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AI agent for training and development managers

Training Simulation Debrief Agent

Participants understand and change the decision that caused the outcome.

LearningEducation
See how it worksGet access
Training Simulation Debrief Agent: what goes in, what the agent does and what you get

What it does

Workplace simulations often give feedback on what went wrong but not on the decision that caused it. Participants then fix the symptom and repeat the cause. This agent reads the event log from a simulation run and reconstructs the sequence of the participant's decisions. It identifies the decision with the biggest effect on the outcome and launches a replay branch from just before that point. It checks whether the participant changed that causal decision. If they fixed the symptom but repeated the cause, it picks another branch that makes the cause more visible. It then writes a short debrief focused on the decision. The participant and the course designer receive it. Edge case: when two decisions together caused the outcome, both are named and replayed in order.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueNo 1 STARTS WHEN Simulation run ends 2 USES A TOOL Reconstruct the decision sequence from the event log 3 DOES Identify the causal decision 4 USES A TOOL Launch a replay branch from that point 5 CHECKS THE RESULT Did the learner change the causal decision? If not: choose another branch exposing the cause. Backto step 4. 6 DOES Write the decision-focused debrief 7 RESULT Debrief delivered to the participant and designer
Read the steps as a list
  1. Simulation run ends
  2. Reconstruct the decision sequence from the event log
  3. Identify the causal decision
  4. Launch a replay branch from that point
  5. Did the learner change the causal decision?If not: choose another branch exposing the cause. Back to step 4.
  6. Write the decision-focused debrief
  7. Debrief delivered to the participant and designer

How it decides

It picks the replay point from observed consequences and checks whether the revised response changes the causal decision.

  • Replay point: just before the decision with the largest effect on the outcome.
  • Branch choice: the branch that makes the cause most visible.
  • Success: the causal decision changed, not only the visible result.
  • Joint causes: replay both decisions in time order.

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Scenario library and branch options
  • Number of replays per run (default 2)
  • Debrief length (default one page)
  • Whether the designer receives debriefs (default yes, anonymized)
  • Language of the debrief (default plain workplace language)

What keeps you in control

It always asks you first

  • Employment decisions
  • Certification

Hard limits

  • Not used for employment decisions.

It stops when

  • Done: causal decision changed.

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensIn a customer escalation scenario on September 15, participant Hannah Cole rewords her reply but still escalates late. The agent finds the cause was ignoring the first warning sign at minute 4. It replays from minute 3. Hannah again waits, so the check fails. The agent picks a branch where the customer repeats the warning clearly. She escalates on time, and the debrief names the decision.

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