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.

Watch Episode #003

Watch Episode #003 on YouTube

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.

AIYING — Accomplishing with AI

https://dastikop.blogspot.com

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

Living Agent #002 | Which Tasks Really Need an AI Agent?

AI FLUENCY | LIVING AGENT | EPISODE 002

Which Tasks Really Need an AI Agent?

Agent, Automation, Assistant or Human? Learn to choose the right approach.

AI agents can accomplish increasingly complex tasks. But not every task requires an agent, and greater autonomy is not always better.

In this episode, we learn how to recognize genuine agent opportunities and avoid unnecessary complexity.

Published: October 2026 | Last reviewed: 23 October 2026

Watch Episode #002

Watch Living Agent Episode #002 on YouTube

B. Explore & Understand

1. The Real Question

Imagine starting Monday morning with 30 emails, three meetings, and a report that must be completed today.

You could ask AI to help organize your work. But should you immediately deploy an autonomous agent?

Not necessarily.

Some tasks are predictable. Some require a single intelligent response. Others require multiple steps, changing information, tool usage and decisions.

Living Agent Principle:
Choose the simplest approach that reliably achieves the desired outcome.

2. Four Ways to Accomplish a Task

Approach	Best Suited For	Example
Manual Work	Tasks needing personal judgment, relationships or direct accountability.	Making a sensitive personnel decision.
Automation	Predictable tasks with clearly defined rules.	Sending a scheduled reminder.
AI Assistant	Generating, explaining, analyzing or drafting in response to a request.	Summarizing a supplied document.
AI Agent	Multistep goals that benefit from adaptive planning and tool use.	Reviewing authorized sources, identifying unresolved actions and preparing a briefing.

These categories can overlap. An AI assistant may include agentic capabilities, and an agent may combine AI-driven decisions with conventional automation.

The important distinction is how the task is performed, not simply what the product is called.

3. When Is an AI Agent Useful?

Consider an agent when several of these conditions are present:

• The desired outcome is clear, but the required steps may vary.
• The task requires gathering information from multiple sources.
• The system must choose between tools or actions.
• Intermediate results influence subsequent steps.
• The work involves multiple stages or dependencies.
• Human supervision can be defined at appropriate points.
• The benefits justify the added complexity and cost.

None of these conditions alone proves that an agent is necessary.

A carefully designed conventional workflow may still be preferable.

4. When Should You Avoid an Agent?

Avoid unnecessary agentic complexity when:

• A simple formula or rule solves the problem.
• The process is entirely predictable.
• There is no need for adaptive decisions.
• The task is too small to justify setup and monitoring.
• The system would require excessive permissions.
• Errors could have serious consequences without reliable safeguards.
• A person should retain direct responsibility for the decision.

Important: Human oversight is not merely a fallback when AI fails. It is a deliberate design choice, especially for sensitive or consequential actions.

5. Our Continuing Case: Meeting Preparation

In Episode #001, we introduced a professional preparing for a client meeting.

Let's examine that task more carefully.

Task A: Set a Meeting Reminder

A simple scheduled reminder is sufficient. There is usually no need for an AI agent.

Recommended: Automation.

Task B: Summarize One Meeting Note

The user supplies the document and asks for a concise summary.

Recommended: AI Assistant.

Task C: Prepare a Meeting Briefing

The system may need to examine several authorized documents, identify open actions, compare previous decisions and assemble a structured briefing.

An agent may add value if it needs to decide which sources or tools to consult as the work progresses.

Recommended: Evaluate an agentic workflow.

Task D: Approve a Contractual Commitment

AI may assist with analysis and preparation, but an authorized human should make the consequential approval.

Recommended: Human decision, with appropriate AI support.

6. A Practical Decision Framework

Before choosing an agent, answer five questions.

Question	What It Tells Us
Is the task repetitive and rule-based?	Conventional automation may be enough.
Is one intelligent response sufficient?	An AI assistant may be appropriate.
Must the system adapt its next steps?	An agentic workflow may add value.
Does it require external tools or information?	Investigate integrations and permissions.
What happens if the system makes a mistake?	Determine oversight, testing and approval requirements.

The objective is not to maximize autonomy. It is to maximize useful outcomes with acceptable risk.

7. Existing Tools and Technologies

Several existing platforms support different parts of this decision.

Resource	Relevant Use
Zapier	Rule-based integrations and automated workflows.
n8n	Workflow automation with configurable integrations and AI components.
ChatGPT	Interactive assistance and supported tool-based tasks.
Claude	Interactive analysis, drafting and supported agentic workflows.
OpenAI Agents SDK	Developing customized agentic systems.
LangGraph	Designing stateful and controlled agent workflows.

