How to Build an AI Task Planning Agent

The ai task planning agent project management I built handles break goals into executable tasks. I use this across client work where repetitive multi-step processes need to run without constant oversight.

The approach: take a high-level objective, decompose it into tasks with estimates, and generate a sprint plan. One script, one run, results delivered.

What You Need

• Python 3.9+
• Anthropic API key
• Relevant API credentials for the tools your agent uses

Step 1: Define the Agent Tools

import anthropic
import json
import os
from dotenv import load_dotenv

load_dotenv()
client = anthropic.Anthropic()

tools = [
    {
        "name": "decompose",
        "description": "Primary tool for the agent's core function",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Input for the tool"}
            },
            "required": ["query"]
        }
    },
    {
        "name": "estimate",
        "description": "Secondary tool for processing or storing results",
        "input_schema": {
            "type": "object",
            "properties": {
                "data": {"type": "string", "description": "Data to process"}
            },
            "required": ["data"]
        }
    }
]

Step 2: Implement Tool Functions

def execute_tool(tool_name, tool_input):
    if tool_name == "decompose":
        return handle_decompose(tool_input)
    elif tool_name == "estimate":
        return handle_estimate(tool_input)
    return "Unknown tool"

def handle_decompose(input_data):
    # Your implementation here
    query = input_data.get("query", "")
    print(f"Running decompose: {query}")
    return f"Results for: {query}"

def handle_estimate(input_data):
    data = input_data.get("data", "")
    print(f"Processing: {data[:100]}")
    return "Processed successfully"

Step 3: Build the Agent Loop

def run_agent(task, max_steps=10):
    messages = [{"role": "user", "content": task}]

    for step in range(max_steps):
        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=4096,
            system="You are an autonomous task planning agent. Use the available tools to complete the task. Think step by step. Be thorough.",
            tools=tools,
            messages=messages
        )

        # Check if agent is done
        if response.stop_reason == "end_turn":
            final = next((b.text for b in response.content if b.type == "text"), "")
            print(f"Agent completed in {step + 1} steps")
            return final

        # Process tool calls
        messages.append({"role": "assistant", "content": response.content})
        tool_results = []
        for block in response.content:
            if block.type == "tool_use":
                result = execute_tool(block.name, block.input)
                tool_results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": str(result)
                })
        messages.append({"role": "user", "content": tool_results})

    return "Max steps reached"

Step 4: Run and Log Results

import sqlite3
from datetime import datetime

def log_agent_run(task, result):
    conn = sqlite3.connect("agent_runs.db")
    conn.execute("""CREATE TABLE IF NOT EXISTS runs (
        task TEXT, result TEXT, ran_at TEXT
    )""")
    conn.execute("INSERT INTO runs VALUES (?, ?, ?)",
        (task, result[:5000], datetime.now().isoformat()))
    conn.commit()

task = "Analyze our top competitors and create a summary report"
result = run_agent(task)
log_agent_run(task, result)
print(result)

What to Build Next

Add error recovery so the agent retries failed tool calls with adjusted parameters. Then add a cost tracker that monitors API token usage per agent run so you can optimize which model handles which steps.

Related Reading

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• Building an AI-Powered Calendar Management System - keeping schedules in sync without the back and forth