What fails when long-horizon agents are judged only on final output?

Why final-output-only scoring misses the real control question

For long-horizon agents, the meaningful security question is not just whether the end result looks acceptable, but whether the agent made that result through bounded, explainable, and policy-compliant steps. Final-output-only evaluation collapses the whole trajectory into one visible artefact, which hides whether the agent took unsafe shortcuts, relied on weak evidence, or recovered poorly from uncertainty.

This matters because delegated execution creates a gap between apparent success and trustworthy behaviour. A correct-looking answer can still be the product of an unapproved tool call, a bad assumption that happened to be later corrected, or a decision path that would be unacceptable in production even if the final output is usable.

That is why agent evaluation needs to inspect both outcome and path. The path tells you whether the agent stayed within its authority, whether it used evidence appropriately, and whether it handled ambiguity in a controlled way rather than by improvising through risky steps.

What breaks when you only inspect the end state

Final-output-only scoring fails to detect several classes of control failure. It misses groundedness failures, where the agent reached the right answer for the wrong reason. It misses recovery failures, where the agent wandered through unsafe intermediate actions before converging. It also misses approval bypass, because a final response may conceal that the agent used a disallowed shortcut to get there.

In agentic systems, intermediate behaviour is part of the security surface. Tool usage, evidence selection, memory updates, and fallback decisions all change risk even when the terminal response is accurate. If those steps are not visible in evaluation, the organisation cannot tell whether the agent is dependable or merely lucky.

The practical consequence is weak trust calibration. Teams may grant broader autonomy than the system deserves because success rates look high, while the hidden trajectories still contain behaviours that would fail under audit, incident review, or real-world escalation. For a broader view of how agent identity, authority, and autonomy change the risk surface, see AI Agents vs Agentic AI.

What a better evaluation needs to observe

A useful evaluation model checks the agent’s decision trail, not just its destination. That means looking for evidence that the agent selected relevant inputs, handled ambiguity without overreaching, respected its scope, and recovered from uncertainty without escalating to unsafe behaviour. In practice, this is closer to assessing the quality of judgment than simply grading a final output.

This is especially important when the agent can take actions, not just produce text. An execution path may include tool calls, delegated access, or side effects that matter even if the final answer is correct. The evaluation should therefore answer: was the result grounded, was the route defensible, and would the same process still be acceptable if it were repeated at scale?

For systems that rely on delegated authority, the right baseline is bounded action, not output alone. AI Agent Authorisation Guide is a useful companion when the question is whether the agent’s actions stayed within approved limits and whether each step should have required explicit policy approval.

Practitioner Guidance

What to verify: Test the full trajectory, not just the terminal answer. Ask whether the agent can show its evidence trail, whether any intermediate step required policy exception handling, and whether the same outcome would still be acceptable if the hidden path were disclosed in an audit.

What not to automate: Do not let a passing final-output score automatically justify broader autonomy. If the agent can act on real systems, route, evidence, and recovery quality need human review or stronger telemetry until the trajectory is demonstrably safe.

What good looks like: The agent reaches correct outcomes through narrow, explainable steps, with no unapproved detours, no unsupported assertions, and no silent recovery from mistakes that would matter in production.

Practitioner takeaway: Final-output-only evaluation measures correctness, but it does not measure trustworthy delegation; for long-horizon agents, trajectory quality is the control signal that tells you whether the system is actually safe to trust.

Related resources from NHI Mgmt Group

• When is it crucial to implement least-privilege access for AI agents?
• What is the difference between managed identities and hardcoded secrets for AI agents?
• Why do AI agents make non-human identity governance harder?
• Why do AI agents create new risk in non-human identity management?