstream services offline. - During incident response, the agent's confident "root cause is X" analysis is trusted without investigation, while the actual cause Y goes unexamined. 🛡 How to Avoid - Position agents as "intelligent but stakeless, memoryless executors" and constrain them with structure (gates, verification, permissions) - Require agents to explicitly output "areas of low confidence" — demand calibrated uncertainty - When you feel "a human would be fine," that's precisely where mechanical checks are needed - Design guardrails with the premise that agent failure modes differ fundamentally from human ones #HarnessEngineering# #AIAgent#