Calibration Meeting Prep Agent
A calibration meeting prep agent is an AI agent for performance & talent management that ahead of calibration, aggregates proposed ratings by team, level, tenure, and lawfully held demographics, flags distribution skews, managers whose ratings run consistently high or low, and narrative-to-rating mismatches, and assembles the calibration pack in the order the session will run.
How does the calibration meeting prep agent work?
What flows in, what the agent does with it, where a person decides, and what comes out.
Reads from
Proposed ratings and narratives · Org, level and tenure · Lawfully held demographics, aggregate · Prior-cycle ratings · Calibration guidelines and rating scale
AI agent · runs when manager ratings are submitted, ahead of calibration
Calibration Meeting Prep Agent
A person decides
Calibration group sets final ratings; HR facilitates
Produces
Calibration pack in session order · Distribution and outlier flags · Session agenda
What does the calibration meeting prep agent do?
Ahead of calibration, aggregates proposed ratings by team, level, tenure, and lawfully held demographics, flags distribution skews, managers whose ratings run consistently high or low, and narrative-to-rating mismatches, and assembles the calibration pack in the order the session will run.
What does it produce?
A calibration pack: distributions, outlier flags with the underlying evidence summaries, and a session agenda
Who decides?
The calibration group of leaders, facilitated by HR, debates and sets final ratings; the agent prepares the pack and never suggests what a rating should become.
What systems does the calibration meeting prep agent connect to?
Examples of the kind of systems this agent would read from or write to, so you can picture it in your own stack. The actual set is whatever you run.
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Performance
proposed ratings and narratives
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HRIS
org, level, tenure and lawful demographics
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Analytics and presentation
the pack itself
What data does it need?
- proposed ratings and review narratives
- org structure and level
- tenure and time in role
- demographics held lawfully for aggregate checks
- prior cycle ratings
How would you measure it?
pack preparation hours, per cycle; rating distribution consistency across managers, per cycle; adjusted rating gaps by cohort, per cycle, above minimum group size; calibration session time, per session
What does a first proof look like?
Rebuild last cycle's calibration pack from the data as it stood before the sessions. HR compares it with the pack they built by hand and the outliers they discussed.
You'd call it working when
Distributions match, flags land on the cases leaders actually debated, and every cohort shown clears the minimum group size.
What usually goes wrong?
- Flags read as verdicts on managers; frame them as distribution checks
- Small teams plus demographics identify people; enforce the minimum group size hard
- Packs that imply a target distribution become forced ranking by stealth
What are the guardrails?
- Never suggests what a rating should become; prepares and flags only
- Demographic cuts aggregate only, minimum group size enforced
- Pack access limited to the calibration group and HR facilitator; retention limited to the cycle
- High-risk under the EU AI Act; works council consultation where required before use
- Every flag and its evidence summary logged for the session record
What leaves your boundary is set per build; the inputs above are the ceiling, and where the model runs, what it retains, and the DPA are agreed with your security team before anything is connected.
Our read
Clearly valuable with real deployments behind it. Needs care on data and adoption.
Parts of this may exist in your current tools. The case for building is usually the join across systems, or your rules and language, that a suite feature cannot carry.
individual ratings plus protected characteristics; consultation and privacy obligations apply
Where it sits in the order
Ratings plus protected characteristics; last in the review chain, once ratings and narratives are captured consistently.
Is a Calibration Meeting Prep Agent worth building for your function?
That depends on your numbers, your data, and what else is on the map for you. The strategy month works that out.
Thirty minutes. Bring the number this would move.