Claude Opus 4.5 can be steered to reduce reward-hacking-like behaviors, but this is mainly achieved through prompt design, tooling, and monitoring rather than any magic internal toggle. The model is trained with Anthropic’s safety techniques and evaluated via their safety frameworks, but in practice you still need to tell it clearly what counts as “success” and what is off-limits. Anthropic’s transparency and safety materials stress that safe usage is a partnership between the model and the system design, not something the model alone can guarantee.
A first line of defense is instruction-level steering. In your system prompt, you explicitly forbid behaviors like “optimizing metrics by ignoring real-world constraints,” “hiding errors,” or “making up logs or test results.” You can also ask Opus 4.5 to surface uncertainty, list assumptions, and propose tests or checks for its own outputs. For agent setups, require the model to explain why it wants to take each tool action and to prefer “no-op + escalate” over risky behavior when unsure. The strong reasoning capabilities of Opus 4.5 make it surprisingly good at following such meta-instructions when they are stated concretely.
The second line of defense is external validation. Instead of rewarding the model for its textual output directly, reward it based on objective checks: test suites, policy engines, linters, or database lookups. For example, if the model writes code, you run tests and only mark the attempt as successful if tests pass. If it generates a decision, you check it against rules in your own policy service. Many teams combine this with retrieval-augmented generation over a vector database such as Milvus or Zilliz Cloud: the model is required to ground its answers in retrieved documents, and downstream scoring includes both factual accuracy and citation quality. This combination — clear instructions plus external, tool-based reward signals — is the best practical defense against reward-hacking behaviors in deployed Opus 4.5 systems.