How AI Agents Can Automate Business Workflows
AI agents are shifting from simple text-generating chatbots to autonomous problem-solvers that execute multi-step workflows. By integrating tools, APIs, and databases, these agents perform tasks previously requiring manual work.
What is an AI Agent?
Unlike traditional software that follows rigid conditional paths (if/else), an AI agent uses a Large Language Model (LLM) as its central engine to make decisions. You provide the agent with a goal, and it dynamically chooses which action to take and which tool to run.
Key Architecture of an Agentic System
An agentic system typically consists of:
1. The Brain (LLM): Handles reasoning, planning, and decision making.
2. Memory: Stores short-term conversation context and long-term database knowledge.
3. Tools: External APIs, database connectors, and custom scripts the agent can call.
# Conceptual tool definition for an AI Agent
class TicketDetailsTool(BaseTool):
name = "get_ticket_details"
description = "Fetches details of a support ticket given its ID."
def _run(self, ticket_id: str):
return db.fetch_ticket(ticket_id)