Living Agent #003 | What Makes an AI Agent an Agent?

AI FLUENCY | LIVING AGENT | EPISODE 003

What Makes an AI Agent an Agent?

Seven Building Blocks Explained

An AI agent is more than a chatbot with tools. It is a system designed to work toward a goal, make or select decisions, use available capabilities, respond to results and operate within defined boundaries.

But what exactly makes such a system agentic?

Let's explore the essential building blocks, how they work together, and how to recognize them in real-world applications.

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B. Explore & Understand

1. What Is an AI Agent?

An AI agent is a software system that uses an AI model and supporting mechanisms to pursue a goal by selecting and executing actions within an operating environment.

Depending on its design, an agent may use tools, maintain task state, consult external information, evaluate intermediate results and adapt its next steps.

An agent does not necessarily need long-term memory, a large collection of tools, or unrestricted autonomy.

Key Idea:
Agentic behavior comes from the system's ability to choose and coordinate actions toward a goal, rather than merely producing a single predetermined response.

2. The Seven Building Blocks

The following seven-part model is a practical educational framework for understanding agents.

It is not a mandatory technical standard. Different agent frameworks organize these capabilities in different ways.

Block 1 — Goal

An agent needs a task or desired outcome.

A goal tells the system what it is trying to accomplish.

Example: Prepare a reliable briefing for tomorrow's client meeting.

A good goal specifies expected results, scope, constraints and success conditions.

Block 2 — Model and Instructions

The AI model interprets information and helps select appropriate next actions.

Instructions define the system's role, behavior, limitations and operating rules.

Example: Use only approved project documents. Distinguish confirmed facts from assumptions. Do not send external messages.

The model does not automatically possess all relevant knowledge or permissions.

Block 3 — Planning and Decision-Making

An agent may decompose a goal into steps and decide what to do next.

Planning can be explicit or implicit. Some agents use structured plans, while others select the next action from the current context.

Example: First identify relevant documents, then extract open actions, then check for unresolved issues.

Block 4 — Tools and Actions

Tools allow an agent to interact with systems beyond the model itself.

Examples include search, databases, documents, calendars, APIs, code execution and authorized applications.

Example: Search an approved project folder for the latest meeting notes.

Tool availability does not imply permission to use every available function.

Block 5 — State and Memory

State represents information the system needs while performing a task.

It may include completed steps, tool results, outstanding questions, selected documents and current progress.

Long-term memory is optional and is not the same as temporary task state.

Example: Remember that three actions have been verified and two remain unresolved during the current workflow.

Block 6 — Feedback and Evaluation

An agent can inspect the results of its actions and determine whether additional work is necessary.

This may involve checking completeness, validating sources, detecting errors or comparing results against criteria.

Example: Check whether each meeting action has an owner, deadline and source.

Feedback is useful only when the system responds appropriately to it.

Block 7 — Oversight and Control

An agent needs appropriate operating boundaries and control mechanisms.

These may include permissions, approval checkpoints, logging, time limits, spending limits, guardrails and termination conditions.

Example: Allow the agent to draft a client email, but require human approval before sending.

Oversight is a system design responsibility, not merely an instruction in a prompt.

3. How the Building Blocks Work Together

A simplified agent workflow can be expressed as:

GOAL → OBSERVE → DECIDE → ACT → CHECK RESULT → CONTINUE OR STOP

The system receives a goal, examines the available context, selects an action, uses an authorized capability and evaluates the result.

It may repeat this cycle until the goal is achieved, an operating limit is reached or human intervention is required.

This is often called an agent loop.

Important Distinction:
An agent loop does not mean the system should continue indefinitely. Reliable agents require clear stopping conditions and bounded execution.

4. Our Continuing Case: Client Meeting Preparation

Consider the professional introduced in the earlier Living Agent episodes.

The objective is to prepare a useful, accurate briefing before a client meeting.

Building Block	Practical Application
Goal	Prepare a meeting briefing.
Model and instructions	Follow briefing requirements and source restrictions.
Planning	Identify sources, review decisions and organize findings.
Tools	Read approved notes and project documents.
State	Track reviewed sources and unresolved actions.
Feedback	Check completeness, accuracy and source support.
Oversight	Require review before external communication.

