AI Engineering Champions
Leading AI-assisted engineering in embedded and systems teams: Copilot, context, orchestration, PR quality, reusable skills, observability and ROI
About this course
A ten-module programme for the engineers who will lead AI adoption inside embedded and systems engineering teams. It moves from why teams need champions, through GitHub Copilot as an engineering platform and cost-conscious prompt and context engineering, to multi-agent orchestration, AI-assisted pull request quality and reusable skills that can be shipped across projects.
What you’ll be able to do
- Set role-based expectations and success metrics for AI-assisted engineering in your team
- Use Copilot agent mode, repository context and model selection effectively and safely on embedded and systems code
- Engineer prompts and layered context that control token cost
- Design multi-agent engineering workflows with clear boundaries, approvals and escalation
- Automate pull request quality checks with evidence capture and traceability
- Package, version and govern reusable skills for use across projects
- Observe agent runs, measure ROI and build a 90-day adoption roadmap
Before you start
What this course assumes you can already do.
- Professional experience in software engineering, testing, DevOps, SRE or engineering programme roles, and day-to-day use of Git and pull requests.
- Hands-on labs need access to an AI coding assistant such as GitHub Copilot.
The curriculum
Open any module to see its objective, every teaching topic, the practice brief and the checkpoint.
Recaps and reference material are included in the LMS.
PART 1Foundations and Context
01AI Champions Kick-off and Enterprise Context
Explain why engineering teams need AI champions, set role-based expectations, and define the pain points and success metrics the programme will move.
- Why engineering teams need AI championsConcept
- From isolated AI use to enterprise adoptionConcept
- Role-based expectations across architects, developers, testers, DevOps, SRE and programme rolesConcept
- Taking stock of current AI practices in your teamConcept
- Business risks of unmanaged agent usage, and observability as the lensConcept
- Workshop: delivery pain points, blind spots and success metricsHands-on
A baseline and three success metrics
Interview two colleagues in different roles about where delivery slows down and how they use AI tools today. Measure one baseline from your repository, such as median pull request review time or reopened defects. Write role-based expectations for two roles and choose three success metrics the programme should move, with how each will be measured.
Knowledge checkpoint with explained answers
02GitHub Copilot Foundations for Embedded Engineering
Use GitHub Copilot across the IDE, GitHub.com and the command line with good repository context, sensible model choice and safe-usage habits on embedded and systems code.
- GitHub Copilot as an engineering platformConcept
- Agent mode, GitHub.com workflows and command-line usageHands-on
- Repository context handlingHands-on
- Code assistance for embedded and systems softwareHands-on
- Model selection and task fitConcept
- Command-line users, studio-style users and safe-usage expectationsConcept
- Generate, explain, refactor and review on a sample repositoryHands-on
Manual versus assisted, measured
Pick four tasks on a sample embedded codebase: explain an unfamiliar module, refactor a function, add unit tests and review a change. Do two manually and two with Copilot, then swap for a second pair of similar tasks. Record time taken, defects found in review and every correction you had to make to generated code, especially memory or timing issues.
Knowledge checkpoint with explained answers
03Prompt and Context Engineering for Cost-Conscious Delivery
Engineer prompts and layered context for code, review and workflow tasks while keeping token and premium-model usage under control.
- Prompt engineering for code, review and workflow tasksHands-on
- Context layering: repositories, design notes, APIs, standards and issue historyHands-on
- Avoiding token waste and governing premium model usageConcept
- Chunking and retrieval-aware context strategiesConcept
- Reusable, role-specific prompt packsHands-on
- Building a prompt and context library, and estimating context inflationHands-on
A prompt pack that costs less
Write a prompt pack of four prompts for code generation, pull request review, defect triage and design explanation, each with layered context references. Run each task twice, once with your full default context and once with trimmed, targeted context, and record token usage and output quality. Recommend context rules for your team from the difference.
Knowledge checkpoint with explained answers
PART 2Agentic Delivery and Quality
04Agentic Frameworks and Multi-Agent Orchestration Patterns
Choose between single-agent and orchestrated patterns and design multi-agent engineering workflows with clear roles, boundaries, approvals and escalation.
- Agentic frameworks overviewConcept
- Single-agent versus orchestrated-agent patternsConcept
- Multi-agent collaboration across planner, architect, developer, tester, release and operations rolesConcept
- Orchestration boundaries, approvals and escalationConcept
- MCP and tool integration for repository-centric agentsHands-on
- Business intent versus technical executionConcept
- Designing a multi-agent workflow for triage, design review or release hardeningHands-on
A defect-triage workflow on one page
Design a multi-agent workflow for defect triage on an embedded product: a planner that reads the report, an analyst that reproduces from logs, a developer that proposes a fix and a reviewer that checks it. For each agent, state its tools, what it may change, its hand-off artefact and its escalation rule. Mark every human approval point.
Knowledge checkpoint with explained answers
05Pull Request Automation and Quality Checks
Automate pull request quality with AI-assisted review, tests, policy and security checks and readiness gates, with evidence that supports traceability.
