Hello team, so far we have learned what you actually need to know about AI, and we also know that AI is prediction not intelligence, and how you can start using it. In the later piece I also introduced you to the concept of AI Frameworks, these are the very basics on how to use AI:
- Choose an agent: ChatGPT, Claude, Gemini
- Implement an AI Framework:
- Get cracking building solutions
This is progress, and you are well on your way to practice the skills that will help you remain employable in the age of AI. Congratulations, you are now ahead of the curve and on your way to becoming an expert. And this is without the tax of all the jargon words like tokens, and context.
It is time to level up team, and while this might be a little scary I promise I will try to keep it somewhat simple.
Today we are going to learn three things:
- What is an agent
- What you can use them for
- Which are some tools to make one
In a following article —202— I will teach you how to make an agent, without writing a single line of code.
Disclosure: I am an affiliate partner of make.com. If you choose to use Make to create your agents and automations through my link, I'll earn a small commission. However, the knowledge shared in this article can be applied to any similar platform.
The 7 magnificent seven jargon words of AI
So far I have restrained myself from asking you to learn jargon words about AI, I did this because I wanted to give you value instead of taxing your learning experience. But alas, this can’t be prevented any longer if we are to level up. Let’s start with the magnificent seven jargon words of AI.
1. Context
Context is everything the agent can “see” at the moment it makes a decision. Think of it as the information currently loaded into its working memory:
- The user’s request
- Recent conversation history
- any Documents retrieved by a search step
- and Instructions you have given it.
Modern models —GPT 5, Gemini 3.0— rely heavily on this context to “reason”. With good context, an agent appears smart and consistent. With bad context, it appears forgetful or confused.
In practice, no-code tools handle context by passing variables, recent messages, or data fetched from previous steps directly into the model’s prompt so the agent can “act intelligently”.
2. Tools
Tools are actions the agent can take outside of the model. In no-code platforms, these show up as “blocks” or “steps”:
- Send an email
- Read a spreadsheet
- Call an API
- Search a database
- Update a CRM, or trigger a webhook.
Agents are powerful not because they generate text, but because they can decide when to use these tools to move toward a goal. I.E, a customer-support agent might check order status, retrieve shipping information, and update a ticket — all by choosing the right tools in sequence.
3. Workflows
Workflows are the blueprint: the structured sequence of steps or conditions that define what the agent can do and in what order. — A glorified word meaning isntructions.
In no-code platforms, workflows take the form of visual canvases or flow diagrams where you connect actions together.
An agent typically operates inside this workflow, making choices at runtime about which branch to take or which tool to call. You can think of workflows as the rails. Agents move within those rails, making autonomous decisions but never stepping outside the safe path you define.
4. Autonomy
Autonomy is the agent’s ability to decide what to do next without being micromanaged. This is the difference between “help me write an email” and “handle this support issue end-to-end.”
An autonomous agent can break down tasks, choose tools, retry steps, and keep working until a goal is met or a limit is reached. In no-code environments, autonomy often comes from giving the model a goal and letting it choose which steps inside the workflow to trigger.
More autonomy equals more leverage — and also more need for guardrails.
5. Memory
Memory is information the agent can store, recall, and use later.
This goes beyond the current context. Memory may include:
- Past user preferences
- Summaries of previous conversations
- or Facts the agent retrieved earlier in a session.
No-code tools implement memory through databases, documents, or vector stores—a different kind of database.
For example, a personal assistant agent might remember your travel preferences, or a hiring agent might recall how you scored candidates last week. Memory makes agents feel familiar, personalized, and consistent.
6. Planning
Planning is the agent’s ability to break a high-level goal into smaller steps and decide the correct order. This is often referred to as “reasoning.” A planner inside the agent evaluates the goal I.E: “Prepare a monthly marketing report”, maps the necessary steps: collect data, clean data, generate insights, format output; and selects which tools to use for each.
In a no-code environment, planning shows up when the agent dynamically selects blocks or subflows rather than following a strict linear script.