These are examples of relevant technologies, not a ranked recommendation.

Capabilities, pricing and availability may change. Verify current documentation before selecting a solution.

8. Best Practices

• Define the task and measurable outcome first.
• Consider manual, automated and assistant-based approaches before using agents.
• Discover existing solutions before building new ones.
• Begin with read-only access when possible.
• Use least-privilege permissions.
• Test on representative, non-sensitive examples.
• Evaluate correctness, time, cost and failure behavior.
• Require human approval for consequential actions.
• Record the reasons for selecting an agent.
• Revisit the decision when technologies evolve.

9. What's Evolving?

As AI models improve, tasks that once required elaborate agent systems may become manageable through simpler assistants or built-in capabilities.

Conversely, improvements in tool integration, planning, computer use and evaluation may make agentic approaches more practical for tasks that were previously difficult.

The appropriate solution therefore depends on the current state of technology, not a permanent label.

Nothing Is Frozen.
The same task may deserve a different technical approach in the future.

C. Practice & Apply

Exercise 1 — Classify Five Tasks

Choose five activities from your personal or professional routine.

For each activity, decide whether manual work, automation, an AI assistant or an agent is the most appropriate starting point.

Task	Suggested Starting Point
Daily reminder	Automation
Explain a difficult concept	AI Assistant
Research several sources and prepare a briefing	Evaluate assistant versus agent
Approve a sensitive business decision	Human, with optional AI assistance
Organize and summarize authorized project updates	Evaluate automation versus agent

Exercise 2 — Reusable Task-Classification Prompt

Act as an AI Workflow and Agent Advisor. I want to accomplish this task: [Describe your task] Desired outcome: [Describe success] Available information: [Describe inputs and sources] Constraints: [Budget, privacy, tools, time, technical skills and permissions] Evaluate four approaches: 1. Manual work 2. Conventional automation 3. AI assistant 4. AI agent For each approach, assess: - Suitability - Setup complexity - Reliability - Expected human involvement - Privacy and security concerns - Potential costs - Main limitations Recommend the simplest approach that can reliably accomplish the task. If an agent is appropriate: - Explain why an agent adds value. - Identify the capabilities required. - Suggest existing tools or platforms worth investigating. - Distinguish verified capabilities from assumptions. - Propose a safe first experiment. - Define success and failure criteria. - Identify actions requiring approval. If an agent is unnecessary, explain why. Do not invent tools, capabilities, pricing or references. Present the result as a decision table followed by a practical action plan.

Exercise 3 — Agent Suitability Scorecard

Score each statement from zero to two.

0 = Not applicable
1 = Partly applicable
2 = Strongly applicable

Criterion	Score (0–2)
The task involves multiple dependent steps.	___
The next action depends on intermediate results.	___
The task benefits from selecting among tools.	___
Relevant information changes during execution.	___
A clear success condition can be defined.	___
The potential benefits justify the added complexity.	___

Maximum Score: 12.

A higher score suggests that an agent may deserve further investigation.

It does not establish that an agent is necessary or safe.

This is an educational heuristic, not a scientifically validated selection instrument.

Risk, permissions, data sensitivity and available alternatives must be assessed independently.

Exercise 4 — Test the Meeting-Preparation Case

Create three fictional meeting notes. Include some completed actions, some unresolved tasks and a few conflicting statements.

Ask an AI assistant to prepare a meeting brief.

Then, if you have access to a suitable agentic system, repeat the exercise using a controlled workflow.

Compare:

• Completeness of identified action items
• Accuracy of dates and responsibilities
• Ability to identify conflicting information
• Quality of source references
• Time required
• Cost and setup effort
• Ease of human review

Do not assume the agent will perform better merely because it uses more steps or tools.

Exercise 5 — Your Decision Record

LIVING AGENT TASK DECISION RECORD Task: _________________________ Desired outcome: _________________________ Approaches considered: MANUAL / AUTOMATION / ASSISTANT / AGENT Recommended approach: _________________________ Reason: _________________________ Existing solutions investigated: _________________________ Required permissions: _________________________ Risks: _________________________ Human approval points: _________________________ Test performed: _________________________ Actual result: _________________________ Final decision: _________________________ Date reviewed: _________________________

Practical Best-Practice Checklist

• Start with a clearly defined outcome.
• Discover existing solutions before building.
• Verify documentation and availability.
• Use the minimum necessary permissions.
• Protect sensitive information.
• Test with representative examples.
• Evaluate actual results, not promises.
• Require approval for consequential actions.
• Document limitations and failures.
• Revisit recommendations as technology evolves.

