Agent Building Blocks
Break an agent into understandable parts so the architecture feels simple.
What this module covers
- Understand the core idea behind agent building blocks.
- Explain the technical idea in plain English without losing the important meaning.
- Connect the concept to real-world tools, workflows, or products.
- Use the topic as part of a larger agent, automation, or AI strategy.
How this deck works
Each lesson gets its own slide. So the slide version mirrors the course structure instead of acting like a tiny summary.
Use arrow keys or the next and previous buttons.
The Basic Agent Loop
Most agents follow a loop: understand, plan, act, observe, adjust, and report.
In simple terms, Most agents follow a loop: understand, plan, act, observe, adjust, and report. Think of it as building a small team out of clear roles instead of one mysterious black box. The goal is not to impress you with jargon but to make the idea usable.
Technical translation: The Basic Agent Loop usually involves structured inputs, model decisions, and some form of state, tooling, or data flow. Under the hood, engineers turn this idea into repeatable components so the behavior is measurable and easier to improve.
Why it matters: this topic shapes whether an agent feels useful or confusing in real life. When this part is weak, the whole system feels less trustworthy.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: explain the basic agent loop in two sentences as if you were teaching a total beginner, then give one example from daily life or work.
Instructions and System Prompts
The instruction layer defines the agent’s role, tone, rules, and limits.
A beginner way to understand this is: The instruction layer defines the agent’s role, tone, rules, and limits. If you can explain it to a friend without using buzzwords, you truly understand it.
Technical translation: when builders talk about instructions and system prompts, they usually mean a concrete system design with prompts, model calls, validation, and often external tools or data sources. The simple idea stays the same even when the implementation becomes more advanced.
Why it matters: many AI products succeed or fail on this exact idea because it affects quality, cost, user trust, and real-world usefulness.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: pick a tool, app, or task you already know and describe where instructions and system prompts shows up inside it.
State and Working Memory
State is the short-term scratchpad that helps the agent remember what is happening right now.
At the simplest level, State is the short-term scratchpad that helps the agent remember what is happening right now. It helps to picture the system as a practical helper that follows a clear job instead of as a magic brain.
Technical translation: State and Working Memory usually involves structured inputs, model decisions, and some form of state, tooling, or data flow. Under the hood, engineers turn this idea into repeatable components so the behavior is measurable and easier to improve.
Why it matters: this topic shapes whether an agent feels useful or confusing in real life. When this part is weak, the whole system feels less trustworthy.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: explain state and working memory in two sentences as if you were teaching a total beginner, then give one example from daily life or work.
Tools and Functions
Tools let the model do things outside plain text, such as opening files or calling APIs.
In simple terms, Tools let the model do things outside plain text, such as opening files or calling APIs. Think of it as building a small team out of clear roles instead of one mysterious black box. The goal is not to impress you with jargon but to make the idea usable.
Technical translation: when builders talk about tools and functions, they usually mean a concrete system design with prompts, model calls, validation, and often external tools or data sources. The simple idea stays the same even when the implementation becomes more advanced.
Why it matters: many AI products succeed or fail on this exact idea because it affects quality, cost, user trust, and real-world usefulness.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: pick a tool, app, or task you already know and describe where tools and functions shows up inside it.
Environment and Permissions
Every agent lives inside an environment with boundaries about what it can read, change, or send.
A beginner way to understand this is: Every agent lives inside an environment with boundaries about what it can read, change, or send. If you can explain it to a friend without using buzzwords, you truly understand it.
Technical translation: Environment and Permissions usually involves structured inputs, model decisions, and some form of state, tooling, or data flow. Under the hood, engineers turn this idea into repeatable components so the behavior is measurable and easier to improve.
Why it matters: this topic shapes whether an agent feels useful or confusing in real life. When this part is weak, the whole system feels less trustworthy.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: explain environment and permissions in two sentences as if you were teaching a total beginner, then give one example from daily life or work.
Feedback Loops
Feedback helps the agent compare what happened against what was supposed to happen.
At the simplest level, Feedback helps the agent compare what happened against what was supposed to happen. It helps to picture the system as a practical helper that follows a clear job instead of as a magic brain.
Technical translation: when builders talk about feedback loops, they usually mean a concrete system design with prompts, model calls, validation, and often external tools or data sources. The simple idea stays the same even when the implementation becomes more advanced.
Why it matters: many AI products succeed or fail on this exact idea because it affects quality, cost, user trust, and real-world usefulness.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: pick a tool, app, or task you already know and describe where feedback loops shows up inside it.
Stopping Conditions
A reliable agent knows when a task is complete, blocked, unsafe, or no longer worth continuing.
In simple terms, A reliable agent knows when a task is complete, blocked, unsafe, or no longer worth continuing. Think of it as building a small team out of clear roles instead of one mysterious black box. The goal is not to impress you with jargon but to make the idea usable.
Technical translation: Stopping Conditions usually involves structured inputs, model decisions, and some form of state, tooling, or data flow. Under the hood, engineers turn this idea into repeatable components so the behavior is measurable and easier to improve.
Why it matters: this topic shapes whether an agent feels useful or confusing in real life. When this part is weak, the whole system feels less trustworthy.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: explain stopping conditions in two sentences as if you were teaching a total beginner, then give one example from daily life or work.
Reporting Results Clearly
The final output should explain what was done, what changed, what failed, and what comes next.
A beginner way to understand this is: The final output should explain what was done, what changed, what failed, and what comes next. If you can explain it to a friend without using buzzwords, you truly understand it.
Technical translation: when builders talk about reporting results clearly, they usually mean a concrete system design with prompts, model calls, validation, and often external tools or data sources. The simple idea stays the same even when the implementation becomes more advanced.
Why it matters: many AI products succeed or fail on this exact idea because it affects quality, cost, user trust, and real-world usefulness.
Example: A coding agent may read files, plan edits, run tests, inspect errors, and report what changed.
Mini exercise: Mini exercise: pick a tool, app, or task you already know and describe where reporting results clearly shows up inside it.
Agent Building Blocks wrapped
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Slides keep the same curriculum, only the presentation changes.