AI Agents: A Beginner’s Technical Introduction
What an AI agent actually is, how it differs from a normal LLM application, where tools and memory fit, and when an agent is useful — without the marketing fog.
Rigg Technologies
Engineering Team
TL;DR
An AI Agent is not just an LLM that answers a prompt. It is software that can use an AI model to choose actions, use tools, observe results, keep state, and continue toward a goal. Agents are most useful when a workflow includes messy information or decisions that are hard to express as fixed rules.
1. First, what is an AI Agent?
Imagine you tell a 10-year-old:
Go to the kitchen, check if there is milk in the fridge. If there isn’t, check the shopping list. If milk is already on the list, do nothing. Otherwise add milk to the list and tell me.
There are several things happening here. The child has a goal, some instructions, the ability to observe, make a decision, take an action, and tell whether the job is finished.
That is very close to the basic idea of an AI Agent.
An agent receives a goal, looks at the information available to it, decides what to do, performs an action, observes the result, and continues until the task is completed or it decides that it cannot continue.
That loop is the important part.
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flowchart LR
A["Goal"] --> B["Observe"]
B --> C["Think"]
C --> D["Act"]
D --> E["Result"]
E --> F{"Done?"}
F -- "No" --> B
F -- "Yes" --> G["Finish"]
%% Styling
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classDef decision fill:#f7f7f7,stroke:#111111,color:#111111,stroke-width:1px,rx:6,ry:6;
class F decision;
2. Then where does the LLM come in?
This is where people often mix up two different things.
An LLM and an AI Agent are not the same thing.
An LLM — something like GPT, Claude, Gemini or an open-source model — is primarily a model that understands and generates language.
"What are the three biggest invoices in this list?"It processes the information and gives you an answer. That alone does not make it an agent.
Think of an LLM as the brain inside many modern agents. The rest of the agent is the machinery around that brain.
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flowchart TD
G["Goal"] --> L["LLM
Think / Decide"]
L --> M["Memory"]
L --> T["Tools"]
L --> C["Context"]
T --> API["API"]
T --> DB["Database"]
T --> EM["Email"]
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class L core;
class M,T,C soft;
The LLM may decide what should happen next. The tools allow it to actually do something. The memory keeps track of what happened before. And some code around all of this controls what the agent is allowed to do, when it should stop and what happens when something goes wrong.
3. So what is not an AI Agent?
This is probably easier to understand with code.
question = user_input()
answer = llm.ask(question)
return answerUseful? Absolutely. Agent? Not really. This is simply an LLM application.
question = user_input()
data = database.search(question)
answer = llm.ask(question, data)
return answerStill useful. Still not necessarily an agent. We gave the LLM some extra information, but the application itself is still following a predefined sequence.
Now consider this instead:
goal = "Find customers with overdue invoices and contact the responsible account managers"
while not finished:
state = read_current_state()
decision = llm.decide(
goal,
state,
available_tools
)
if decision.action == "QUERY_INVOICES":
result = invoice_database.search(decision.parameters)
elif decision.action == "FIND_ACCOUNT_MANAGER":
result = crm.find_manager(decision.customer)
elif decision.action == "SEND_EMAIL":
result = email.send(decision.recipient, decision.message)
elif decision.action == "FINISH":
finished = true
save_result(result)Now things become more interesting. The programmer has not hardcoded every step of the process. The agent is deciding which tool it needs next based on the current situation.
That is much closer to an actual AI Agent.
4. The anatomy of an agent
A real-world agent usually has several parts.
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flowchart LR
GOAL["Goal"]
subgraph AGENT["AI Agent"]
direction LR
LOOP["Control Loop"] --> LLM["LLM"]
MEM["Memory / State"] --> LLM
LLM --> TOOLS["Tools"]
TOOLS --> LOOP
LOOP --> MEM
end
GOAL --> LOOP
TOOLS --> EXT["External Systems
ERP · CRM · Email · APIs"]
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classDef muted fill:#f7f7f7,stroke:#d9d9d9,color:#111,stroke-width:1px,rx:6,ry:6;
class LLM dark;
class LOOP,MEM,TOOLS muted;
(a) A goal
The agent needs to know what it is trying to achieve. For example:
"Find all critical support tickets from today and prepare a summary for the operations manager."The goal is different from a normal programming instruction because the exact path to reach it may not always be known beforehand.
