Performance Reviews
Run structured review cycles with self, peer, and manager evaluations. AI agents orchestrate the logistics — scheduling, peer nominations, deadline tracking, and context assembly. By the time a manager opens a review, everything they need is already there.
Create review cycles with deadlines
Define named review cycles with announcement dates, self-review deadlines, peer review deadlines, manager review deadlines, and sign-off dates. The agent tracks where every employee stands and sends reminders as deadlines approach.
Build custom review forms
Design review questions with long-text, short-text, and 1-5 rating question types. Mark questions as required or optional. Different forms for self, peer, and manager reviews.
Agent-orchestrated peer nominations
Employees nominate their peer reviewers, and the Review Agent routes nominations to managers for approval. No manual tracking of who's nominated whom — the agent handles the coordination.
Self-review submission
Employees reflect on their achievements, strengths, and growth areas using the custom form. Self-reviews provide context that peers and managers reference in their own evaluations.
Agent-collected peer reviews
The Review Agent sends forms to approved peers, tracks submissions, and chases stragglers. By the time a manager opens the review, all peer input is already assembled.
Manager review with full context
Managers write their reviews with self-review and peer feedback visible for reference. The agent assembles everything — OKR data, feedback history, peer input — so managers just talk about the person.
Formal sign-off
Managers formally sign off on each review to close the cycle. Sign-off is a deliberate action, not an automatic transition.
Agent-tracked completion status
The agent monitors self, peer, and manager completion across the cycle. It knows exactly who's done, who's pending, and where the bottleneck is — and nudges accordingly.
Cycle status automation
Review cycles transition through draft, announced, in progress, and completed states. The agent manages the state machine, sends announcements, and escalates when phases run behind schedule.
Automatic feedback integration
The agent links feedback requests and responses submitted during an active review cycle. Reviewers and managers see the full picture without switching contexts or aggregating manually.