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An autonomous agent built with Upsonic’s AutonomousAgent that reads raw shipment data, decides which KPIs matter, writes a structured report, and produces matplotlib charts. The agent runs two tasks in sequence (analyst then visualizer) with all behavior defined in AGENTS.md, not in code.

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Overview

The setup has two parts:
  1. AutonomousAgent with a workspace directory, no custom tools, no system prompt
  2. Two Task objects sent sequentially to the same agent
The agent uses its built-in tools (read/write files, run_python) to explore data, compute metrics, write a report, and generate charts autonomously.

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Project Structure

operations_analyst/
├── main.py                         # Two-task pipeline (~40 lines)
├── requirements.txt                # upsonic, anthropic, matplotlib, pandas
└── workspace/
    ├── AGENTS.md                   # Agent behavior: task definitions, rules, memory
    ├── SOUL.md                     # Agent identity and personality
    ├── shipment_data.csv           # Input data (80 shipment records)
    └── memory/                     # Agent writes session logs here
After running, the workspace will also contain: 
workspace/
├── KPI_REPORT.md                   # Generated report with tables and commentary
├── charts/                         # Generated PNG charts (one per metric)
│   ├── ontime_performance.png
│   ├── carrier_comparison.png
│   └── ...
└── memory/
    └── YYYY-MM-DD.md

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Environment Variables

ANTHROPIC_API_KEY=your-api-key

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Installation

cd examples/autonomous_agents/operations_analyst
uv venv && source .venv/bin/activate
uv pip install -r requirements.txt

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Usage

python main.py
The agent will:
  1. Read the shipment CSV (80 rows of delivery records)
  2. Compute KPIs: on-time rate, carrier performance, route delays, cost efficiency
  3. Write workspace/KPI_REPORT.md with tables, breakdowns, and commentary
  4. Generate one chart per metric and save them to workspace/charts/

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How It Works

ComponentRole
AutonomousAgentReads workspace files, runs Python, manages all logic
Task 1 (Analyst)Reads CSV → computes KPIs → writes KPI_REPORT.md
Task 2 (Visualizer)Reads report → runs matplotlib via run_python → saves PNGs
AGENTS.mdTask definitions, workspace layout, memory rules, safety rules
SOUL.mdAgent identity and analysis style

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Flow

  1. Agent reads AGENTS.md and SOUL.md from the workspace on startup
  2. Task 1: Agent reads shipment_data.csv, decides which metrics matter, computes them, and writes KPI_REPORT.md
  3. Task 2: Agent reads the report, writes matplotlib code, executes it via run_python, and saves charts to charts/
  4. Agent logs the session in memory/

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Complete Implementation

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main.py

"""
Operations Analysis — Upsonic AutonomousAgent (two-task pipeline)

Task 1 (Analyst):    reads shipment_data.csv → decides KPIs → writes KPI_REPORT.md
Task 2 (Visualizer): reads KPI_REPORT.md → runs matplotlib code directly via run_python → produces charts

One agent. Two shots. Fully autonomous.
"""

import os
from upsonic import AutonomousAgent, Task

WORKSPACE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "workspace")

print(f"Workspace: {WORKSPACE}")

agent = AutonomousAgent(
    model="anthropic/claude-sonnet-4-5",
    workspace=WORKSPACE,
)

analyst_task = Task(
    "Read shipment_data.csv. Identify the KPIs that matter most for delivery operations — "
    "on-time rate, carrier performance, route delays, cost efficiency. Compute each from the raw data. "
    "Write KPI_REPORT.md with a summary table, per-carrier breakdown, and a "
    "## Agent Commentary section with your analysis and recommendations."
)

visualizer_task = Task(
    "Read KPI_REPORT.md and shipment_data.csv. Based on the KPIs in the report, use run_python "
    "to execute matplotlib code that creates one chart per key metric. "
    "Use a white background with dark text for readability. Save all charts as PNGs to the charts/ directory. "
    "Do not write a .py file — run the code directly."
)

if __name__ == "__main__":
    print("\n── Task 1: Analyst ────────────────────────────")
    agent.print_do(analyst_task)

    print("\n── Task 2: Visualizer ─────────────────────────")
    agent.print_do(visualizer_task)
No system prompt, no custom tools. The agent reads everything from its workspace and uses run_python to execute generated code.

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Workspace: AGENTS.md

The key to this example. Instead of hardcoding analysis steps in Python, the agent reads its instructions from AGENTS.md:
  • Two-task pipeline: Task 1 analyzes data and writes a report, Task 2 reads the report and produces charts
  • Memory: Daily logs in memory/YYYY-MM-DD.md for continuity between tasks and sessions
  • Workspace layout: Which files are inputs, which are outputs, who owns what
  • Rules: Never delete source data, no exfiltration, trash over rm
  • Tools: Task 2 writes its own visualization script from scratch based on what Task 1 found
Change the task descriptions or edit AGENTS.md to analyze different metrics. The agent adapts without touching code.

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Sample Data

The included shipment_data.csv contains 80 shipment records with:
  • 3 Carriers: FastCargo (reliable), SpeedLine (mixed), EcoShip (cheap but slow on long routes)
  • 8 Destinations: Western cities are fast, eastern cities (Erzurum, Diyarbakir, Trabzon) have delays
  • 4 Categories: Electronics, Furniture, Food & Beverage, Clothing
  • Date range: Jan-Mar 2025
Swap it with your own data to analyze different operations.

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Repository

View the full example: Operations Analyst