Grading only the final answer misses most failures — so what should evaluators ask to see about how the work got done?

This reads the question as: what should a grader ask to see besides the final answer or deliverable? The corpus says a lot, mostly from agent evaluation, and one study covers human writing and coding. Its consistent answer is that the finished product is the least informative thing you can grade. In one long-reasoning study, checking intermediate states and policy compliance during generation raised task success from 32% to 87%, because most failures were process violations rather than wrong answers (Where do reasoning agents actually fail during long traces?). Agent benchmarks are converging on the same move: evidence shifts from the final response to the whole interaction trajectory, and scoring shifts from correctness to process quality, recoverability, coordination and robustness (How should we evaluate agent behavior beyond final answers?). Recoverability is the item most people leave out. It asks whether the work noticed a mistake and fixed it, not only whether it ended up right.

Finished work can also mislead the person who made it. High-quality AI output triggers a fluency heuristic, so users read the smoothness of the result as evidence of their own competence, even though they didn't produce it (Does processing ease mislead users about their own competence?). A polished deliverable therefore makes weak evidence for both the grader and the author. For human work, process data can help, but only up to a point. Analysis of writing and programming shows that wholesale AI delegation leaves a clear signature, with contributions arriving in concentrated bursts outside the author's usual rhythm. Ordinary AI collaboration looks the same as minimally assisted work (Can process data distinguish AI delegation from ordinary collaboration?). Timing evidence can flag someone who handed the task over. It can't certify that the person understood the work, and it shouldn't be used to punish normal collaboration.

The next question is what the record should contain and how far it can be trusted. BenchShield's approach is to issue a claim that an agent followed the intended evaluation path, backed by recorded infrastructure evidence instead of a single terminal score (Can infrastructure evidence replace terminal scores in benchmark validation?). Auditing work adds a checklist: which agents talked to each other, what information they exchanged, which tools they called, what approvals were obtained, which policies applied, and whether anyone modified the records afterward (What must auditors reconstruct to verify agentic workflows?). The last item matters most, because process evidence that can be quietly edited proves nothing. Treating the evidence as versioned files that can be inspected, corrected and rolled back, not as hidden state, is one way to make it auditable. It also lets you audit what someone knows separately from how they behave (Can person-grounded skills remain auditable without hidden prompt state?).

Process evidence also has to be fine-grained and judged against the right standard. Step-level confidence catches reasoning breakdowns that a global average hides, and it can stop a bad trace early (Does step-level confidence outperform global averaging for trace filtering?). Step-by-step checking still isn't sufficient. A workflow can pass every local check (plausibility, alignment, protocol compliance) and still fail as a whole, because those checks test different properties than end-to-end safety requires (Can individual components pass safety checks if the system still fails?). An assessment therefore needs step evidence and a system-level outcome check together. The step checks also need domain knowledge. In finance, a process reward model grounded in expert knowledge caught factual and regulatory errors that a general one, which mainly flags logical incoherence, missed (Can general process reward models catch factual errors in finance?).

The last problem is who reads all this evidence. An agentic judge that collects its own evidence showed 0.27% judge shift against 31% for a plain LLM judge, but its memory module cascaded errors, so it needs error isolation (Can agents evaluate AI outputs more reliably than language models?). Splitting the judging into stages also helps: extracting claims, retrieving related work, then comparing reached 86.5% reasoning alignment with human reviewers on novelty assessment (Can structured pipelines make LLM novelty assessment reliable?). The corpus has little on classroom-style assessment of human students specifically. What it supports is a general recipe: require a tamper-evident trajectory, check it step by step against domain knowledge, and verify the end-to-end outcome separately.