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Reviewed

Assessment complete; awaiting evidence revision.

Agent Architecture Review, Validation snapshot

Evaluated 30 April 2026 against the AI Design Blueprint doctrine · focus: code-review agent governance for software engineering workflows

Needs Changes

Status: Needs Changes

0/4

aligned of applicable principles · 0%

This is an autonomous agentic PR triage workflow, so the listed agentic design principles apply strictly. The code has strong foundations: explicit delegation policy, per-PR approval variables, abort handling, deterministic diff hashing, structured statuses, event logs, and rich audit records. The remaining gaps are mostly lifecycle and durability issues: approval does not truly pause when no decision is available, checkpoints are not actually saved after each meaningful transition, aborted runs can still be logged as completed, and the persisted audit misses the final completion event.

Per-principle findings

4 principles evaluated. Verdict, severity, evidence and recommendation for each.

P0

Design for delegation rather than direct manipulation

needs changes

Classified as category (A), an autonomous agentic workflow: `run()` builds a plan, iterates over `PULL_REQUESTS`, calls the OpenAI API in `review_pr()`, makes decisions in `_process_pr()`, invokes action handlers such as `post_comment()`, `apply_suggested_fix()`, and `merge_pr()`, and includes approval/abort gates via `approval_gate()` and `WorkflowAborted`. Delegation scope is mostly explicit through `DelegationPolicy`, `DEFAULT_POLICY`, `allowed_repos`, `allowed_actions`, `high_risk_paths`, and `available_actions()`. Per-PR control is attempted with `PR_AGENT_APPROVAL_<PR_ID>` taking precedence over `PR_AGENT_APPROVAL`, and termination is explicit through the `abort` decision, `PRStatus.AB…

Recommendation

Change the no-decision path in `approval_gate()` from immediate rejection to a real paused/pending state, such as persisting `PRStatus.AWAITING_APPROVAL` or a run-level paused state, saving a checkpoint, and exiting or sleeping/polling until an operator supplies a decision. If environment variables remain the control surface, document that they are pre-run configuration rather than live steering, or replace them with a durable approval source such as a file, queue, database row, or API endpoint.

P0

Ensure that background work remains perceptible

needs changes

The workflow provides some perceptibility through `state.log()`, `event_log`, per-record `events`, `PRStatus`, and durable checkpoint files written by `save_checkpoint()` using `.tmp` plus `os.replace()`. However, despite the comment `# P2: INCREMENTAL CHECKPOINT — atomic, after each meaningful transition`, checkpoints are only saved in `run()` after `_process_pr()` returns, and once in the `WorkflowAborted` handler. Inside `_process_pr()`, many meaningful state transitions occur without durable persistence: `rec.status = PRStatus.REVIEWING`, assignment of `rec.review`, transition to `BLOCKED_INVALID_OUTPUT` / `BLOCKED_HIGH_RISK` / `AWAITING_APPROVAL`, approval status changes, action handler…

Recommendation

Call `save_checkpoint(state, run_id)` after each user-meaningful transition, or pass a checkpoint callback/run context into `_process_pr()` and the action handlers. At minimum checkpoint after setting `REVIEWING`, after receiving/parsing the model response, after entering a blocked/awaiting-approval state, after approval decisions, after each external action, and after final PR status. Consider a heartbeat or `last_updated` field for long-running calls so background work remains visibly active rather than merely eventually persisted.

P0

Expose meaningful operational state, not internal complexity

needs changes

The code uses mostly user-relevant operational states through `PRStatus`, including `QUEUED`, `REVIEWING`, `AWAITING_APPROVAL`, `BLOCKED_HIGH_RISK`, `BLOCKED_INVALID_OUTPUT`, `REJECTED`, `SKIPPED`, `ABORTED`, `COMPLETED`, and `FAILED`. Logs such as `PLAN_PUBLISHED`, `APPROVAL_REQUESTED`, `PR_SKIPPED`, and `PR_FAILED` are also generally meaningful. However, run-level state is ambiguous: if `aborted` is set, the workflow logs `WORKFLOW_ABORTED`, then still proceeds through the normal `else` path and logs `WORKFLOW_COMPLETED` after `save_audit()`. That means an operator-aborted run can appear both aborted and completed. There is also no explicit run-level status field in `_serialize_state()`; c…

Recommendation

Add an explicit run-level status to `TriageState` or `_serialize_state()`, such as `running`, `awaiting_approval`, `aborted`, `failed`, or `completed`. Avoid logging `WORKFLOW_COMPLETED` for an aborted run; use a distinct terminal event such as `WORKFLOW_TERMINATED_ABORTED` or set final status to `aborted`. Keep technical details such as exact model/prompt in the audit view, while exposing concise user-facing status separately.

P0

Establish trust through inspectability

needs changes

Inspectability is strong in several places: `diff_hash()` uses deterministic SHA-256; `AuditRecord` stores `pr_id`, `repo`, `diff_hash`, `pr_metadata`, `model`, `prompt`, `review`, `approval_decision`, `approver`, `actions_taken`, `rollback_token`, and per-PR `events`; `review_pr()` records `record.model` and `record.prompt` before the OpenAI call; `ParsedReview` preserves `raw_response`; and `_serialize_state()` includes the full prompt, raw response, PR metadata, hash algorithm, actions, and event log. One audit completeness issue remains: `save_audit()` writes the final audit file before `run()` logs `WORKFLOW_COMPLETED`, so the terminal completion event is not included in the persisted a…

Recommendation

Move terminal run-state logging before `save_audit()`, or update and persist a final run status inside `_serialize_state()`. For stronger auditability, also consider storing OpenAI response metadata such as response id, finish reason, usage, and system fingerprint where available, but the core provenance fields requested are present.

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