Reflection

Think about the last task for which you used AI.

Did it genuinely require an agent, or would a simpler solution have accomplished the same outcome?

Key Takeaway:
Agentic AI is not about delegating everything. It is about identifying where intelligent, controlled delegation creates genuine value.

Coming Next

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

We will examine the building blocks of agentic systems: goals, models, instructions, tools, state, decision-making and oversight.

AI Fluency | Living Agent

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

Educational content. Product capabilities and availability may change. Validate current documentation and conduct appropriate testing before deployment.

Living Agent #001 | Agents Are Real — Why Build What Already Exists?

AI FLUENCY | LIVING AGENT | EPISODE 001

Agents Are Real — Why Build What Already Exists?

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

AI agents are no longer merely futuristic ideas. Real agentic systems, frameworks and reusable capabilities are already available. The challenge is knowing what exists, what works, and what is appropriate for your particular problem.

Published: October 2026 | Last reviewed: 23 October 2026 | Series: Living Agent

Watch Episode #001

Watch Living Agent #001 on YouTube

B. Explore & Understand

1. The central idea

Imagine you need help preparing for an important meeting tomorrow.

You want to review relevant documents, identify unfinished tasks, prepare discussion points and create a useful meeting brief.

One approach is to ask an AI assistant to suggest what you should do.

Another approach is to use an agentic workflow that can retrieve authorized information, perform multiple steps, use appropriate tools, and produce a structured result.

But before building such a workflow, there is an important question:

Does a suitable solution already exist?

This question is the starting point of Living Agent.

2. What exactly is an AI agent?

An AI agent is a software system that uses AI to pursue a goal, often by deciding which steps to take, using tools, processing their results and continuing until a task is completed or a stopping condition is reached.

Depending on its design, an agent may include:

• A goal or task to accomplish
• An AI model to interpret and reason
• Instructions defining expected behavior
• Tools for accessing information or taking actions
• Context or memory for maintaining relevant state
• Controls for permissions, safety and oversight
• Evaluation to determine whether it succeeded

Not every AI-powered application is an agent. Some systems only generate answers, while others follow predetermined workflows.

Agentic behavior generally involves some degree of decision-making about the steps or tools needed to achieve a goal.

3. Discover before reinventing

The growing agent ecosystem offers several ways to solve practical problems.

Approach	When it makes sense
Use an existing agent	Its capabilities already match your task and requirements.
Use an existing skill	A reusable procedure or specialized capability can extend your current assistant or agent.
Configure an existing platform	You need different instructions, tools, integrations or approvals.
Build a new agent	Existing solutions cannot adequately meet your requirements.
Use ordinary automation	The task is predictable and does not require AI-driven decisions.
Do the task manually	Automation offers little benefit or introduces unnecessary risk.

The best solution is not necessarily the most autonomous or technically sophisticated. It is the one that solves the problem reliably and appropriately.

4. Where can we discover agents and skills?

The following resources illustrate different parts of the existing ecosystem. They are not interchangeable products.

Resource	What to explore
OpenAI Agents SDK	A framework for developing agents with tools, handoffs, guardrails and tracing.
Claude Agent Skills	Reusable skills that provide specialized instructions and resources for supported agent workflows.
skills.sh	Discover community-published skills. Inspect their source and permissions before installing or using them.
Google Agent Development Kit	Agent development and orchestration.
Microsoft Copilot Studio	Creating and managing business-oriented agents and workflows.
Model Context Protocol	A protocol for connecting AI applications to external tools and information sources.

Important: A skill is not necessarily a complete agent. An SDK is not a ready-made business solution. An MCP server provides connectivity, not automatically safe or reliable behavior. Understand what each resource actually offers.

5. Our continuing case: The meeting assistant

Throughout Living Agent, we will return to practical problems and examine how available solutions evolve.

Our first scenario is a professional preparing for tomorrow's client meeting.

The task

Review relevant meeting notes, identify open questions, summarize pending decisions, and prepare a concise briefing.

Step 1 — Define the outcome

A one-page briefing containing the agenda, important background, unresolved issues, and recommended discussion points.

Step 2 — Discover available solutions

Investigate whether an existing assistant, agent, document workflow or reusable skill can perform the required work.

Step 3 — Compare alternatives

Assess document access, reliability, privacy, setup effort, pricing, integration requirements and user control.

Step 4 — Start with limited permissions

Provide approved documents and request a draft briefing. Do not authorize sending messages or changing calendars during the initial test.

