DEV Community: Leonhard Satria Suharjo

Making my first Ontology with Palantir Foundry

Leonhard Satria Suharjo — Sat, 16 May 2026 10:23:50 +0000

The role of Object and Ontology in Data Engineering

This article contains references from the official “Deep Dive: Creating Your First Ontology” course by Palantir Learn — Skilljar, alongside my personal takeaways.

Introduction

In the past few days, I have been revisiting the Data Engineering concept through the Foundry Course provided by Palantir. Back in April 2025, I had the chance to participate in some of the courses involving some of the other Foundry’s tools. Therefore, this ontology course not only resumes my previously halted learning progress in data engineering, but also introduces me to various new concepts.

This article will discuss the key points and definitions I learn from this course, how I apply them in the course, and what I think of the course learning model.

Read the full article on Medium: https://medium.com/stackademic/making-my-first-ontology-with-palantir-foundry-c4f9a06af6b3

IBM Bob — What I tried making with it

Leonhard Satria Suharjo — Sat, 16 May 2026 10:18:06 +0000

Hours of clicking “Approve” to build an AI Scheduling Assistant

This article is part of an ongoing series on personal software projects and things I learn along the way

Introduction

A couple days ago, I encountered an advertisement on a social media about a hackathon from lablab.ai utilizing IBM’s new AI coding agent, called the IBM Bob. The format was pretty straightforward, within 48 hours, I would need to build something with IBM Bob as my development partner and submit it. With no experience of knowing or using IBM Bob before, I was intrigued by the chance to try this new tool through this hackathon.

This article will document what I end up making with the tool, how IBM Bob works in practice and what it actually feels like to ship a project almost entirely through AI generated code, including what went well and what I will do improvise next time.

Read the full article on Medium: https://medium.com/@leonhard.suharjo/ibm-bob-what-i-tried-making-with-it-bd3d7e670d87

Why Your Laptop Lags at Times — Context Switching

Leonhard Satria Suharjo — Wed, 31 Dec 2025 00:47:50 +0000

An Operating Systems Concept

~ One last article to close 2025 :)

Recently, I’ve been learning a new subject at my uni (University of Messina), operating systems. As I dive deeper into the subject, I noticed that there are lots of OS concepts that I have been implementing in my very own laptop. While the subject is fresh in my mind, I’ll be sharing a topic I found interesting, called the context switching.

Read the full article here: https://medium.com/@leonhard.suharjo/why-your-laptop-lags-at-times-context-switching-b572bdb0a83e

CANDI - Cloud Defense Intelligence Platform

Leonhard Satria Suharjo — Mon, 08 Dec 2025 15:51:31 +0000

AI-powered military logistics and security command center built with Google Cloud & Gemini 3.

Military logistics represents one of the world's most complex supply chain systems. The U.S. Department of Defense alone loses $10+ billion annually to supply chain inefficiencies. This app demonstrates how Google Cloud's advanced capabilities can transform this critical sector.

Cloud Defense Analytics showcases three key insights from my Google Cloud Digital Leader certification journey:

DIGITAL TRANSFORMATION: We consolidated siloed legacy systems into a unified cloud data warehouse, reducing waste from 40% to 15% through real-time visibility and automated insights.
PREDICTIVE AI: Using Gemini 3's multimodal reasoning, the app analyzes logistics documents and photos to predict supply chain failures before they impact operations—mimicking how Google Cloud's Vertex AI helps organizations make data-driven decisions at scale.
ZERO TRUST SECURITY: Implementing strict IAM controls and encryption ensures only authorized personnel access sensitive military data, protecting national security while maintaining operational agility.

Why This Matters:
Modern defense organizations need technology that matches geopolitical complexity. This app proves that Gemini 3's advanced reasoning and
multimodality can solve problems that were impossible to tackle with traditional tools.

The application is built entirely using vibe coding principles in Google AI Studio, demonstrating how non-technical military planners can rapidly prototype solutions that bridge the gap between strategy and technology.

Skills Demonstrated:

Cloud Architecture (Digital Transformation principles)
AI/ML Integration (Predictive analytics)
Security Implementation (Zero Trust, IAM)
DevOps & Reliability (Real-time monitoring)

Project links

Video presentation = https://www.youtube.com/watch?v=Ah2WVvFbMrc
AI studio app preview: https://ai.studio/apps/drive/1nkWoz4ZLT93ztUmHlw4kuNMNaiZi0Uq8?fullscreenApplet=true

*Image Gallery: *

My article on the Google - Kaggle AI agents 2025, Feel free to leave a like or comment!

Leonhard Satria Suharjo — Sat, 06 Dec 2025 12:41:06 +0000

Leonhard Satria Suharjo

Dec 6 '25

How I Connected AI Agents with Manufacturing Supply Chain

#googleaichallenge #ai #python #kaggle

Comments

5 min read

How I Connected AI Agents with Manufacturing Supply Chain

Leonhard Satria Suharjo — Sat, 06 Dec 2025 12:40:01 +0000

Building SuplaiCerdas: From Google-Kaggle’s AI Agents Course to Real-World Supply Chain Automation

*Project view on Github @leonhardsuharjo

Weeks ago, I had the opportunity to participate in the AI Agents 5-day Intensive Course held by Kaggle and Google. The course consisted of daily notebook exercises, video podcasts, and livestreams that systematically introduced me to the fundamentals of AI agents — from basic concepts to advanced topics like agent tools, multi-agent architectures, and quality evaluation.

