Executive Summary
This post documents my capstone project from the AI Agents Intensive by Google and Kaggle. I designed an autonomous agent system for accessibility mapping, with a focus on agent architecture, reasoning and planning loops, tool usage, and evaluation strategies. The project demonstrates applied experience in building agentic AI systems rather than isolated model experiments.
🔗 Kaggle Notebook: Access All Areas: Agentic Accessibility Mapping
Introduction
I recently completed the AI Agents Intensive Capstone Project on Kaggle titled Access All Areas: Agentic Accessibility Mapping. This project marked an important milestone in my learning journey, where I moved beyond …
Executive Summary
This post documents my capstone project from the AI Agents Intensive by Google and Kaggle. I designed an autonomous agent system for accessibility mapping, with a focus on agent architecture, reasoning and planning loops, tool usage, and evaluation strategies. The project demonstrates applied experience in building agentic AI systems rather than isolated model experiments.
🔗 Kaggle Notebook: Access All Areas: Agentic Accessibility Mapping
Introduction
I recently completed the AI Agents Intensive Capstone Project on Kaggle titled Access All Areas: Agentic Accessibility Mapping. This project marked an important milestone in my learning journey, where I moved beyond experimenting with language models and focused on building an autonomous agent system that can reason, plan, and act to solve a real world problem.
The capstone is part of the five day AI Agents Intensive program by Google and Kaggle. It brings together concepts such as agent architectures, tool usage, memory, and evaluation into a single applied project. Instead of focusing only on model performance, the emphasis is on designing systems that behave intelligently over multiple steps.
Why This Project Matters
Most traditional AI applications are reactive. They respond to prompts and return outputs. Agentic AI is different. It introduces autonomy. An agent can break down a task, decide what to do next, use tools, evaluate results, and iterate until a goal is achieved.
The Access All Areas project reflects this shift clearly. The challenge was not simply to analyze data, but to design an agent that could explore information, infer accessibility related features, and map them in a structured and repeatable way. This made the project feel much closer to real world AI systems than typical notebooks or tutorials.
From Course Modules to Capstone Execution
The five day intensive covered several core ideas that directly influenced my approach to the capstone:
- Foundations of agent design and reasoning loops
- Tool calling and external system integration
- Context management and memory
- Evaluation strategies for agent behavior
- Moving from prototype ideas to production ready thinking
Each concept was reinforced through hands on labs and example notebooks. By the time I started the capstone, the focus shifted from learning individual techniques to combining them into a coherent system.
Project Overview: Agentic Accessibility Mapping
Problem Framing
Accessibility data is often fragmented, incomplete, or inconsistently represented. Rather than treating this as a static classification problem, I framed it as a goal driven agent task. The agent’s objective was to explore the available data, identify signals related to accessibility, reason about their relevance, and generate a structured mapping.
This framing allowed the agent to adapt when information was missing or ambiguous instead of failing outright.
Agent Architecture
The system was designed as a multi stage workflow:
- A perception step to interpret raw inputs
- A reasoning and planning step to decide next actions
- An action step where the agent used tools or logic to validate and record results
This separation helped keep the system modular and made it easier to debug and improve individual components.
Evaluation and Reliability
One of the most important lessons from the course was that agent outputs must be evaluated, not just generated. I incorporated basic validation checks and confidence based logic to ensure that the agent’s outputs were consistent and interpretable over multiple runs.
Challenges and Key Learnings
Several challenges emerged during development:
- Handling noisy or incomplete signals required careful reasoning and fallback logic
- Coordinating tool usage without unnecessary calls took iteration
- Designing evaluation criteria for agent behavior was more complex than traditional accuracy metrics
These challenges reinforced an important insight: building agents is as much about system design as it is about prompting or modeling.
Project Walkthrough Video
To complement this blog, I created a short explainer video using NotebookLM. The video walks through the motivation behind the project, the overall agent architecture, and key implementation decisions from the Kaggle notebook.
It is intended as a high-level narrative overview and pairs well with the technical details discussed here.
What This Project Taught Me
This capstone helped me shift how I think about AI development:
- Agents are systems, not single model calls
- Evaluation and observability are essential for trust
- Tool integration significantly increases real world usefulness
Most importantly, it gave me confidence in designing agentic workflows that can adapt and reason rather than simply respond.
What’s Next
Building on this experience, I plan to explore:
- More complex multi modal agents
- Richer evaluation techniques for agent behavior
- Real world applications in accessibility and urban data systems
Closing Thoughts
The Access All Areas capstone was more than a final assignment. It was a practical exercise in designing autonomous AI systems with intent and structure. It represents a meaningful step in my transition from learning concepts to building agentic solutions that can operate in realistic environments.
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