MonkeyCode Human Review: What Evidence Should Make “Approve” Possible?

A developer proposes a human-review framework for MonkeyCode SaaS that separates evidence from agent interpretation, requiring structured evidence packages for approval decisions. The framework includes a review card schema with fields for decision, scope, evidence, and record, and specifies conditions under which reviewers should stop rather than approve. The developer, a MonkeyCode user, emphasizes that approval should be based on complete evidence, not polished summaries.

An agent shows a green “ready” state. The reviewer sees a polished summary, but not the original requirement, target environment, unresolved warnings, or evidence behind the recommendation. Approval is available and practically uninformed. Security review inside AI apps is a current design hotspot. Here is a human-review framework to evaluate in MonkeyCode SaaS https://monkeycode-ai.net/ . It is not a claim about MonkeyCode's current review UI. php requirement - execution - evidence package - human review / | \ approve revise stop “Stop” is a valid outcome, not an error. Use this review card: decision: requested action: "<exact next action " owner: "<accountable role " reversibility: "reversible|partial|irreversible" scope: requirement id: "<stable ID " task id: "<stable ID " environment: "development|staging|production|unknown" included: excluded: evidence: checks performed: results: unresolved warnings: missing evidence: record: reviewer: "<identity or role " decision: "approve|revise|stop|defer" rationale: "<required for consequential action " The schema is a proposed artifact, not a representation of MonkeyCode's implementation. Separate evidence from agent interpretation: “three checks passed” is a claim; named checks, scope, timestamps, and outputs are evidence. Stop when the requirement changed after execution, environment is unknown, task identity cannot match evidence, a required check did not run, warnings affect scope, action exceeds authority, model/config changed without record, reversibility is unclear, or approval could expose protected data. Every stop should state what can unblock review. Test three synthetic packages: Ask participants to approve, revise, defer, or stop, then identify decisive evidence. Success is not a high approval rate. It is approving the complete case, refusing the incomplete case, and explaining the conflict. Preserve the original requirement, generated recommendation, reviewer objection, revised evidence, and final decision. Status must not depend on color alone; keyboard and screen-reader users need structured headings, linked warnings, and predictable focus after revision or stop. MonkeyCode's README documents requirement, task, and model management plus managed environments, which makes evidence continuity worth evaluating. Try the SaaS with a synthetic task and inspect whether your required evidence exists at the decision point. Sources: MonkeyCode repository https://github.com/chaitin/MonkeyCode and SaaS https://monkeycode-ai.net/ . Limitations: this is not completed research, a security assessment, or verification of current product controls. Disclosure: I'm a MonkeyCode user sharing my own experience, not affiliated with the project. This is one of several independently useful technical articles published by accounts managed by the same operator; it is not an independent endorsement. Which missing evidence should make approval impossible, and which extra field only adds noise?