(b) An LLM
The LLM usually handles things that traditional software is not particularly good at: understanding language, interpreting messy information, choosing between several possible actions, extracting information from documents, comparing things, and reasoning about what should happen next.
(c) Tools
Without tools, an agent can think but cannot do much.
Search database
Call REST API
Read document
Send email
Create task
Browse internal system
Run calculation
Query ERP
Read CRM
Create reportFrom the agent's point of view, a tool is simply a capability that it is allowed to use.
(d) Memory or state
Imagine an agent doing ten steps but forgetting the previous nine every time. Not particularly useful. Agents therefore normally maintain some form of state.
customer = ABC Ltd
invoice = INV-19231
manager = John
email_sent = trueOr it may involve conversation history, database records, documents, vector search or long-term memory.
(e) The control loop
This is what connects everything.
while true:
observe()
decision = think()
if decision == FINISHED:
break
result = act(decision)
remember(result)Observe. Think. Act. Repeat.
That is the heart of many agent systems.
5. Why is this suddenly useful now?
Technically, software has been making automated decisions for decades. We had rule engines, schedulers, workflow systems, RPA, scripts and bots.
So why all the excitement around agents?
Because traditional automation works extremely well when the rules are predictable.
IF invoice_due_date < today
AND payment_status = UNPAID
THEN send_reminder()You absolutely do not need an AI Agent for that. Normal software is cheaper, faster and more reliable.
But things become harder when the instruction looks like this:
Check today's customer complaints.
Identify which ones appear serious.
Compare them with previous complaints from the same customer.
Check whether an engineer is already working on the issue.
If necessary, prepare an escalation summary and notify the correct person.
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flowchart LR
INPUT["Incoming Work"] --> Q{"Is the path
predictable?"}
Q -- "Yes" --> AUTO["Traditional Automation"]
Q -- "No / Ambiguous" --> AGENT["AI Agent"]
AUTO --> RULES["Rules
if / else
scheduled workflows"]
AGENT --> REASON["Interpret
Decide
Use tools"]
RULES --> RESULT["Result"]
REASON --> RESULT
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class AUTO soft;
There are suddenly several things that are difficult to express as simple
if/else conditions. What does serious mean? Are two
differently written complaints actually describing the same problem? What information
from previous complaints is relevant? Who is the correct person to notify?
This is the kind of gap LLM-based agents can fill.
6. Agents are especially useful around messy information
Traditional software loves structured data.
customer_id = 10021
amount = 54000
status = "UNPAID"Very easy.
Humans, unfortunately, do not always communicate like databases. We write emails, PDFs, reports, WhatsApp messages, notes, support tickets, procurement documents and meeting minutes.
The customer called again. Apparently the freezer has been behaving strangely since yesterday evening. Same thing happened around Eid according to Mahmud bhai. They are getting annoyed now.
A normal rule engine does not know what to do with that. An LLM can understand it remarkably well. An agent can then take that understanding and connect it with actual software systems.
That combination is where things become useful.
7. A practical example
Imagine a company receives hundreds of procurement notices and opportunity documents.
Someone has to:
- download the document,
- read it,
- understand the eligibility criteria,
- compare it with company information,
- identify important deadlines,
- detect missing documents,
- prepare a summary,
- and notify the correct team.
A traditional system can automate parts of this. But the document-reading and interpretation still require a human.
An agent could instead work something like this:
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flowchart LR
A["New Opportunity
Detected"] --> B["Download
Document"]
B --> C["Read
Document"]
C --> D["Extract
Requirements"]
D --> E["Check Company
Profile"]
E --> F["Compare
Eligibility"]
F --> G["Check Required
Documents"]
G --> H["Flag Missing
Items"]
H --> I["Prepare
Summary"]
I --> J["Notify Responsible
Person"]
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classDef important fill:#f7f7f7,stroke:#111,color:#111,stroke-width:1px,rx:6,ry:6;
class A start;
class C,D,F important;
The important point is not that the AI magically does everything.
