How to Build Your Own AI Agent System in Python (Step by Step)
AI agents are everywhere. But most tutorials show you how to USE an agent. Here's how to BUILD one from scratch.
What Is an AI Agent?
An AI agent is a system that can:
- Understand a goal
- Break it into steps
- Execute each step
- Adapt based on results
Core Architecture
class Agent:
def __init__(self):
self.memory = []
self.tools = {{}}
def think(self, goal):
# Break goal into sub-tasks
steps = self.plan(goal)
results = []
for step in steps:
result = self.execute(step)
results.append(result)
if self.should_adapt(result):
steps = self.replan(goal, results)
return self.synthesize(results)
def register_tool(self, name, func):
self.tools[name] = func
The Intent Parser
The first thing an agent needs is intent recognition:
import re
class IntentParser:
def __init__(self):
self.intents = {{
'search': ['find', 'search', 'look for', 'get'],
'compute': ['calculate', 'compute', 'sum', 'average'],
'transform': ['convert', 'transform', 'change'],
'summarize': ['summarize', 'sum up', 'tl;dr'],
}}
def parse(self, query):
query = query.lower()
for intent, keywords in self.intents.items():
if any(k in query for k in keywords):
return intent
return 'unknown'
Multi-Agent Coordination
For complex tasks, you want multiple agents working together:
class AgentOrchestrator:
def __init__(self):
self.agents = {{
'searcher': SearchAgent(),
'analyzer': AnalysisAgent(),
'writer': WritingAgent(),
}}
def coordinate(self, task):
# Phase 1: Research
data = self.agents['searcher'].run(task)
# Phase 2: Analyze
insights = self.agents['analyzer'].run(data)
# Phase 3: Generate output
return self.agents['writer'].run(insights)
Complete Agent System
I've built a complete agent system with:
- Intent parser
- Task planner
- Tool registry
- Memory system
- Multi-agent coordination
π Get the Agent System β $19 USDT (TRC-20)
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Published str(int(time.time()))
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