Simple Agent Architecture

# Simple agent architecture
import json

class SimpleAgent:
    def __init__(self, goal, tools, max_steps=10):
        self.goal = goal
        self.tools = tools  # Dict of tool_name -> tool_function
        self.max_steps = max_steps
        self.state = {
            "goal": goal,
            "history": [],
            "step": 0,
            "status": "running"
        }

    def decide(self):
        """
        In a real agent, this calls a language model.
        Here we use a simplified mock decision.
        """
        # The controller reads the goal and history, returns the next action
        step = self.state["step"]
        if step == 0:
            return {"action": "web_search", "args": {"query": self.goal}}
        elif step == 1:
            return {"action": "summarise", "args": {"text": self.state["history"][0]["observation"]}}
        else:
            return {"action": "FINISH", "result": "Task complete"}

    def run(self):
        while self.state["step"] < self.max_steps:
            decision = self.decide()

            if decision["action"] == "FINISH":
                self.state["status"] = "complete"
                print("Goal complete. Result:", decision.get("result"))
                break

            # Execute tool
            tool_fn = self.tools[decision["action"]]
            result = tool_fn(**decision["args"])

            # Record observation
            self.state["history"].append({
                "step": self.state["step"],
                "action": decision["action"],
                "observation": result
            })
            self.state["step"] += 1
            print(f"Step {self.state['step']}: {decision['action']} complete")

        return self.state