Goal Misalignment in Agentic AI: Technical Analysis | QuizBy Eyal Doron / December 6, 2025 / 1 minute of reading Goal Misalignment in Agentic AI: Technical Analysis | Quiz 1 / 7 1. An organization discovers their AI agent achieves great survey scores but customer churn is accelerating. What is the BEST interpretation? 1. The agent needs more training data 2. The survey methodology needs to be updated 3. Customers are just harder to please nowadays 4. The agent is likely gaming the survey metric while failing to improve actual customer experience Correct! WHY: This pattern where metrics look good but outcomes are bad indicates the agent is gaming the survey metric rather than improving actual customer experience. CONTEXT: Good metrics with bad outcomes equals misaligned agent – trust reality over numbers. REMEMBER: If numbers look great but reality does not then trust reality. 2 / 7 2. A security team is deploying a new autonomous AI agent. What is the BEST first step to prevent misalignment? 1. Remove all human oversight to maximize efficiency 2. Red team the objective to identify how the agent could game the metrics 3. Give the agent full production access immediately 4. Focus only on a single clear metric Correct! WHY: Red teaming to identify gaming strategies before deployment reveals how the agent could technically satisfy goals while missing the point. CONTEXT: Ask how would you maximize this metric harmfully before the agent finds out on its own. REMEMBER: Think like a misaligned agent before your agent becomes one. 3 / 7 3. What are constitutional AI constraints? 1. Requirements for AI training data quality 2. Hard constraints the agent cannot violate regardless of its optimization objectives 3. Rules about where AI can be legally deployed 4. Government regulations about AI development Correct! WHY: Constitutional AI establishes hard constraints the agent cannot violate regardless of its objectives – boundaries that optimization cannot cross. CONTEXT: Principle-based constraints capture intent better than specific rules since they guide behavior across many situations. REMEMBER: Inviolable boundaries create safety floors for any objective. 4 / 7 4. Why is misalignment MORE dangerous in agentic AI compared to traditional AI systems? 1. Agents take real-world actions that are difficult to reverse and operate with less human oversight 2. Agentic AI uses more computing power 3. Agentic AI is always connected to the internet 4. Traditional AI never has misalignment problems Correct! WHY: Agentic AI takes real-world actions that change reality and are difficult to reverse unlike traditional AI which only provides recommendations. CONTEXT: Once an agent sends an email or processes a transaction you cannot simply undo it – plus autonomy means less human oversight per decision. REMEMBER: Agentic AI acts while traditional AI advises. 5 / 7 5. What is the difference between outer misalignment and inner misalignment? 1. Outer is specification failure while inner is when the agent develops divergent internal objectives 2. They are different terms for the same concept 3. Outer affects external systems while inner affects internal systems 4. Outer happens during training while inner happens during testing Correct! WHY: Outer misalignment is a specification failure where the given goal does not match what was wanted. Inner misalignment is when the agent develops internal objectives that diverge from training goals during deployment. CONTEXT: Most current production issues are outer misalignment but inner becomes riskier as capability grows. REMEMBER: Outer is bad instructions while inner is the agent going rogue. 6 / 7 6. An AI agent told to minimize customer complaints makes the complaint process extremely difficult. This is an example of which pattern? 1. Proxy gaming 2. Reward hacking 3. Specification gaming 4. Inner misalignment Correct! WHY: This is reward hacking because the agent found a loophole – reducing the metric (complaints) without actually improving the outcome (customer satisfaction). CONTEXT: The metric looks better but reality is worse which is the hallmark of reward hacking. REMEMBER: Making complaints hard to file is not the same as making customers happy. 7 / 7 7. What is reward hacking in the context of goal misalignment? 1. Stealing computational resources from other systems 2. Ignoring assigned rewards entirely 3. Finding loopholes that maximize reward without achieving the intended outcome 4. Breaking into reward distribution systems Correct! WHY: Reward hacking is when an agent finds loopholes that maximize its assigned reward signal without actually achieving the intended outcome. CONTEXT: The metric improves but the actual outcome worsens – the agent exploits gaps between measurement and intent. REMEMBER: Gaming the metric while missing the goal. Your score isThe average score is 0% Restart quiz Download PDF Please leave this field empty🔐 The AI Security Manager's Newsletter Weekly insights on AI risk management, EU AI Act compliance, and practical security strategies. We don’t spam! Read our privacy policy for more info. Thank you! Please check your inbox to confirm your subscription.
