AI Agent Orchestration: Building Autonomous Workflows

What is an AI Agent?

While standard LLM applications are reactive (answering user questions), AI Agents are active. An agent is given a high-level goal, access to a set of tools, and the autonomy to plan, execute, and verify its own actions to accomplish the goal.

The Agent Loop: Plan, Act, Observe

AI agents operate in a continuous loop:

1. Plan: The agent decides what action to take next based on the user’s prompt.
2. Act: The agent invokes a tool (e.g., executing a SQL query or calling a web search API).
3. Observe: The agent reviews the tool’s output and determines if the task is complete. If not, it plans the next step.

# Simple agent tool definition using Python
class WebSearchTool(BaseTool):
    name = "web_search"
    description = "Searches the web for current information."
    
    def _run(self, query: str) -> str:
        return search_search_engine(query)