Building Your First Agentic AI
Step-by-step guide to creating a simple autonomous AI agent:
Step 1: Define the Agent's Purpose
class TaskAgent:
def __init__(self, name, capabilities, tools):
self.name = name
self.capabilities = capabilities
self.tools = tools
self.memory = {}
self.current_goal = None
def set_goal(self, goal_description):
self.current_goal = Goal(goal_description)
return self.plan_execution()
def plan_execution(self):
# Decompose goal into actionable steps
plan = self.llm.generate_plan(
goal=self.current_goal,
capabilities=self.capabilities,
available_tools=self.tools
)
return self.execute_plan(plan)
Step 2: Implement Tool Integration
class ToolInterface:
def __init__(self):
self.available_tools = {
'web_search': self.web_search,
'send_email': self.send_email,
'file_operations': self.file_ops,
'api_calls': self.make_api_call
}
def execute_tool(self, tool_name, parameters):
if tool_name in self.available_tools:
try:
result = self.available_tools[tool_name](**parameters)
return {'success': True, 'result': result}
except Exception as e:
return {'success': False, 'error': str(e)}
return {'success': False, 'error': 'Tool not found'}
Advanced Implementation Patterns
ReAct Pattern (Reason + Act)
def react_cycle(self, observation):
while not self.goal_achieved():
# Reason about current state
thought = self.llm.reason(
observation=observation,
goal=self.current_goal,
memory=self.memory
)
# Decide on action
action = self.llm.choose_action(
thought=thought,
available_tools=self.tools
)
# Execute action
observation = self.execute_action(action)
# Update memory
self.memory.update({
'thought': thought,
'action': action,
'observation': observation
})
if self.should_replan(observation):
self.replan()
return self.get_final_result()
Multi-Agent Coordination
class AgentOrchestrator:
def __init__(self):
self.agents = {}
self.task_queue = Queue()
self.results = {}
def coordinate_agents(self, complex_goal):
# Decompose into sub-goals
sub_goals = self.decompose_goal(complex_goal)
# Assign to appropriate agents
for sub_goal in sub_goals:
agent = self.select_best_agent(sub_goal)
self.task_queue.put({
'agent_id': agent.id,
'goal': sub_goal,
'dependencies': sub_goal.dependencies
})
# Execute with coordination
return self.execute_coordinated_plan()
Popular Frameworks & Tools
LangChain Agents
- ReAct agents for reasoning and acting
- Tool integration with LangChain tools
- Memory management and conversation history
- Custom agent creation and deployment
AutoGPT & GPT-Engineer
- Autonomous code generation and execution
- File system operations and project management
- Web browsing and research capabilities
- Self-improving and iterative development
Microsoft AutoGen
- Multi-agent conversation frameworks
- Agent specialization and role definition
- Human-in-the-loop interactions
- Group chat and collaboration patterns
CrewAI
- Role-based agent collaboration
- Task orchestration and workflow management
- Agent hierarchy and delegation
- Built-in tools and integrations