Learn agentic coding by owning one complete delivery loop.

Start with files and folders, an editor, the command line, and how a browser talks to a server. Learn enough HTML, CSS, and JavaScript to change a simple page and explain the change. MDN's curriculum offers a structured route through these foundations and Git-based version control. Work through one lesson, reproduce the example, and change one behavior yourself before moving on.

For the classifier exercise, first write ten fictional support requests in a spreadsheet. Decide which categories are useful, where two categories overlap, and what an uncertain request should do. Then make a static form that displays a request and records your decision. Add deterministic rules before a model. You now have a baseline whose behavior you can compare with a later AI implementation.

Use an assistant to explain unfamiliar code and suggest small experiments. Keep a learning log: what you predicted, what happened, and what you can now explain without the assistant. If you cannot trace a value from a form field to its output, reduce the exercise. A working demo is encouraging; understanding why it works is the next milestone.

Keep your familiar language and test framework. Your next learning problem is the boundary around the agent: what context it receives, which files it may change, what commands it may run, and what requires a person to decide. Give it a small repository with a README, executable tests, a few fixtures, and one narrowly described issue.

For example, ask it to add an uncertain category without changing the existing API response shape. State the expected behavior before it writes code. Inspect its diff, run the relevant checks, and supply one awkward input that was absent from the happy path. Then ask a colleague to use the result from the README. The handoff often reveals assumptions that a green test did not cover.

GitHub's guidance on Copilot code review describes automated review as assistance within the review process. Apply that distinction to any coding agent: a model can propose a patch or identify a concern, but the reviewer still needs evidence that the change is correct. Separate the agent's explanation from the commands that actually ran and the output they produced.

Anthropic distinguishes workflows that follow predefined paths from agents that decide how to use tools as they proceed. That distinction gives a learner a useful progression. First build a fixed sequence that reads a request, applies a rule or model, validates the result, and presents a recommendation. Add dynamic tool choice only when you can name a case the fixed sequence cannot handle well.

Introduce failures one at a time: an empty request, a conflicting label, malformed model output, a timeout, or an instruction embedded inside the request that asks the system to ignore its task. Decide the expected behavior before running each example. An uncertain result can go to a person; a timeout can produce a clear retry state. Neither should silently turn into a confident category.

This teaches the part of agentic work that transfers between tools: defining inputs, checking outputs, limiting effects, and keeping a useful record of failure. You can change the model later without discarding the learning. A library tutorial supplies implementation mechanics; your examples establish what the system must do.

The AI software development lifecycle, or AI SDLC, connects discovery, design, implementation, evaluation, release, operation, and learning. For this exercise, each stage should leave a small artifact that helps the next person make a decision. Keep the amount of documentation proportional to the task; the point is continuity, not a large binder.

Begin with the user's problem and the current manual process. Record your proposed behavior and its limits. Keep the implementation and evaluation examples in version control. Before releasing even a local demonstration, explain how someone will recognize a failure and return to the previous version. After a trial with fictional data, revise the problem statement when the evidence changes your mind.

The Training site's AI SDLC page describes a control framework and its evidence chain. Use it to ask sharper questions about your exercise, rather than treating a diagram as proof that your project implements those controls. NIST's AI Risk Management Framework is another primary reference for considering risk across design, use, and evaluation; reading it does not confer compliance or certification.

General courses are useful when they repair a foundation you keep encountering: programming, Git, APIs, databases, testing, or security. Provider courses are useful when you need to understand a tool's actual interfaces and limits. A structured academy or cohort is useful when it supplies assignments, feedback, revision, and people who can explain why your work missed the bar. These options can complement one another.

Before committing to a program, inspect an assignment and its review criteria. Ask what the learner must produce, whether feedback leads to another attempt, which prerequisites are assumed, and whether instruction or review is available for the dates you need. Separate a published curriculum from a staffed session. Compare the evidence you will leave with, not the number of tools named on the landing page.

LockedIn FDE Training publishes curriculum briefs, role paths, provider-learning references, and its qualification method for inspection. The public tour shows the learning experience; full lessons and runnable workbenches require provisioned enrollment. Review availability, cohort access, and any qualification decision have their own requirements. Start by inspecting those boundaries and the work a route asks you to produce.

Forward-deployed engineering adds the customer's operating context to the software problem. Practice asking who handles an exception, which systems contain the source of truth, what access is needed, and who will own the change after you leave. Being able to build the classifier is one skill. Being able to establish whether it should exist, fit it into a workflow, and transfer it to an operating team is a broader assignment.

For a training exercise, ask another person to play the process owner using a written fictional scenario. Preserve their answers separately from your interpretation. When the scenario changes, revise the design and explain the tradeoff. Label the role-play as simulated; it cannot establish real customer demand or field delivery experience.

The related fdebenchmark.org site provides starting paths and a practice skills assessment that can help identify a next learning question. Its practice feedback is distinct from a verified practitioner rating or a hiring decision. A useful sequence is to identify a gap, practice it here or through another suitable course, and retain the resulting evidence for a later review.

Publish a sanitized README, your problem brief, a small set of evaluation examples, a demonstration of a known failure, and the change you made after review. Keep personal information, customer material, credentials, and confidential repository content out of a public portfolio. State which decisions you made and which work the assistant produced. A reader should be able to reproduce the exercise and understand the remaining uncertainty.

Use a simple exit check: can another person run the project, explain its limits, identify a failure, and take the next step without you narrating every click? If the answer is no, you have found the next learning task. You do not need another framework to make that task valuable.

Training, assessment, and consulting serve different decisions. This site develops and documents learning. The benchmark site helps people inspect assessment methods and practice skills. LockedIn Labs' company site explains AI consulting and implementation for organizations moving work into operation. These are related properties; their links describe that relationship and do not imply independent endorsement or automatic score credit.