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Jeffrey.Feillp
Jeffrey.Feillp

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How to Build Your Own AI Agent System in Python (Step by Step)

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:

  1. Understand a goal
  2. Break it into steps
  3. Execute each step
  4. 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
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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'
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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)
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Complete Agent System

I've built a complete agent system with:

  • Intent parser
  • Task planner
  • Tool registry
  • Memory system
  • Multi-agent coordination

πŸ‘‰ Get the Agent System β€” $19 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs


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