This article serves as a part of my capstone project submission: SuplaiCerdas, a prototype AI agent application designed to automate material cost and risk assessment for manufacturing projects.

Further details can be found at my github profile @leonhardsuharjo

My Project in 34 Words

An enterprise AI agent system that monitors component prices across suppliers for IoT manufacturing companies. The system helps maintain profit margins on tender commitments by detecting price spikes before they impact production costs.

Background issue — The Manufacturing Business Challenge

As someone deeply interested in the device and manufacturing business model, I’ve observed a critical challenge that many companies face during project execution.

Manufacturing projects often involve months-long processes with multiple phases: tender submission, designing, testing, evaluation, and mass production. Here’s the catch — companies must commit to specific prices during the tender phase, which happens months before actual project execution.

The risk
Suppose a scenario where a business submits a tender for smart TV controllers at a price of 5000 USD each and win the bid. However, months later when production actually started, the price of the smart TV’s critical component, suppose its microcontroller has spiked twice in price due to various reasons. Therefore, this will bring disruption to the company’s initial budget that was submitted during the bid, resulting in margin loss.

SuplaiCerdas is created to address this risk.

*Sample material price

What I Learned (and Applied) from the Course

*Course main page

Before diving into the project, I completed three key notebooks from the 5-day course that directly shaped my approach:

Day 1B: Multi-Agent Systems & Workflow Patterns

*Kaggle notebook accessible at my profile @leonhardsuharjo

This notebook introduced me to multi-agent architectures — the idea of building specialized agents that work together like a team. The key concept I applied from this notebook is Sequential Workflow Pattern, which is breaking down tasks into a pipeline of different agents performing sequential tasks.

For SuplaiCerdas, this meant:

PriceCheckAgent retrieves current prices from all suppliers
ComparisonAgent calculates price changes and assigns risk levels
RecommendationAgent synthesizes findings into actionable procurement advice

Day 2A: Building Custom Function Tools

*Kaggle notebook accessible at my profile @leonhardsuharjo

This notebook taught me that agents become powerful when they can use tools to interact with the outside world. The key concept I applied in this notebook is Custom Function Tools, which is turning python functions into agent tool using ADK (Agent Development Kit).

For SuplaiCerdas, I built two custom tools:

check_component_prices(): Connects to supplier database and retrieves current prices
calculate_price_change(): Calculates percentage change from historical data and flags risk levels (HIGH >10%, MEDIUM 5–10%, LOW <5%)

Day 4B: Agent Evaluation

*Kaggle notebook accessible at my profile @leonhardsuharjo

This notebook emphasized that agents are non-deterministic — they don’t always behave the same way, so systematic testing is critical in order to know the system is ready for initial deployment.

For SuplaiCerdas, I wrote three test cases:

Test 1: Price check tool retrieves data from all 3 suppliers
Test 2: Price change calculation detects 17.24% spike as HIGH risk
Test 3: Complete workflow executes end-to-end without errors

Building SuplaiCerdas: The Three-Agent System

Suplaicerdas is comprised of three main agents, as the following:

Agent 1: PriceCheckAgent

Role: Gather Data
Tool: check_component_prices(component_code)
What it does: Queries a simulated supplier database for current prices across 3 suppliers
Sample output:
Supplier_A_Electronics: $5.80
Supplier_B_Components: $6.00
Supplier_C_Global: $5.70

*Actual python code

Agent 2: ComparisonAgent

Role: Risk Analyst
Tool: calculate_price_change(current_price, last_month_price)
What it does: Calculates percentage change and assigns risk levels
High if price increase >10%
Medium if 5–10% price increase
Low if <5% increase
Sample output:
Supplier A: 5.45% increase → MEDIUM risk
Supplier B: 3.45% increase → LOW risk
Supplier C: 0.88% increase → LOW risk

*Actual python code

Agent 3: RecommendationAgent

Role: Procurement Advisor
Tool: Pure LLM reasoning (no external tools)
What it does: Synthesizes price + risk data into a clear, actionable recommendation
Sample output: ‘ RECOMMENDATION: Supplier_C_Global CURRENT PRICE: $5.70 (lowest) RISK LEVEL: LOW (0.88% increase) RATIONALE: Supplier_C_Global offers the best combination of competitive pricing and price stability. Supplier_A_Electronics shows a moderate price increase (5.45%), flagging a MEDIUM risk that could impact your tender margins. ‘

*Actual python code

Testing the solution prototype

Below are the running procedures as well as screenshots of the product:

Run agent test python file, proceed after all test passed.

Run main.py python file and enter component code to be tracked.

After entering desired component code, system will do an automated assessment and return a result of recommendations.

What’s next

As I have achieved a working prototype with a simulated supplier data, there are several directions I can take to further improve this app’s functionality, including the following:

Real Data Integration: Use actual supplier APIs
Currency Conversion: Support different currencies (USD/EUR/IDR)
Automation: Schedule daily price checks with alerts to user for certain conditions like high risk components

Conclusion

Participating in this AI agents course and working on the project has taught me more than just theories. It has taught me how I could implement AI agents into real-life cases, which I believe is crucial during the recent AI developments.

I am excited for what’s ahead.

Thanks for taking the time to read this article :), feel free to leave a comment.

Github project repo link: https://github.com/leonhardsuharjo/SuplaiCerdas_GoogleKaggle-Capstone_AI-Agents_LeonhardSatriaSuharjo

-- written by Leonhard Satria Suharjo --