W
EU AI Act: Not High Risk Q2

Learning Event Management Agent

Physical training logistics - rooms, trainers, equipment - handled automatically.

Coordinates training event logistics: room booking, trainer scheduling, participant management, and materials procurement - on time.

Analyse your process
Airbus Volkswagen Shell Renault Evonik Vattenfall Philips KPMG

Assessment

Agent Readiness 76-83%
Governance Complexity 11-18%
Economic Impact 48-55%
Lighthouse Effect 24-31%
Implementation Complexity 28-35%
Transaction Volume Weekly

What This Agent Does

Instructor-led training remains essential for many development needs: hands-on technical skills, leadership workshops, safety certifications, and complex compliance topics. But the logistics of physical training events are disproportionately time-consuming: booking rooms with the right equipment, scheduling trainers, managing participant registration and waitlists, arranging catering, handling cancellations and rescheduling, and coordinating post-event follow-up. The Learning Event Management Agent automates these logistics. It handles the entire event lifecycle from scheduling to follow-up: identifies available rooms and trainers, manages participant registration with waitlist handling, sends reminders and pre-event preparation materials, coordinates day-of logistics (equipment, catering, room setup), collects post-event feedback, and manages the administrative closure (attendance recording, certification issuance, cost allocation). For organisations running dozens of training events per month across multiple locations, the coordination effort saved is substantial.

Micro-Decision Table

Human
Rules Engine
AI Agent
Each row is a decision. Expand to see the decision record and whether it can be challenged.
Receive event request Parse training event requirements (type, capacity, equipment, location) Rules Engine

Structured intake from training plan or ad-hoc request

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Find available resources Identify available rooms, trainers, and equipment AI Agent

Automated availability search across multiple resource calendars

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Schedule event Book room, trainer, and equipment for confirmed date AI Agent

Automated resource booking with conflict detection

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Manage registration Process participant enrollments and maintain waitlist Rules Engine

First-come-first-served with priority rules and capacity limits

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Send event communications Distribute invitations, reminders, and preparation materials AI Agent

Automated communication schedule per event type

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Coordinate day-of logistics Confirm room setup, equipment, catering, and trainer arrival AI Agent

Automated checklist confirmation with exception alerting

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Record attendance Capture participant attendance for records and certification Rules Engine

Attendance tracking linked to certification and completion records

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Collect feedback Distribute post-event evaluation survey AI Agent

Automated survey distribution to attendees

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Process administrative closure Allocate costs, issue certifications, update training records AI Agent

Automated closure tasks from event completion data

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Decision Record and Right to Challenge

Every decision this agent makes or prepares is documented in a complete decision record. Affected employees can review, understand, and challenge every individual decision.

Which rule in which version was applied?
What data was the decision based on?
Who (human, rules engine, or AI) decided - and why?
How can the affected person file an objection?
How the Decision Layer enforces this architecturally →

Prerequisites

  • Room and resource booking system
  • Trainer availability and scheduling system
  • Registration and waitlist management capability
  • Communication platform for event notifications
  • Equipment and catering management process
  • Integration with LMS for attendance and completion recording
  • Cost allocation rules for training events

Governance Notes

EU AI Act: Not High Risk
Not classified as high-risk under the EU AI Act - the agent handles logistics without employment-affecting decisions. GDPR applies to participant registration data and attendance records. Data retention for training attendance should follow the organisation's learning records retention policy. Works council information rights are minimal for logistics management but may apply to attendance tracking systems.

Infrastructure Contribution

The Learning Event Management Agent builds the physical resource booking and event coordination infrastructure. The multi-resource scheduling, waitlist management, and post-event administration patterns established here are reusable for any agent managing physical or hybrid workplace events. Builds Decision Logging and Audit Trail used by the Decision Layer for traceability and challengeability of every decision.

Does this agent fit your process?

We analyse your specific HR process and show how this agent fits into your system landscape. 30 minutes, no preparation needed.

Analyse your process

What this assessment contains: 9 slides for your leadership team

Personalised with your numbers. Generated in 2 minutes directly in your browser. No upload, no login.

  1. 1

    Title slide - Process name, decision points, automation potential

  2. 2

    Executive summary - FTE freed, cost per transaction before/after, break-even date, cost of waiting

  3. 3

    Current state - Transaction volume, error costs, growth scenario with FTE comparison

  4. 4

    Solution architecture - Human - rules engine - AI agent with specific decision points

  5. 5

    Governance - EU AI Act, works council, audit trail - with traffic light status

  6. 6

    Risk analysis - 5 risks with likelihood, impact and mitigation

  7. 7

    Roadmap - 3-phase plan with concrete calendar dates and Go/No-Go

  8. 8

    Business case - 3-scenario comparison (do nothing/hire/automate) plus 3×3 sensitivity matrix

  9. 9

    Discussion proposal - Concrete next steps with timeline and responsibilities

Includes: 3-scenario comparison

Do nothing vs. new hire vs. automation - with your salary level, your error rate and your growth plan. The one slide your CFO wants to see first.

Show calculation methodology

Hourly rate: Annual salary (your input) × 1.3 employer burden ÷ 1,720 annual work hours

Savings: Transactions × 12 × automation rate × minutes/transaction × hourly rate × economic factor

Quality ROI: Error reduction × transactions × 12 × EUR 260/error (APQC Open Standards Benchmarking)

FTE: Saved hours ÷ 1,720 annual work hours

Break-Even: Benchmark investment ÷ monthly combined savings (efficiency + quality)

New hire: Annual salary × 1.3 + EUR 12,000 recruiting per FTE

All data stays in your browser. Nothing is transmitted to any server.

Learning Event Management Agent

Initial assessment for your leadership team

A thorough initial assessment in 2 minutes - with your numbers, your risk profile and industry benchmarks. No vendor logo, no sales pitch.

30K120K
1%15%

All data stays in your browser. Nothing is transmitted.

Related Pages

Decision Layer
Governance layer between AI agent and target system. Every decision documented and challengeable.
Co-Determination & AI
Works council, works agreements, and co-determination as architecture constraint.
HR AI Agents
All HR agent services at a glance.
HR Agent Readiness
7 questions, 3 minutes. How ready is your organisation?
Assessment eBook
15 questions for your leadership team as PDF.

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Back to Catalog

Frequently Asked Questions

Can the agent manage virtual and hybrid training events?

Yes. The agent handles virtual events (video conference link generation, virtual room setup) and hybrid events (physical room + streaming setup) with the same workflow, adapted for the event format.

How does the agent handle last-minute cancellations?

Cancellations trigger automatic waitlist promotion, participant notification, and (if below minimum participation threshold) potential event cancellation with trainer and room release. The agent applies configurable cancellation rules per event type.

What Happens Next?

1

30 minutes

Initial call

We analyse your process and identify the optimal starting point.

2

1 week

Discover

Mapping your decision logic. Rule sets documented, Decision Layer designed.

3

3-4 weeks

Build

Production agent in your infrastructure. Governance, audit trail, cert-ready from day 1.

4

12-18 months

Self-sufficient

Full access to source code, prompts and rule versions. No vendor lock-in.

Implement This Agent?

We assess your process landscape and show how this agent fits into your infrastructure.