Write your awesome label here.
Put green engineering guidance inside the agentic coding tools your teams already use.
Ten modules on building green agent skills: where they belong across the engineering lifecycle, how to design and build one to the standard, designing the agent itself for a low footprint, distributing a single source across tools, governing them, and evaluating whether they change what the tools produce.
7h 40m
10
30 questions
80%
Certification
Building a Green AI Agent
How the course works
Built to the agent skills standard
Skills built to a standard and governed as such, rather than a prompt library.
Review as the high-leverage point
One integration there influences every change that passes through it, which makes it a strong first choice for many teams.
Worked examples and a capstone
Build and install, evaluate and measure, then design and evaluate your own.
What this course helps with
Guidance at the moment of decision
Green engineering guidance that arrives while the code is being written is followed more often than a standard that sits in a wiki.
One maintained source
Editor, review tool and pipeline carrying the same version, rather than drifting copies.
Evidence that a skill works
A way to check whether the code the tools produce is measurably less wasteful, rather than assuming the skill is effective.
Who this is for
- Architects and developers using agentic tools day to day
- Platform and developer-experience engineers who distribute tooling
- Engineering leads standardizing practice across teams
Related courses:
For model training decisions rather than the coding loop, see Green AI for ML Practitioners.
What you will work with
The lifecycle map
Where a skill earns its place across seven points, from design and architecture through testing, review and delivery to the operations handoff.
One source, many tools
Distributing the same guidance without forking it into divergent copies.
The evaluation
How to establish whether the skill changed the output.
What you will be able to do
Course Lessons
The full course
01
The Prompt Footprint
Introduces the five-dimensional view: cost, carbon, energy, water, embodied hardware. Establishes SCI for AI as the standards anchor. Positions the regulatory landscape (CSRD, EU AI Act, procurement disclosure) as the why now for Footprint literacy.
Before You Begin
Modules
02
The Specification Lever
How prompt intent quality drives Footprint. The GSF Intent Framework introduced. Trade-offs: over-specification wastes tokens, under-specification wastes retries. Worked examples across capability tiers.
Modules
03
The Structure Lever
Prompt organization: system messages, few-shot examples, output constraints, formatting instructions. Where structure earns its tokens and where it doesn't.
Modules
04
The Reasoning Depth Lever
When to invoke chain-of-thought, tree-of-thought, extended reasoning modes; when not to. The GSF Reasoning Budget: task-class taxonomy for reasoning depth decisions.
Modules
05
The Model Selection Lever
The most consequential single decision in inference efficiency. Capability tiers and routing decisions. The GSF Model Fit Test protocol. Same task across tiers, measured.
Halfway Check-in
Modules
06
The Orchestration Lever
Where agents leak resource: tool selection, chain depth, redundant retrieval, missing caching. Token budgets as a first-class design constraint. Case study from the supply-chain agent paper (70–90% workflow-level savings).
Modules
07
Measuring the Prompt Footprint
Hands-on demonstrations with EcoLogits, CodeCarbon, AI Energy Score. Producing numbers a CSRD auditor can read. The measurement chain from token count to gCO₂eq.
Modules
08
Procurement & Disclosure
What to ask your LLM vendor. What CSRD and the EU AI Act will require. The GSF Procurement question bank organized by Footprint dimension.
Modules
09
Course Completion
50 questions, 120 minutes. Pass Marks: 85%
Final Assessment
Share Your Feedback
Assessment & Certification
Time to complete