Management Brief

AGENTIC
CODING
WORKSHOP

Hands-on, 1.5 days. The team learns to build with AI and stays in control of what ships.

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AI DELIVERS.
HUMAN DECIDES.

Gives the team a repeatable way to use AI for real development work—code, tests, docs—while keeping decisions and quality in their hands.

1.5
Days, In-Person
7
Developers
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Workshop Format

SETUP &
LOGISTICS

DURATION & DELIVERY

  • 1.5 days (~10.5 hours)
  • In-person, hands-on
  • Pedro + 6 developers

TOOLING

  • Primary: Claude Code
  • Overview of GitHub Copilot, OpenAI Codex, and Cursor
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Goals

WHAT SUCCESS
LOOKS LIKE

1

ROLE SHIFT

Developers plan, define, and review—AI writes the code.

DELIVERY PROOF

Each dev ships a working component to preview.

3

OPERATING MODEL

Clear agreement on what AI does vs. what humans own.

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Learning Outcomes

WHAT PARTICIPANTS
WILL LEARN

GIVE AI CLEAR TASKS

Structure work with clear objectives, acceptance criteria, and constraints so AI executes reliably.

CHECK ITS WORK FAST

Tests, reviews, and checklists to validate AI output without slowing delivery.

SET UP REPEATABLE TEAM WORKFLOWS

Conventions, templates, and lightweight automation that make agentic work sustainable.

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Business Value

WHY THIS
MATTERS

FASTER DELIVERY

Shorter cycle from idea to working preview.

BETTER QUALITY

Tests and docs ship by default.

CLEAR GOVERNANCE

AI produces, humans approve.

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Schedule

WORKSHOP
SCHEDULE

DAY 1 9:00am – 5:30pm

  • 9:00 am — Arrive & settle in
  • 9:00 – 12:45 — Foundations: mindset shift, tool landscape, Claude Code setup
  • 12:45 – 2:45 — Lunch
  • 2:45 – 5:30 — Hands-on Practice: multi-file workflows, power features, integrations
  • 5:30 pm — End of day

DAY 2 9:00am – 12:45pm

  • 9:00 am — Arrive
  • 9:00 – 10:30 — Build from Design: Figma-to-code, browser automation
  • 10:45 – 12:00 — Ship It: each dev deploys a component
  • 12:00 – 12:45 — Team Playbook: review, agree on norms, close out

3 focus areas: Foundations — how agentic coding works and why it matters. Practice — real workflows on real code. Proof — ship a working component.

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Participants

THE
TEAM

PEDRO JARAMILLO

Facilitator

Mateo Mateus

Tatiana Terront

Alejandro Camacho

Johan Valbuena

Nicolas Suarez

Miguel Arbelaez

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Measurement

HOW WE
MEASURE SUCCESS

LEADING INDICATORS

Capabilities

Can the team reliably run agentic workflows? Task structuring, validation processes, tool fluency.

LAGGING INDICATORS

Outcomes

Does delivery get faster without increasing defects or risk? Velocity, quality, and governance metrics.

Method: Define with Jamey and engineering leadership.

Specifics come after the workshop so we don't create overhead during adoption.

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Follow-Up

HOW THIS
STICKS

The workshop gives the team the tools. Jamey drives adoption day-to-day. I run three follow-up sessions to support him.

1

WEEK 1

Jamey and Pedro sync on what landed and what's blocked. Pedro runs a troubleshooting session with the team.

WEEK 3

Working session on a topic the team picks. Jamey flags what's working and what needs adjusting.

6

WEEK 6

Jamey owns the playbook. Pedro does a final review, closes out gaps, and hands off fully.

Jamey already leads the engineering team—this builds on his existing role, not around it.

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AI DELIVERS.
HUMAN DECIDES.

Next: lock dates with Jamey, confirm access, agree on guardrails.

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