Agentic Engineering Masterclass · 8-9 October 2026
Book a seat · €650 / person / day
90 days from audit to governance to company-wide capability.
AI Adoption Program
90-day cadence
- 01
Understand
Audit and baseline
Days 1-30
- 02
Enable
Governance and training
Days 31-60
- 03
Scale
Operating model
Days 61-90
Audit first. Governance second. Training once we know what the company actually needs.
THE PROGRAM
Stop thinking model-first. Start thinking system-first.
Most companies already have AI inside the building. Different teams, different tools, different levels of maturity. Some people are getting real work done. Others are blocked by quality, security, cost, or a lack of a shared way of working.
If powerful models consistently produce unreliable outcomes, the problem is often the harness around the model. The context you provide, the rules, the tools, the permissions, the validation, and the feedback loops determine the quality of the result.
The opportunity is bigger than choosing the right AI tools. It is about building the organizational and technical capabilities required to use AI reliably. We run that as a 90-day engagement: Understand, Enable, Scale.
If powerful models consistently produce unreliable outcomes, the problem is often the harness around the model.
90-DAY PLAN
Understand, enable, then scale
Three phases. Audit first. Governance second. Training and reference workflows once we know what the company actually needs.
Days 1-30
Understand and baseline
Days 31-60
Enable and build
Days 61-90
Operationalize and scale
Before introducing new tools, policies, or training, understand how AI is actually used across the company today.
Map the current AI landscape
Build a clear picture of what is already happening, across engineering and every other function.
- ▸Which AI tools are currently being used
- ▸Who is using them, and which teams
- ▸What they are using them for
- ▸Which tools are officially licensed versus individually expensed
- ▸What data each tool can access
- ▸Which external services company data may flow through
- ▸Current AI-related costs
- ▸Existing policies and restrictions
- ▸Where teams are seeing value
- ▸Where teams are experiencing problems or resistance
Understand real workflows
Look past the tool list. Find the workflows where AI is already becoming part of how the company operates.
Where is AI actually creating value today, and where could it create significantly more value?
- ▸Software development, code review, and testing
- ▸Research, documentation, and data analysis
- ▸Marketing and content creation
- ▸Customer support and product management
- ▸Internal operations and go-to-market workflows
Establish a baseline
Where possible, measure the starting point so later work can be compared against something real.
- ▸Adoption and AI usage by team
- ▸Cost and time saved
- ▸Development velocity, quality, and reliability
- ▸High-value workflows
- ▸Data and security exposure
- ▸Employee confidence and capability
Deliverables after 30 days
The company should have enough real data to decide what happens next, rather than designing an AI strategy from assumptions.
- An AI usage landscape
- Tool and license inventory
- Data-access and data-flow overview
- Key workflow map
- Initial risk and governance assessment
- Adoption and capability assessment
- Baseline metrics
- Prioritized opportunities
- Training needs by team and persona
THE OUTCOME
Not more AI. Reliable, measurable AI.
At the end of 90 days, the objective is not for the company to simply use more AI. The objective is to know how to adopt new models and tools without rebuilding the strategy every time the technology changes.
MAKE IT STICK
AI Ready onboarding, so the capability survives hiring
New employees should not have to independently discover how the company uses AI. Together we create a short, role-specific AI Ready program delivered through Hackages. Different tracks exist for engineering and for everyone else.
Engineering
Agentic engineering, coding agents, harnesses, testing, evaluation, reliability, and development workflows.
- ▸The company AI philosophy
- ▸Approved tools and harness principles
- ▸Data and security expectations
- ▸Company rules and best practices
- ▸Practical exercises on real workflows
Everyone else
Using AI and agents to automate and augment real business workflows safely and effectively.
- ▸The company AI philosophy
- ▸Approved tools for the role
- ▸Data and security expectations
- ▸Internal AI workflows for the function
- ▸Practical exercises on work they already do
GUIDING PRINCIPLE
The durable advantage is the system around the model
Models will continue to change. The durable advantage is the system around them. Build that system well, and the company can take advantage of increasingly capable models while keeping quality, visibility, security, and control.
01
Context
What the model can see: the repo, the docs, the data, the constraints.
02
Rules
Instructions the team actually follows: permissions, style, risk, review.
03
Orchestration
Who plans, who executes, who reviews. Humans stay on direction.
04
Skills and tools
Reusable skills, connectors, and tools that make work repeatable.
05
Execution
The loop that turns a plan into a change, without improvising every time.
06
Validation
Tests, evaluation, and human oversight where the risk requires it.
07
Feedback
What we learn from failures goes back into rules, skills, and context.
08
Observability
Cost, usage, quality, and risk stay visible. Not surveillance. Measurement.
TRAINING INSIDE THE 90 DAYS
Workshops when they are the right tool, not the first move
Alongside the engagement, Hackages provides targeted training and workshops based on what the discovery phase actually finds. That can include the Agentic Engineering Masterclass and role-specific enablement for non-engineering teams.
- Agentic Engineering Masterclass for engineering teams
- Harness, context, and evaluation practices on your environment
- Role-specific programs for product, marketing, operations, and HR
- Reference workflows the rest of the company can copy
WHO THIS IS FOR
Companies that already have AI in the building
- Companies where AI adoption is already happening, but unevenly
- Leaders who need visibility into tools, cost, data access, and risk
- Engineering teams worried about the quality of AI-generated code
- Organizations that want training for more than developers: product, marketing, HR, operations
- Teams that need an onboarding path so new hires do not start from zero
MEET YOUR INSTRUCTORS
Davy Engone
Founder & Software Mentor @ Hackages
Davy has trained 5,000+ engineers and installs AI capability inside companies the same way he installs it on a repo: start from how work actually happens, then build the system around the model. Audit, harness, training, then an operating model that survives the next model release.
The 90 days should answer one question
How can the company turn rapidly improving AI capabilities into a reliable, measurable, and sustainable competitive advantage?
FAQ'S
if your question wasn't answered below
Chances are at this stage you have a couple questions. Here are a few questions that are often asked.
A workshop trains a group for one or two days. The AI Adoption Program is a 90-day engagement with the company. We audit how AI is actually used, set up governance that still lets people move, then train engineering, product, marketing, HR, and the other functions that need it. Workshops such as the Agentic Engineering Masterclass sit inside phase two when they are the right tool.
Ready to run this inside your company?
We scope the 90 days around your landscape: the audit, the governance, the training, and the onboarding track. No generic AI strategy deck.