Fikayo Adepoju

May/June 2026 Cohort

Cohort 2

Cohort 1

Industry-standard multi-agent patterns and leave confident enough to architect/ship autonomous systems that solve real-world problems

Founder | Instructor @LinkedIn

LinkedIn

CircleCI

Twilio

auth0 by Okta

I've taught thousands of engineers the patterns that hold up in production

AI agents are no longer a research topic. They're showing up in production pipelines, internal tools, and enterprise platforms right now — and engineering teams are expected to build with them.But there's a gap. Knowing how to call an LLM is not the same as knowing how to architect a team of autonomous agents that coordinates work, manages state, handles failures, and ships reliably in a real environment.Most tutorials stop at single-agent demos. They won't show you how to structure agents that hand off tasks to each other, scale across complex multi-step pipelines, or make the right architectural call when your system grows beyond a single context window.This course closes that gap. Over four weeks, you'll design and build a fully working, enterprise-grade multi-agent system from scratch; using LangChain and LangGraph, the same stack powering production agent systems at companies like Klarna, Uber, and J.P. Morgan.

Multi-agent systems are the new standard. Learn to architect autonomous teams.

Design multi-agent architectures using industry-standard patterns

Match any problem to the right multi-agent pattern instantly

Build stateful, durable agent pipelines that survive in production

Assemble autonomous agent teams that coordinate without human input

Ship a production-ready multi-agent system as your capstone project

Apply UX principles that make multi-agent systems intuitive to use

The Backend/Full-Stack Engineer - You write Python and ship real systems. Now your team expects AI agents and you want to build them right.

The ML/AI Engineer - You've built models and pipelines but haven't crossed into multi-agent orchestration. You're ready to architect.

The Technical Lead or Senior Engineer - You make architectural decisions for AI features at work and need proven patterns your team can use.

Trained 12000+ learners on AI Agents

Everyone's shipping agents. Almost no one is engineering the harness.

The model is the easy part. The hard part is the harness: the workspace you give your agent, the code execution it runs in, the memory and search it reaches for, the way you fight context rot, and the observability that tells you what actually went wrong. This is the layer that decides whether your agent works in production or quietly fails in front of users.Harness engineering is a discipline. It has patterns, anti-patterns, and load-bearing decisions that most teams make accidentally on a Tuesday afternoon and then live with for a year.This one-day workshop is a deep, framework-agnostic walkthrough of that discipline. One harness layer per hour, each with a concept walkthrough and demo, drawn from real harnesses running in production today.

AI/ML engineers already shipping LLM features who want to move from "it works" to "it works in production"

Backend and software engineers moving into agent work who want the mental models before they pick a framework

Tech leads and staff engineers designing agent systems who need a defensible architecture and a vocabulary to evaluate vendor claims

Cut Your Agent Architecture Decisions from Weeks to 30 Min

Founder | Instructor @ LinkedIn Learning

Recognize when "add another agent" is a reflex, not a solution, via a real case study of over-engineering

Identify the Multi-Agent Trap

A diagnostic question to determine if you need specialized reasoning or just better tools

Apply the Task Decomposition Test

Leave with a concrete, defensible recommendation for your actual project, not "it depends"

Learn directly from Fikayo Adepoju

Fikayo Adepoju

About Fikayo

Courses

Multi-Agent Engineering: Build Autonomous Systems with LangChain & LangGraph

Workshops

Harness Engineering for AI Agents

Lightning Lessons

Cut Your Agent Architecture Decisions from Weeks to 30 Min