Step 5 — Verify the result

Check factual accuracy, completeness, source references and whether the output meets the original goal.

6. How should we choose?

An agent recommendation should consider more than a product's popularity.

• Fit: Does it solve the actual problem?
• Availability: Can the intended user access it?
• Reliability: Does it work consistently?
• Security: What information and permissions does it require?
• Cost: What are the subscription, usage and maintenance costs?
• Control: Can users inspect, approve and stop actions?
• Maintainability: Will the solution remain practical as technology changes?

These considerations apply to existing agents as well as newly developed ones.

7. What is evolving?

The foundations of agentic systems continue to develop.

Important areas to watch include computer-use capabilities, multimodal agents, agent-to-agent collaboration, tool interoperability, reusable skills, long-running workflows, evaluation methods and agent security.

Some approaches are mature enough for specific production uses, while others remain experimental or are evolving rapidly.

Living Agent will distinguish documented capabilities, practical demonstrations, emerging research and unverified claims.

The Living Agent principle:
Technology evolves. Recommendations change. Evidence matters. Learning continues.

C. Practice & Apply

This section turns the episode into a practical learning experience.

Exercise 1 — Discover your first agent opportunity

Choose one repetitive or time-consuming task from your everyday life or work.

Examples:

• Preparing a weekly progress report
• Summarizing research articles
• Organizing meeting notes
• Comparing suppliers
• Preparing a study revision plan
• Reviewing customer feedback

Describe the task, desired outcome, available information and actions you would permit an AI system to take.

Exercise 2 — Use this discovery prompt

Copy the following prompt into an AI assistant. If current web research is required, use a system with web access and verify its links.

Act as an AI Agent Discovery Advisor. My task: [Describe the task] My desired outcome: [Describe the result] My constraints: [Budget, privacy, technical skills, available software and permissions] Help me: 1. Decide whether I need an AI agent, a conventional automation, an assistant, or a manual process. 2. Identify existing agents, tools, skills or platforms that may help. 3. Distinguish verified capabilities from assumptions or untested claims. 4. Compare the most suitable options for cost, complexity, security, reliability and ease of use. 5. Recommend whether I should USE, ADAPT, BUILD or AVOID. 6. Suggest a safe first experiment using minimal permissions. 7. Provide a checklist for evaluating whether the experiment succeeded. Do not invent products, capabilities, prices or source links. If current information cannot be verified, clearly state that limitation. Ask clarifying questions when needed.

Exercise 3 — Evaluate the recommendation

For your preferred solution, answer the following questions:

1. Does the solution actually exist and remain available?
2. Does its documentation support the claimed capabilities?
3. Can it perform the task using the information I can safely provide?
4. What permissions does it need?
5. Can I test it without affecting real records or other people?
6. How will I verify its output?
7. What would make me reject the solution?

Exercise 4 — Your first agent decision record

LIVING AGENT — DECISION RECORD Task: ____________________________ Desired outcome: ____________________________ Candidate solution: ____________________________ Source / documentation: ____________________________ Approach: USE / ADAPT / BUILD / AVOID Why this approach? ____________________________ Required permissions: ____________________________ Main risks: ____________________________ First safe experiment: ____________________________ Success criteria: ____________________________ Actual test result: ____________________________ Next action: ____________________________ Date reviewed: ____________________________

Best-practice checklist

• Start with a clearly defined outcome.
• Discover existing solutions before building.
• Verify documentation and availability.
• Inspect third-party skills and code before installation.
• Use the least permissions necessary.
• Keep sensitive information out of unnecessary tests.
• Require approval for consequential actions.
• Evaluate real outputs against clear success criteria.
• Record failures and limitations.
• Revisit recommendations as capabilities evolve.

Further reading

• OpenAI Agents SDK documentation
• Anthropic Agent Skills documentation
• Explore skills.sh
• Model Context Protocol

What did we learn?

AI agents are real, but not every problem requires a new agent.

Discovering, evaluating and responsibly reusing existing capabilities is itself an essential agentic AI skill.

The most valuable agent is not necessarily the newest or most autonomous. It is the one that achieves the intended outcome reliably, safely and efficiently.

One question to take away:
Before you build your next AI agent, have you discovered what already exists?

Coming next

Living Agent #002 — Which Tasks Really Need an AI Agent?

We will explore how to recognize genuine agent opportunities and distinguish them from tasks better handled by assistants, ordinary automation or human judgment.

AI Fluency | Living Agent

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

This educational article is not an endorsement of any particular vendor. Product capabilities, pricing, permissions and availability may change. Verify current documentation before deployment.