LOCKEDIN / FORWARD-DEPLOYED ENGINEERING 101
Understand it.
Then build it.
You can be an experienced engineer and still be new to agents. Start with what each thing is and how it helps a person do useful work. Explore 48 original introductions across software, agents, cloud, security and delivery. Start here without a model key; each topic connects to deeper instruction and practice.
- 101UnderstandPlain language, visual walkthrough, vocabulary
- 102Build with guidanceOriginal lessons, examples and knowledge checks
- 103Practise and explainRunnable labs, failure cases and handover
- 01Give it a task
- 02Choose and use a tool
- 03Produce or stop
An application built around an AI model
What is an AI agent?
An AI agent is software that uses an AI model to choose some of the steps needed to complete a task. It can call tools, inspect their results and decide what to do next within limits set by the application. The model is one component; the agent also needs code, tools and rules about when to stop.
An FDE needs to decide when model-driven choices help a real task and when a predictable sequence is enough. An agent is useful only if it improves the work and stays within its allowed scope.
CONNECT IT TO WHAT YOU KNOW
Start with something familiar.
A normal program follows rules you wrote. An agent application can ask a model to choose the next permitted action based on what it observes, then use ordinary code to execute and check that action.
The model proposes; application code grants access, runs tools and enforces limits. A chatbot that only answers text does not necessarily have that action loop.
Words worth knowing
- Model / LLM
- The AI component that generates text or proposes an action; LLM means large language model.
- Tool
- A function the application makes available, such as looking up a procedure.
- Agent loop
- The repeated cycle of model choice, permitted tool use and checking whether to continue.
- Workflow
- The connected steps used to complete a task. Some or all can be fixed in advance.
ONE EXAMPLE / FICTIONAL CASE
Watch the work take shape.
For a maintenance question, a chatbot might write a general answer. An agent can decide to look up an approved procedure, read the result, then draft a response for a person to review. It cannot gain permission to send the response just by deciding to do so.
- 01Give it a task
- 02Choose and use a tool
- 03Produce or stop
Give it a task
A person asks for a draft reply to a maintenance request. The application supplies the request and the allowed actions.
Task: prepare a draft; do not send itFROM RECOGNITION TO REASONING
Separate a chatbot, a fixed workflow and an agent.
- 01
Describe a question answered with no tools.
- 02
Describe a fixed lookup-then-format sequence.
- 03
Describe one task where choosing the next diagnostic action adds value, and set a stopping rule.
Three examples with the decision owner marked.
Compare your reasoning when you are ready
If the next steps are already known, use a fixed workflow first. Introduce a model-controlled choice only where its flexibility helps the task and can be evaluated.
Keep notes in your own working copy. Moving through these cards does not record completion.
TURN THE IDEA INTO USEFUL WORK
You can now go deeper.
A chatbot answers. A fixed workflow follows prescribed steps. An agent can choose some steps. None of these names grants permission to act.
Academy lessons use your existing training access. Free guides stay open.
Learn with the original creators
Compare the discussion of simple workflows and agents with your own task. Return with one reason to use an agent and one reason not to.