The result is not simply a generated paragraph. It is a controlled workflow that can gather, organize, check and present information.

However, if all required documents are already available in one prompt, an ordinary AI assistant may still be sufficient.

5. Agent vs. Chatbot vs. Workflow Automation

System	Typical Behavior	Example
Chatbot or assistant	Responds to a user request; may also have tools and agentic features.	Summarize this document.
Rule-based automation	Executes predefined steps and conditions.	Send a reminder at 9 AM.
AI agent	Selects and coordinates actions based on a goal and evolving context.	Investigate unresolved project actions and prepare a sourced briefing.

These categories overlap in practice. An assistant can run agentic workflows, and an agent can contain deterministic automation steps.

The system's behavior matters more than the marketing label.

6. Discover Existing Agent Technologies

Before building an agent from scratch, investigate established platforms, frameworks and reusable components.

Resource	What to Explore
OpenAI Agents SDK	Agent definitions, tools, handoffs, guardrails and tracing.
OpenAI Developer Documentation	Models, tool calling and current agent-building capabilities.
LangChain and LangGraph	Agent orchestration, stateful workflows and execution control.
Anthropic Documentation	Tool use and agent design guidance.
n8n	Visual workflow automation and AI-enabled integrations.
skills.sh	Discover reusable agent skills; review their source and permissions before adoption.

These resources represent different layers of an agent ecosystem.

A reusable skill is not necessarily a complete autonomous agent. An orchestration framework is not the same as a ready-to-use business application.

Review current official documentation, deployment requirements and security controls before selecting a technology.

7. Common Misconceptions

Misconception	Better Understanding
An agent must use many tools.	A small agent can be useful with limited capabilities.
Every agent needs long-term memory.	Temporary task state may be sufficient.
An agent must operate without humans.	Human approval can be part of agent design.
More autonomy always means better performance.	Autonomy should match task requirements and risk.
A chatbot with a tool is always an agent.	Tool access alone does not establish agentic behavior.
Agent architecture is fixed.	Models, frameworks and design patterns continue evolving.

8. Best Practices for Designing Agents

• Define a measurable outcome before selecting technology.
• Discover existing agents, skills and frameworks first.
• Keep instructions explicit and testable.
• Prefer narrowly scoped tools and permissions.
• Separate temporary task state from persistent memory.
• Treat external content and tool results as potentially untrusted.
• Use structured outputs when appropriate.
• Set limits on actions, time, cost and retries.
• Require approval for consequential operations.
• Test normal cases, failures and unexpected inputs.
• Maintain logs and evaluate real task outcomes.
• Revisit the architecture as technology changes.

9. What Is Evolving?

Agent technologies are advancing across several dimensions:

• More capable reasoning and planning
• Improved tool use and integration
• Reusable agent skills
• Stateful and persistent workflows
• Computer-use capabilities
• Multi-agent collaboration
• Better tracing and evaluation
• Stronger governance and security controls

As these capabilities evolve, the best architecture for a task may change.

Living Agent Principle:
Understand the foundations. Discover what already exists. Build only what is needed. Keep learning as the ecosystem evolves.

C. Practice & Apply

Exercise 1 — Identify the Building Blocks

Choose an AI application you currently use.

Examine it against the seven building blocks.

Building Block	Your Observation
Goal	What outcome does the system pursue?
Model and instructions	What intelligence and rules guide it?
Planning	Does it select or adapt next steps?
Tools	What external capabilities can it use?
State	What information does it retain during execution?
Feedback	How does it check progress or results?
Oversight	What permissions and approval points exist?

Some characteristics may not be visible from the product interface. Mark them as unknown rather than assuming they exist.

