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Scaling agentic AI full-stack development with TypeScript, Effect and Node.js

A streamlined, AI-boosted development workflow, from backend to frontend, with agents working inside a solid engineering frame.

Indicative duration
2 to 3 days
Level
Advanced
Delivery
On-site · Remote · Hybrid
Formats
  • In-house
  • Intensive bootcamp
  • Coaching / mentoring
  • Talk / masterclass

Expected outcomes for your teams and business

What the training concretely changes, beyond the classroom:

  • More delivered, with the same team

    AI agents take on the repetitive work so developers focus on design, review and product decisions.

  • Quality kept under control

    Typed, tested and Effect-based foundations make AI-generated code verifiable before it ships.

  • A shared, repeatable workflow

    The team adopts common conventions, guardrails and tooling instead of scattered individual tricks.

  • AI adoption grounded in results

    Teams know where AI pays off and where it does not, so investment goes where it brings value.

Learning objectives

By the end of the course, participants will be able to:

  • Set up a full-stack TypeScript monorepo designed for fast feedback (types, tests, watch mode)
  • Use coding agents effectively: context, specifications, task slicing and review
  • Build guardrails that keep AI-generated code correct: types, Effect, tests and static analysis
  • Share types and contracts end to end, from database to UI
  • Integrate LLM features into a product reliably (structured outputs, errors, retries, costs)
  • Define a sustainable team workflow combining human judgment and agents

Audience

  • Full-stack and backend developers wanting to scale their impact with AI
  • Tech leads redesigning their team's development workflow

Prerequisites

  • Professional TypeScript experience
  • Having followed the Effect course, or equivalent experience, is recommended but not required

Programme

  1. Module 1 A stack built for fast feedback

    • Full-stack TypeScript monorepo structure
    • End-to-end types and shared contracts
    • Watch mode, hot reload and incremental tooling
    • Dependency graphs and affected-only pipelines

  2. Module 2 Effect as a guardrail

    • Why explicit errors and dependencies help agents too
    • Services and layers as stable seams
    • Schema at every boundary
    • Testing effects with substitutable layers

  3. Module 3 Working with coding agents

    • How agents work and where they fail
    • Writing specs and plans an agent can execute
    • Context engineering: instructions, conventions, skills
    • Reviewing and verifying agent output

  4. Module 4 Quality at agent speed

    • TDD as a safety net for generated code
    • Static analysis and architecture rules enforced in CI
    • Small batches and trunk-based development
    • Measuring what really improves

  5. Module 5 Shipping AI features

    • Calling LLMs from Effect services
    • Structured outputs and validation
    • Resilience: timeouts, retries, fallbacks and cost control
    • Evaluating and observing AI features in production

Teaching approach

Around 70% hands-on. Participants build a small full-stack application with coding agents throughout the course, and experience each guardrail by seeing what happens without it. Every module ends with a short retrospective on what the agents got right, what they got wrong, and why. Participants leave with the reference repository, its agent instructions and the slides.

Tailored to your context

Before the session, a short call helps me understand your stack, the agents and tools you already use, and how your team works. The reference application can then mirror your architecture, or the workshop can run directly on your codebase. In coaching format, we set up the workflow on your project and refine it with the team over several weeks.

Let's talk about your needs

A programme to adapt, a team to train, a question about a format? Write to me and let’s discuss.

Get in touch

antoinecoulon.consulting@gmail.com

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