It is that AI can now participate inside normal software workflows where previously a human had to perform the interpretation step.
8. Agents also don't have to be completely autonomous
There is another misconception here. People hear the word agent and imagine a fully autonomous AI running around doing whatever it wants.
That would usually be a terrible production architecture.
A useful agent can have very strict boundaries.
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flowchart LR
A["Agent Reads
Invoices & CRM"] --> B["Agent Analyzes
Situation"]
B --> C["Agent Drafts
Action Plan"]
C --> D{"Approval
Required?"}
D -- "Yes" --> H["Human Review"]
H -- "Approved" --> E["Execute Action"]
H -- "Rejected" --> F["Record Result"]
D -- "No" --> E
E --> F
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This is called human-in-the-loop design.
The agent does the tedious part. A person remains responsible for important decisions.
In many business systems, this is actually the better design.
9. One agent or many agents?
You will also hear about multi-agent systems.
The idea sounds sophisticated, but it is fairly simple. Instead of one agent doing everything, several specialized agents cooperate.
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flowchart TD
P["Procurement Agent"]
P --> D["Document Agent"]
P --> E["Eligibility Agent"]
P --> R["Research Agent"]
D --> REV["Review Agent"]
E --> REV
R --> REV
REV --> H["Human"]
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class P dark;
class REV soft;
One agent extracts documents. Another checks eligibility. Another gathers missing information. Another reviews the combined result.
There are cases where this architecture makes sense. There are also plenty of cases where it creates unnecessary complexity.
More agents does not automatically mean a better system.
Sometimes one good agent with three well-designed tools is better than ten agents talking to each other.
10. The boring engineering still matters
This is probably the less exciting part of the AI Agent conversation. But it matters the most in production.
A demo agent can be built surprisingly quickly. A production agent is a different problem.
You need to think about things like:
What happens when the LLM gives a wrong answer?
What happens when an API is unavailable?
Can the same action happen twice?
How much will each run cost?
How do we audit what the agent did?
Which tools can it access?
What data can it see?
When does it need human approval?
What happens if the agent gets stuck in a loop?
How do we test it?
How do we know whether it is actually helping?In other words, the interesting part may be the AI.
But most of the reliability still comes from good old software engineering.
11. We use agents in production too
At Rigg Technologies, we are not looking at agents only as an interesting research topic.
We currently have 12 AI agents running in production, working across different kinds of systems and workflows.
Some are small. Some perform only one very specific task. Others interact with several internal services, databases or AI models before completing a job.
And that has taught us something important:
Sometimes a database query solves the problem. Sometimes a cron job solves it. Sometimes normal workflow automation solves it. And sometimes the missing part of the workflow is exactly the kind of reasoning an AI Agent is good at.
Knowing which one you need is much more useful than simply adding AI everywhere.
12. So, in one sentence, what is an AI Agent?
A reasonably useful technical definition would be:
An AI Agent is a software system that can use an AI model to decide what actions to take, interact with tools or systems, observe the results and continue working toward a goal.
Or, if we explain it to the 10-year-old again:
You don't just ask the AI a question.
You give it a job.
And you give it enough tools to try to get the job done.
13. Do you need one?
Maybe.
If people inside your company repeatedly spend time reading information, deciding what to do with it, moving between several systems and performing the next action manually, there may be an opportunity for an agent.
But sometimes ordinary automation is the better answer.
We build both.
If you have a workflow in mind, talk to us.
We can help you figure out whether you actually need an AI Agent, a normal automation system, or something in between.
14. FAQ
What technologies did you use?
We used Node.js, PostgreSQL, Redis, React, and AWS cloud infrastructure to build the complete solution.
How long did the implementation take?
The entire project took approximately 12 weeks from initial discovery to deployment.
How is the system secured?
Role-based access control, end-to-end data encryption, and continuous monitoring ensure high security standards.
Can it integrate with other tools and regions?
Yes, through flexible REST/GraphQL APIs and multi-tenant setup for global scale.
15. Comments & Testimonials
Priya Shah
3 days agoCOO
"Fantastic transformation! This platform is an absolute backbone for our operations."
Rohit Menon
5 days agoIT Head
"Real-time visibility has reduced firefighting significantly. Great work, team!"