Exercise 2 — Reusable Agent Anatomy Prompt

Act as an AI Agent Architecture Advisor. I want to understand or design an agent for this task: [DESCRIBE THE TASK] Desired outcome: [DEFINE SUCCESS] Available tools and information: [LIST RESOURCES] Constraints: [PRIVACY, COST, TIME, PERMISSIONS AND RISKS] Analyze the following building blocks: 1. Goal 2. Model and instructions 3. Planning and decision-making 4. Tools and actions 5. State and memory 6. Feedback and evaluation 7. Oversight and control For each block, explain: - Why it matters - Whether it is needed - How it would work - A practical example - Main risks or limitations Then: A. Determine whether an AI agent is actually necessary. B. Suggest simpler alternatives. C. Identify existing agents, platforms or reusable skills worth investigating. D. Distinguish verified capabilities from assumptions. E. Propose the simplest viable architecture. F. Define permissions, approval points and stop conditions. G. Recommend a safe pilot with measurable success criteria. Do not invent product capabilities, documentation or test results. Present a decision table, architecture outline and action plan.

Exercise 3 — Design the Client Meeting Agent

Use three fictional documents:

1. Previous meeting notes containing action items.
2. A project status report with recent updates.
3. A list of questions for the upcoming meeting.

Design a workflow that produces:

• A concise meeting summary
• Confirmed project updates
• Open actions with owners
• Unresolved questions
• Source references
• Items requiring human confirmation

Begin with an AI assistant. Only introduce an agentic workflow if adaptive tool selection or multistep execution adds demonstrable value.

Exercise 4 — Evaluate the Agent Loop

If you have access to an agent-building environment, run a small controlled test.

Observe whether the system:

1. Understands the intended goal.
2. Selects appropriate actions.
3. Uses only authorized tools.
4. Tracks intermediate results.
5. Recognizes incomplete information.
6. Corrects or reports errors.
7. Stops when the task is complete.

Safety Note:
Use fictional or approved test data. Avoid granting unnecessary access to email, files, financial systems or external communications.

Exercise 5 — Agent Evaluation Scorecard

Criterion	Evidence to Collect
Goal completion	Was the required output delivered?
Accuracy	Are statements supported by source data?
Tool discipline	Were only approved tools used?
State management	Were completed steps tracked correctly?
Feedback	Were missing or conflicting details detected?
Oversight	Were approval requirements respected?
Efficiency	Was the result worth the time and cost?
Reliability	Does performance remain acceptable across repeated tests?

Evaluate using observable evidence, not the agent's own claims of success.

Exercise 6 — Your Agent Design Record

LIVING AGENT EPISODE 003 DESIGN RECORD Task: ____________________________ Desired outcome: ____________________________ Is an agent necessary? YES / NO / UNCERTAIN Reason: ____________________________ Goal: ____________________________ Model and instructions: ____________________________ Planning approach: ____________________________ Tools: ____________________________ Task state: ____________________________ Feedback mechanism: ____________________________ Permissions and oversight: ____________________________ Stop conditions: ____________________________ Existing solutions reviewed: ____________________________ Test results: ____________________________ Human approval required: ____________________________ Next improvement: ____________________________ Review date: ____________________________

Practical Checklist

• Is the goal clearly defined?
• Does the system need adaptive actions?
• Have simpler approaches been considered?
• Have existing solutions been investigated?
• Are tools and permissions limited?
• Is task state managed appropriately?
• Are intermediate results checked?
• Are stop conditions defined?
• Are consequential actions controlled?
• Can success be measured independently?

Reflection

Consider an AI system that can search documents, draft emails and update a calendar.

Is it automatically an AI agent?

Or does the answer depend on whether it can choose and coordinate those actions toward a goal?

Key Takeaway:
An AI agent is defined by purposeful, adaptive action within an operating environment. Its value depends not on how many capabilities it possesses, but on whether those capabilities help accomplish a real goal reliably and safely.

Coming Next

Living Agent #004 — The AI Agent Loop: How Agents Think, Act and Adapt

We will explore how agents observe information, select actions, use tools, evaluate results and decide when to continue or stop.

AI Fluency | Living Agent

Discover what exists.
Build what matters.
Evolve with what emerges.

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Educational content. Technology capabilities, product features and availability may change. Verify current documentation and test systems appropriately before real-world deployment.