Building a Parental Coach AI Agent: Lessons from a Weekend Challenge

Last weekend, I embarked on a challenge that my wife set for me. Since I often speak about AI agents, large language models (LLMs), and generative AI, she tasked me with creating an AI agent that acts as a parental coach. Confidently, I thought I’d nail it in a day—after all, the power of LLMs would do most of the heavy lifting, right? I was wrong. I failed miserably at delivering a finished AI agent by the end of the weekend. But the experience taught me invaluable lessons about the nuances of building AI-powered applications. Here are the key insights from my journey.

1. Amazon Bedrock: A Game-Changer for LLM Access

Amazon Bedrock is a remarkable service. It allows developers to access a variety of LLMs from different providers without the hassle of managing multiple subscriptions, inserting credit card details, or juggling API keys. That said, while Bedrock simplifies access, working with multiple LLMs simultaneously presents its own set of challenges. Each LLM behaves differently, with varying input requirements and output formats. Writing uniform code that accommodates these variations can quickly become tricky.

Key takeaway

Bedrock makes LLM access seamless, but developers still need to build logic to handle the quirks of each model.

2. Skipping Agent Frameworks: Back to Basics

For this project, I deliberately avoided using agent frameworks. I wanted to get my hands dirty, fully understanding the mechanics behind the scenes. This choice helped me see the challenges developers face when building from scratch, especially in terms of architecture and workflow decisions. While frameworks can speed things up, building without one revealed the inner workings of memory management, tool integration, and how LLMs interact with other services.

Key takeaway

Sometimes, skipping frameworks and starting from scratch is the best way to learn the foundational mechanics.

3. AWS Staples Still Reign Supreme

The usual suspects—AWS Lambda, DynamoDB, and S3—remain the backbone of rapid application development. These services proved just as critical for building AI agent applications as they do for traditional cloud applications. When paired with Amazon Bedrock, these services provided a robust environment for testing, scaling, and iterating quickly.

Key takeaway

AWS’s core services continue to be essential building blocks, even in the world of AI agents.

4. The Core Components of an AI Agent

Through trial and error, I identified three fundamental components an AI agent needs:

Memory

The agent must store context and be able to summarize or compact information for long-term use.

Tools

I created a folder of tools that the agent could use uniformly. While tools could be implemented as independent Lambda functions, I found it simpler to build them as classes or functions.

Access to LLMs

The agent requires access to a variety of LLMs to handle diverse tasks.

Building these components independently gave me a better understanding of how they work together and what challenges arise when integrating them.

Key takeaway

Understanding these components is crucial for designing AI agents that work efficiently and reliably.

5. Token Limits: A Persistent Constraint

One of the biggest hurdles was the token limit of LLMs. In this project, the agent needed to generate HTML forms, which can get lengthy. Unfortunately, even the largest models I accessed (supporting up to 8K tokens) struggled with truncation. This limitation forced me to rethink workflows and design more compact outputs.

Key takeaway

Token limits remain a challenge, particularly for text-heavy applications like form generation. Designing around these constraints requires creative problem-solving.

6. AI Agents Also Need to Communicate

One thing I learned during this challenge is that AI agents aren’t just about “thinking” or “doing.” They also need to communicate effectively with users. And this goes far beyond a chatbot interface. AI agents must interact with users across multiple channels to ensure their value is fully realized in real-world applications. I realized that AI agents could very well become some of the biggest users of CPaaS (Communication Platform as a Service) solutions in the coming years. These services allow developers to integrate communication capabilities—like SMS, email, voice, and even video—directly into their applications.

For my parental coach AI agent, I leveraged

Vonage APIs

to send SMS notifications, ensuring users received timely alerts and updates. SendGrid to send emails that included HTML forms and summarized important interactions with the agent. These communication channels enabled the agent to reach users where they were most likely to engage, whether that was a quick text notification or a detailed email containing actionable content. But as I worked on this, I saw the potential for even more sophisticated communication. Imagine agents sending reminders, asking for feedback, or escalating tasks using voice calls or WhatsApp messages. The possibilities are endless, but they also highlight a significant design challenge: ensuring consistent and meaningful communication across channels without overwhelming the user.

Key takeaway

AI agents must excel at multi-channel communication. CPaaS solutions will play a critical role in enabling agents to interact effectively with users, making this an area developers should watch closely in the coming years.

Final Thoughts: You Know What I’m Doing Next Weekend This challenge reminded me how much I enjoy getting my hands dirty with coding and building. There’s something deeply satisfying about solving problems (or at least trying to) and facing the intricate quirks of LLMs head-on. It’s clear that understanding the limitations and intricacies of LLMs is key to harnessing their potential. They’re incredible tools for rapidly building AI workflows, but they come with their share of headaches—like token limits, model behavior quirks, and design trade-offs.

As for next weekend? You bet I’m diving back into this challenge. Because if there’s one thing I love more than building, it’s figuring out how to fix what didn’t work the first time. Stay tuned—I might even nail it this time. (Or at least get closer.)