- Pull request automation for engineering teamsConcept
- AI-assisted review comments and test suggestionsHands-on
- Policy checks, coding standards and security and compliance promptsHands-on
- Readiness gates, defect prevention and shift-left qualityConcept
- Review quality where correctness and traceability matterConcept
- Configuring a review flow with evidence capture, and closing the loop on a defectHands-on
Catch the same defect automatically next time
Set up a pull request template, AI-assisted review and three automated checks for a sample embedded repository. Seed a realistic defect, such as an unchecked buffer length, and see whether the flow catches it. If it does not, add or change one check so it would, and show the evidence captured for traceability on the next pull request.
Knowledge checkpoint with explained answers
06Production-Ready Reusable Skills and Cross-Project Reuse
Package prompts, instructions, heuristics and agent behaviours into versioned, governed skills that can be shipped across projects and teams.
- Packaging skills, instructions, prompt packs, heuristics and agent behavioursConcept
- Making skills production-ready and shippable across projectsHands-on
- Governing skill libraries: versioning and ownershipConcept
- Reuse across programmes and engineering teamsConcept
- Creating three reusable skills and documenting their deploymentHands-on
Three skills someone else can use
Build three reusable skills for your team, for example a design explainer, a pull request reviewer and a root-cause summariser. Give each a purpose statement, inputs, outputs, two worked examples, a test and known limits, and store them in a versioned library with an owner. Ask a colleague on another project to use one and record what broke.
Knowledge checkpoint with explained answers
07Studio-Style Workflows for Business and Engineering Collaboration
Let programme and product roles shape agent workflows through studio-style tools while technical owners implement them within governance.
- How semi-technical users shape agent behaviourConcept
- Workflow authoring for programme and product rolesHands-on
- Translating business intent into agentic executionConcept
- Approval flows and human-in-the-loopConcept
- Collaborating without bypassing governance, and where studio tools fitConcept
- Handing a workflow over to technical ownersHands-on
From programme intent to an implementable workflow
With a colleague in a programme or product role, define an agent-assisted workflow for weekly release coordination: the outcome, inputs, decisions, approvals and acceptance checks. Write it in a shared template, then hand it to a technical owner who lists what is missing for implementation. Revise it together until they can build it.
Knowledge checkpoint with explained answers
PART 3Observability, Value and Adoption
08Agent Observability and Enterprise Control
- Why monitoring agents mattersConcept
- Observability for agentic systems: traces and replayConcept
- Control scores, cost leaks and prompt and output driftConcept
- Failure patterns, thresholds and alertsConcept
- Adoption tracking and operational reviewConcept
- Lab: inspecting synthetic run traces for cost leakage, failures and weak controlHands-on
A monitoring scorecard from run traces
Using the provided synthetic trace set, define four signals: boundary adherence, cost per successful task, failure rate by pattern and output drift. Compute each, set a threshold with a reason, and replay the three worst runs to explain what happened. Present a one-page scorecard with the actions an operational review should take.
Knowledge checkpoint with explained answers
09ROI, Token Economics and Deployment Governance
Measure the return on deployed AI workflows, govern token spend, and decide when an agent should be scaled, tuned, restricted or retired.
- Calculating ROI for deployed AI workflowsConcept
- Engineering productivity versus production impactConcept
- Cost-aware usage models and token governanceConcept
- Measuring business valueConcept
- Scale, tune, restrict or retire: deciding an agent’s futureConcept
- Usage dashboards and an enterprise review cadenceConcept
- Building a simple ROI model for one workflowHands-on
An honest ROI model
Pick one AI-assisted workflow in your team and build an ROI model with at least three levers, such as review time saved, defects caught earlier and adoption rate, against subscription, token and support costs. Use measured baselines where you have them and label every assumption. Show how the result changes if each assumption is twenty percent worse, and recommend scale, tune, restrict or retire.
Knowledge checkpoint with explained answers
10Embedded Engineering Capstone and Adoption Roadmap
- Integrating the components into one adoption planConcept
- Business case, governance controls and rollout roadmapConcept
- Presenting a use case with blueprint, skills, monitoring design and a 90-day roadmapHands-on
A 90-day roadmap that survives questions
Knowledge checkpoint with explained answers
AI-assisted engineering adoption plan for Veltra Embedded Systems
Veltra Embedded Systems is a fictional maker of industrial controller firmware with architecture, development, test, DevOps, SRE and programme teams. The organisation, repository and run traces are synthetic.
Use case, baseline and success metrics — Choose the use case and measure where delivery stands today.
Assisted workflow and skill pack — Build the AI-assisted workflow and the reusable skills it depends on.
Orchestration and pull request quality — Design the multi-agent workflow and automate quality checks around it.
Observability model and ROI — Define how the workflow will be observed and whether it pays back.
Business case and 90-day roadmap — Present the integrated plan to a review panel.
Next course
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