Engineers approve or reject the automatically generated flag-removal PRs, serving as the human quality gate in the agentic workflow.
The system supports post-run evaluation of agent outputs across six artifact projection representations, enabling researchers to assess quality and relevance without having monitored the run live.
The study distinguishes systems that support local incorporation of agent outputs (human reviews and slots in results) from those that allow broader agent-driven reorganization.
Participants who returned to the chat more frequently absorbed more falsehoods rather than fewer, showing that human review behavior did not function as a corrective mechanism against confident AI errors.
Sidecar surfaces under-specification in user prompts during the wait, giving users a structured moment to reflect and refine before the next delegation.
Participants performed only generic checks rather than generating diagnostic evidence, revealing shallow review behavior in agentic programming tasks.
Multi-agent debate setups, intended as a review safeguard, actually increased team error rate from 50.0% to 59.5% when exposed to ORA attacks.
For pipeline defects the agent cannot fix locally, it opens upstream GitHub issues proposing backward-compatible fixes for human review.
A frontier model proposes initial labels and answers the same questions as human annotators, creating a joint human-AI review process for trace evaluation.
The 'hollow orchestrator' failure mode is explicitly about review surfaces failing — developers approving work they can no longer evaluate.
The multi-agent setup is explicitly framed as a peer-review/error-checking architecture, and the paper stress-tests whether peer critique actually catches false answers.
Multi-agent peer-checking is tested as a review mechanism, and found to fail catastrophically under minimal adversarial pressure.
Participants provide explicit collaboration satisfaction and output quality judgments, creating a review signal that frontier models currently fail to predict accurately.
Expert participants actively decomposed tasks and scrutinized agent outputs, sacrificing completion rate to maintain evaluative oversight.
Medical training cases show that AI-assisted review of diagnostic decisions can transmit systematic decision biases into later unaided human judgment.
The shift from pull requests to 'antique change requests' visible in the chart suggests Anthropic built new review primitives suited to agent-generated code at scale.
The harness enforces checkpoints during code generation that catch failures a smaller model would otherwise propagate through to final output.
SkillRelayGuard provides pre-execution advisory review of artifact integrity while leaving the final execution decision to the agent itself.
Researchers inspected agent-retrieved evidence directly within the shared document artifacts, using co-located traces to evaluate agent output quality.
AI agents performing automated pull-request code review are shown to approve revised code that still contains hidden vulnerabilities after the attacker uses the reviewer's own feedback to appear compliant.
The paper identifies a pattern of post-hoc review as a trust mechanism, where practitioners monitor execution and assess work afterwards rather than approving every step in advance.
The study directly challenges the validity of multi-agent debate as a review mechanism, showing stated consensus can mask persistent internal disagreement rather than genuine convergence.
The experimental setup models a review-like interaction where one agent sees a peer's dissenting answer and explanation and may revise its own judgment accordingly.
The proposed audit framework positions user judgment as a structured check against agent-stated capabilities, enabling systematic review of behavioral claims.
Project-wide structural review and agent dialogue help authors inspect the evolving narrative work at both local and cross-layer levels.
The judgment tasks are structured as single-correct-answer problems where agents must evaluate and potentially revise their answers in light of peer responses, surfacing how identity biases corrupt peer review dynamics.
The Engine returned citation-rich answers explicitly designed so reporters could verify and trust the results before publishing.
The study motivates interfaces that help users evaluate the quality of their joint work with AI rather than simply accept AI-produced answers.
Agents surface concerns and challenge perspectives on the document, functioning as a continuous peer-review layer during composition.
Developers evaluate and approve agent work through the graph interface rather than relying solely on natural-language chat, reducing cognitive load during oversight decisions.
Supervisory control loops include review stages where developers assess agent-produced work before proceeding.
Human coders were used to validate automated coding of agent behavior against declared behavioral rules, establishing a post-hoc review methodology for agentic compliance.
Human engineers reviewed agent-generated Rust migrations at scale across 800,000 lines of production code.
The hierarchical condition gives the Manager agent power to reject worker output and require revision, operationalizing a formal review loop between agents.
Trace2Flow translates completed agent execution traces into interactive graph-based workflows that users can inspect to detect prompt or agent errors post-execution.
The study measures how well humans evaluate and selectively accept or reject model answers, finding deference increases with perceived model competence but confidence calibration degrades post-advice.
The agentic software factory model implies human engineers shift toward reviewing and approving agent-generated code rather than writing it directly.
A designated accountability layer agent reads upstream agent reports to assign fault, but is shown to perform worse than chance when agents' own conclusions are included in those reports.
Every substantive skill edit is authored or merged through a named human account, establishing human review as the de facto governance gate for agent capability updates.
The paper demonstrates that report-only review of multi-agent outputs cannot distinguish independent corroboration from replication, undermining standard human review workflows that treat agent consensus as evidence of correctness.
Human oversight is deferred to post-hoc inspection of agent-generated findings rather than inline approval of each step.
Frames design critique and critical thinking as the human review layer that agents cannot replace, positioning humans as quality-gatekeepers over generated output.