Agent Concepts

Design effective AI agents by mastering these core concepts.

┌─────────────────────────────────────────────────────────┐
│                    AI AGENT ANATOMY                      │
├─────────────────────────────────────────────────────────┤
│                                                         │
│    ┌──────────────┐     ┌──────────────┐                │
│    │ System Prompt │     │    Memory    │                │
│    │  (behavior)   │     │  (context)   │                │
│    └──────┬───────┘     └──────┬───────┘                │
│           │                    │                        │
│           ▼                    ▼                        │
│    ┌────────────────────────────────┐                   │
│    │             LLM                │                   │
│    │   (reasoning & decisions)      │                   │
│    └───────────┬────────────────────┘                   │
│                │                                        │
│         ┌──────┴──────┐                                 │
│         ▼             ▼                                 │
│    ┌─────────┐   ┌──────────────┐                       │
│    │  Tools  │   │ Knowledge    │                       │
│    │ (APIs,  │   │ Base (RAG)   │                       │
│    │  code)  │   │              │                       │
│    └─────────┘   └──────────────┘                       │
│                                                         │
└─────────────────────────────────────────────────────────┘

Core Concepts

What Makes an Agent "Agentic"

An AI agent differs from a simple chatbot in its ability to:

Reason about tasks and break them into steps
Decide which tools or actions to take
Execute actions through tool calls
Observe results and adjust behavior
Iterate until the task is complete

This iterative loop is what makes agents autonomous and capable of handling complex, multi-step tasks.

Key Components

Every AI agent in Axellero consists of:

Component	Purpose	Example
LLM	Reasoning and language understanding	GPT-4, Claude, etc.
System Prompt	Defines agent behavior and constraints	"You are a helpful assistant..."
Tools	Actions the agent can perform	Search, calculate, query database
Memory	Context retention across turns	Chat history, tool results
Knowledge Base	Document retrieval (RAG)	Product docs, FAQs

Agent vs Workflow

Aspect	AI Agent	Workflow
Decision Making	Dynamic (LLM decides)	Static (predefined paths)
Execution Path	Non-deterministic	Deterministic
Best For	Conversational, exploratory tasks	Data pipelines, scheduled jobs
Tool Usage	Decided at runtime	Defined at design time
Cost	Variable (depends on reasoning)	Predictable