AI Agent on Kaggle
Source: Dev.to
Introduction: Stepping into the World of AI Agents
When I first embarked on the AI Agents Intensive program, I realized that agents were not just theoretical concepts but powerful frameworks capable of solving real‑world problems.
For our Capstone Project, we chose to build a Smart Content Generator Agent whose primary objective was to reduce the time spent on creating compelling advertising copy for social media. This article details the challenges I faced in designing the agent—such as structuring the prompt and dealing with performance issues—and the lessons learned in guiding an agent to produce accurate, high‑quality content.
Agent Architecture and Tools
- Agent’s Goal: Analyze the provided product features and desired tone, and instantly generate an engaging social media post.
- Engine (LLM): Google’s Gemini‑2.5‑Flash, chosen for its high speed and efficiency, ideal for a short‑duration hackathon project.
- Tools: No external tools (e.g., web search, calculator) were integrated. All necessary information for the ad copy was supplied via the prompt, which significantly improved latency.
Challenges and Solutions
| Challenge | Solution |
|---|---|
| Output Consistency – The agent often defaulted to a generic voice instead of the desired “Humorous” or “Formal” tone. | System Prompt Refinement – Provided a clearer system prompt, defining the agent as a “High‑level Advertising Consultant.” |
| Input Control – The agent sometimes omitted critical input, such as the product name or key features, in the final copy. | Forced Formatting – Mandated in the prompt that “the output must contain the product name and all three specified features.” |
| Performance (Latency) – Larger models caused wait times exceeding 15 seconds per output. | Model Selection – Switched to Gemini‑2.5‑Flash, balancing speed and quality, reducing time‑to‑output to under 5 seconds. |
Future Vision
- Multimodality: Extend the agent to generate corresponding images using models like Gemini 2.5 Pro, enabling complete text‑and‑image social media posts.
- Memory: Add a session‑memory feature so the agent can recall brand guidelines after learning them once, ensuring consistent tone and style across multiple outputs.
- Self‑Improvement: Develop a feedback loop that automatically adjusts the agent’s prompt based on user feedback, allowing continuous improvement of output quality.
Conclusion and Acknowledgements
The AI Agents Intensive Capstone Project was more than a competition; it was an invaluable lesson in understanding and deploying generative AI agents. Despite the challenges, we successfully harnessed Gemini to create an efficient and accurate AI copywriter, boosting confidence to build larger, more complex multi‑tool agents in the future.
I extend heartfelt gratitude to the Kaggle team and the Google mentors for their guidance during this five‑day intensive. This experience marks a significant turning point in my AI journey.