LockedIn FDE Engineering Academy
Become the engineer who can build, evaluate and hand over a working AI system.
Claude Code · LangGraph · MCP · Client delivery
Explore the trainingForward-deployed engineer training by LockedIn Labs
The engineering stack
Recognizable tools. Working labs. A clear artifact to carry into client delivery.
4 technical tracks · Swipe or use arrow keys
Course + coding project · Training access required
Read the repository, repair a failing boundary and hand over a tested change.
Real framework lab · Free walkthrough
Build a tool-calling agent. Checkpoint its state and recover separate human reviews after a restart.
TypeScript SDK · Free Node.js lab
Connect a real SDK client and server. Inspect discovery, tool schemas and host-owned permissions.
Runnable local labs · Free walkthroughs
Trace agent work, recover a failed worker and defend a release using segment-level evidence.
Inside LockedIn FDE
Enter an engineering studio built around a real project. Follow the lesson, inspect the example, practise the decision and keep your own work.
Featured course
Take a small enterprise code change from repository orientation to a tested handoff. Learn the method here, then practise on a complete offline project with real tests.
First: Terminal 101: understand the command before the agent · 35 min
Read the contract, repair the behavior, and hand over tested work.
Your course sequence
From setup to handoffOriginal LockedIn Labs curriculum · Course outline · Training access required
A delivery method that continues beyond the lesson
Understand the operation and agree the smallest useful delivery slice.
Use agents, tools and code within explicit data and permission boundaries.
Challenge the result with difficult cases and inspect the evidence.
Make the work reproducible, explain its limits and name the next owner.
AI-native learning / Individual to cohort
Start with the team’s roles and baseline. Choose the learning depth, agree the weekly practice time, and name the work a receiving engineer should be able to inspect.
Choose the depth. Then practise the delivery.
Explain LLMs, agents, and tools without hand-waving
Design repeatable AI workflows for your own work
Apply agentic patterns inside your discipline
Design and ship multi-step agentic workflows
Build an AI native operating model for a real org
Learning levels describe the curriculum. Cohort dates, access and review arrangements are agreed during planning.
Field notes · Ideas behind the work
Matt Pocock's public talks and open-source workflow make a useful point for AI-native engineers: faster generation raises the value of alignment, shared language, feedback, and deliberate system design.
A composite failure scenario shows how an ambiguous tool schema can turn a capable model into an unreliable operator — and why a common protocol is plumbing, not proof of contract quality.
A forward-deployed engineer works close to a customer to understand a problem, build and integrate a solution, and support its use. Explore the skills, team responsibilities, and evidence to practice.
Find your starting point
Start with a free diagnostic. See which decisions come naturally and where to focus next.
Take the free diagnostic No account required.