7. Orchestration
Orchestration is everything that coordinates agents, tools, workflows, and limits.
It ensures the agent runs safely, in the right order, and within the boundaries you set. In enterprise environments, orchestration might involve scheduling, monitoring, rate limits, and error handling. In no-code platforms, orchestration is the layer where you define guardrails: what data the agent can access, which actions it is allowed to perform, and how errors should be handled. Orchestration turns a collection of steps into a reliable system.
Orchestration is a concept I want to share now, but it will most likely not be needed until you are running several agents.
So, What is an AI Agent?
“An AI agent is a computer program that can execute autonomously, perceive information, plan —reason— about it, and take actions to achieve a goal on your behalf running tools.”
Across IBM, Google Cloud, AWS, McKinsey, BCG, and NVIDIA, the definitions share the same structure: an AI agent is a program that interacts with its environment, collects or receives information, and uses that information to perform tasks toward a goal. It can use tools, has some form of planning or reasoning, retains memory across steps, and operates with a degree of autonomy.
A practical way to think about it:
An AI agent is a digital worker you assign a job to. It doesn’t just answer questions; it completes tasks; takes actions, follows procedures, and makes decisions within boundaries you set for it.
It is not magic — it is structured prediction connected to real-world tools.
What you can use AI Agents for
AI agents are already being used in real businesses today. Most people do not notice it yet because their experience of AI is limited to chatbots. But the real power is happening behind the scenes — in operations, sales, logistics, HR, customer service, and personal productivity.
Here are some common real-world use cases:
Customer support automation Agents can read tickets, classify issues, pull data from internal tools, update customer records, and draft replies. Instead of a chatbot that “talks,” this is a system that “does.”
Sales assistants An agent can research leads, enrich CRM data, extract key information from emails, and schedule follow-ups. It can even score accounts based on your sales criteria.
HR and recruiting Agents can screen resumes, extract candidate profiles, route applications to the right managers, schedule interviews, and follow up.
Operations and logistics Agents can track shipments, check inventory, reconcile records, read PDFs, or generate daily operational summaries.
Many companies are already using autonomous agents to reduce back-office workload.
Personal assistants You can build agents to organize your inbox, summarize newsletters, draft weekly reports, generate meeting notes, estimate workloads and send automated quotes.
Data processing Agents can ingest spreadsheets, clean data, categorize entries, merge datasets, and produce structured outputs — tasks that previously required analysts and tedious manual work.
Compliance and auditing Agents can review documents, flag anomalies —articles priced at zero usd, or negative quantities—, extract key terms, and compare items against policy rules—can this article be shipped to that address.
Research agents These agents gather information from multiple sources, summarize findings, extract insights, and organize data into reports. Perplexity is one of the best at this.
All the examples above share the same pattern:
- Agents get instructions
- Decide which tools to use
- Take actions, and move toward a goal.
The goal is obviously, defined by you.
Which are some tools to build AI Agents
Today we are focusing on no-code level tools — the simplest way to build agents without writing a single line of code. These tools let you define goals, workflows, and actions, while the AI model handles decision-making.
Make
Make.com is a visual automation platform with a powerful drag-and-drop canvas. You can integrate almost any app, set up scenarios, and let the agent choose which modules to run. Make shines for business workflows: ticketing, CRM updates, emails, databases, and more.
With Make, you can embed an agent inside a scenario and give it autonomy to choose the next best step.
In the next article I will teach you how to create you first AI Agent using Make.
n8n
n8n is an open-source automation tool with a flexible workflow builder. You can run it locally or in the cloud. n8n is great for teams that want more control over their automations.
It integrates deeply with APIs, databases, and custom webhooks. An AI agent can be placed at decision points inside an n8n workflow to plan, choose tools, and automate multi-step processes.
Congratulations you just leveled up
This concludes the overview of what an Agent Is and how it works. Next we will learn how to actually build one of these agents.