swati goyal

Posted on Mar 14

Day 3 – Core Components Of An AI Agent

#ai #programming #tutorial #learning

The 5 Core Components of AI Agents (That Most Developers Get Wrong)

Most people think an AI Agent = LLM + prompt.

That’s completely wrong.

👉 An AI Agent is a system of coordinated components.

Not a single model.

Not a prompt.

Not an API call.

If you misunderstand this, your agents will:

behave randomly
break under complexity
burn tokens 💸
become impossible to debug

Let’s fix that mental model.

Big Picture First: An Agent Is Not One Thing

Before diving into components, think about a smartphone.

A phone isn't just:

a screen
a processor
storage
apps

Individually they are useless.

Together they become a powerful intelligent system.

AI agents work the same way.

They are systems of cooperating modules.

The 5 Core Components of an AI Agent

Every production agent architecture usually includes these five parts:

#	Component	Purpose
1️⃣	LLM (Brain)	Reasoning & language understanding
2️⃣	Planner	Decides what to do next
3️⃣	Tools	Executes real-world actions
4️⃣	Memory	Stores knowledge & context
5️⃣	Control Loop	Orchestrates the agent lifecycle

Let's break them down.

🧠 1. The LLM (The Brain)

The LLM is the reasoning engine of the agent.

It handles:

understanding instructions
interpreting observations
generating decisions
producing structured outputs

But here's the key:

⚠️ LLMs do NOT run systems.

They should not:

execute tools
manage memory
track goals
control execution

Simple Analogy

LLM = Subject Matter Expert

A consultant can:

analyze problems
recommend solutions
explain reasoning

But they don't execute the operations themselves.

Common Beginner Mistake ❌

Trying to push everything into a single prompt:

planning
memory
execution
error handling

Result:

fragile prompts
massive token usage
unpredictable behavior

🔑 LLMs should think. Agents should act.

🧭 2. The Planner (Where Autonomy Comes From)

The Planner is what separates an agent from a chatbot.

It answers:

What should happen next?

Planner Responsibilities

The planner:

breaks goals into steps
determines execution order
revises strategy after failure
decides when the task is complete

Example

Goal:

Resolve a customer refund request.

Planner output:

Identify transaction
Check refund policy
Validate eligibility
Initiate refund
Notify customer

If eligibility fails, the plan changes.

That’s autonomous reasoning.

Popular Planning Strategies

Planner Type	Behavior
ReAct	Think → Act → Observe
Plan-and-Execute	Plan first, execute after
Hierarchical	Goals → subgoals
Manager–Worker	Agents delegating to agents

Planning is what gives agents decision-making ability.

🛠 3. Tools (How Agents Affect the Real World)

Without tools, an agent is just a talking brain.

Tools are the hands and legs of the system.

Examples of Tools

Agents commonly interact with:

APIs
databases
search engines
browsers
code execution environments
internal systems (GitHub, Jira, CRM)

Tool Execution Flow

User Goal
   ↓
Planner selects action
   ↓
LLM selects tool + parameters
   ↓
Tool executes
   ↓
Result returned to agent

This separation is critical for safety and auditing.

Example: Research Agent

Task	Tool
Search topic	Web search
Open sources	Browser
Summarize information	LLM
Store notes	Database

Anti-Pattern ❌

Hardcoding tool usage in prompts.

Instead:

✅ Tools should be externally managed and auditable.

🧠 4. Memory (Why Good Agents Improve Over Time)

Memory transforms agents from clever into useful.

Without memory, agents repeat mistakes forever.

Two Types of Memory

Type	Purpose	Example
Short-term	Task state	Steps already completed
Long-term	Cross-session knowledge	Past failures

Memory Flow

Observation
   ↓
Short-Term Memory
   ↓
Relevant Recall
   ↓
LLM Reasoning
   ↓
Optional Long-Term Storage

Example: DevOps Agent

Long-term memory stores:

previous incidents
known fixes
rollback strategies

Result:

🚀 Faster incident resolution.

Common Mistake ❌

Dumping everything into a vector database.

Memory should be curated, not infinite.

🔁 5. The Control Loop (The Most Overlooked Component)

The Control Loop is the system that runs the agent.

It manages the lifecycle.

Responsibilities

The control loop:

runs reasoning cycles
tracks step limits
manages retries
detects failures
prevents infinite loops

The Agent Loop

Perceive → Reason → Plan → Act → Observe
      ↑_______________________________↓

This repeats until a stop condition is met.

Safety Mechanisms

Production agents usually include:

step limits
budget caps
tool allowlists
human approval gates

Without these, agents can run forever and burn money.

Full Agent Architecture

           Goal
            ↓
        Planner
            ↓
     ┌────────────┐
     │    LLM     │
     └────────────┘
      ↓         ↓
    Tools     Memory
      ↓         ↑
   External Systems

        ↑
    Control Loop

Each component has a clear responsibility boundary.

Interactive Exercise

Try designing your own agent.

Pick a task you perform every week.

Fill this table:

Question	Answer
Goal
Decisions needed
Tools used
What should be remembered?
Failure scenarios

If you can answer this, you can design an AI agent architecture.

Common Agent Architecture Mistakes

Mistake	Result
No planner	Random behavior
No memory	Repeated mistakes
No control loop	Infinite execution
LLM doing everything	Unstable system

Key Takeaways

✔ AI agents are multi-component systems

✔ LLMs are only one part

✔ Planning enables autonomy

✔ Tools enable real-world actions

✔ Memory enables learning

✔ Control loops enable safety

When these pieces work together, agents become:

✅ predictable

✅ scalable

✅ safe

Test Your Skills

🚀 Continue Learning: Full Agentic AI Course

👉 Start the Full Course: https://quizmaker.co.in/study/agentic-ai

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