Table of Contents

Training Design

Core Philosophy

The purpose of this training is not to teach App Builder, DevOps Agents, DOM, ticketing, or AI agents as individual features. The purpose is to create a mindset shift.

Participants should stop thinking... ...and start thinking
"What application should I build?" "What operational outcome do I want to achieve?"

Understand → Act → Create

flowchart LR
    U[Understand] --> A[Act] --> C[Create]
    C -.->|Measure value & re-prioritize| U
Step Meaning Typical question
Understand The operational problem, constraints, and desired future state What hurts today, and what does "good" look like?
Act Prioritize the highest-value opportunities Which opportunity returns the most value right now?
Create Only what is needed to prove value and achieve measurable outcomes What is the smallest thing that proves it works?

Key Messages

1. Start with the Outcome

Every session begins with these questions – never with technology:

  • What problem are we solving?
  • What does success look like?
  • What would the future state look like?
  • What business value would be created?
  • How do we reduce operational effort, cost, or risk?
  • How do we accelerate time-to-value?

2. Work Backwards from the Future State

  1. Define an ambitious future state
  2. Work backwards
  3. Identify the smallest valuable step
  4. Deliver something useful quickly
  5. Measure value
  6. Re-prioritize continuously

The goal is not to reach the final destination immediately. The goal is to continuously create value.

3. Multiple Value Streams

Do not focus on a single roadmap. Participants may start one value stream and discover another with a much higher return – that is expected.

Value Stream Next Increment Value
A – Incident Resolution Current next increment Save 5 minutes per ticket
B – Operational Briefing Potential first increment Save 45 minutes per operator per day

→ Value Stream B should be prioritized.

We don't continue because we started. We continue because it is still the highest-value investment.

4. Operational Data Is the Competitive Advantage

The value is not AI. The value is:

Data Knowledge
Operational telemetry Company standards
Historical data Policies
Operational processes Procedures
Institutional knowledge

AI amplifies operational knowledge. Without a foundation of trusted operational data and knowledge, AI has limited value.


Session Structure

# Session Format Participants create Key takeaway
1 Keynote: Vision + Vanguard Theoretical – Operations are heading towards AI-native, value-stream-driven work
2 DataMiner DevOps Agents Demonstration – Intent becomes implementation – the value is accelerated solution creation
3 App Builder Workshop Hands-on An application Solve an operational problem, not an infrastructure problem
4 Specialized Agents Workshop Hands-on An operational agent The most valuable agents understand how your organization operates
5 Agent-to-Agent Collaboration Demonstration – Realistic collaboration with controlled actions and human approval

1. Keynote: Vision + Vanguard

Objective: Show where operations are heading.

Topics: AI-native operations · Digital twins · Agentic systems · Autonomous operations · Time-to-value · Future-state thinking · Multiple value streams

2. DataMiner DevOps Agents

Objective: Show how intent becomes implementation.

Intent → Example outcome
Data Model Objects and fields describing the operational domain
Business Logic Rules, calculations, and workflows
Integration Connections to existing systems and data
Guardrails Validation, permissions, and safe boundaries

Emphasis: The value is not generation – it is accelerated solution creation.

3. App Builder Workshop

First hands-on session. The backend is pre-built – DataMiner services and elements with live telemetry, alarms, trend data, and static service metadata – so participants focus entirely on solving a problem. No AI is used in this session.

→ Details: App Builder Workshop · Hands-On Backend

4. Specialized Agents Workshop

Second hands-on session. Participants create a specialized operational agent.

The purpose is not AI experimentation. It is bringing organizational knowledge into DataMiner:

  • Company policies
  • Operational procedures
  • Escalation rules
  • Standards
  • Best practices

The most valuable agents understand how your organization operates.

The agent builds on the same vertical and services used in the App Builder Workshop, so participants reuse the context they already know.

All AI output lives in this session – for example service summaries, recommended actions, and an Operations Copilot answering questions such as:

  • What requires immediate attention?
  • Which service is at highest risk?
  • Summarize the operational situation.
  • What should I investigate first?

This output is generated at runtime and is not stored in the backend.

5. Agent-to-Agent Collaboration

Demonstration of realistic multi-agent operations:

Concept What to show
Context passing One agent hands over enriched context to another
Separation of responsibilities Each agent has one clear role
Controlled actions Agents act only within defined boundaries
Human approval points Operators approve impactful actions

Avoid unrealistic fully autonomous scenarios.


Final Training Message

Participants should not leave saying "I learned App Builder" or "I learned how to create an agent."

They should leave saying:

I learned how to identify operational value, prioritize the most impactful opportunity, and rapidly create a working solution that improves operations using DataMiner's operational data, workflows, and organizational knowledge.

That is the mindset shift the entire Empower program should create.