Smart Cover Letters Made Stupid Simple

This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)

What I Built

For the Agent.ai Challenge, I built an intelligent, personalized Cover Letter Writer that streamlines the job application process by generating tailored cover letters based on the user’s LinkedIn profile and a job posting. This agent leverages multiple advanced features of Agent.ai, including invoking python utilities via serverless functions, invoking another agent, and custom prompts to deliver high-quality, user-focused results.

Key Features and Workflow

1. Inputs:

• LinkedIn Profile URL: The agent accepts the user’s LinkedIn URL to parse and extract the username. Using Agent.ai’s “LinkedIn Profile” action, the agent fetches the user’s public profile data, such as skills, work history, and accomplishments. Anecdotally: I opted for asking for the full URL rather than just the username after testing this with 15 friends; the majority provided the full LinkedIn URL or didn't know what "LinkedIn username" meant when asked.

A serverless function processes the URL to extract the username seamlessly. (Note: In the future I could imagine a world where a string parse action is available that let's you use regex for a simpler approach to this).

• Job Posting URL: The agent can process any publicly available job posting. It fetches the page content and sends it to a secondary Agent.ai Agent ("Job Post Summarizer"), which parses and summarizes the posting into an XML format. This modular design ensures that the job summarization logic is isolated, making it easier to improve independently of the cover letter writing logic.

• Output Tone: The user can specify the tone of the cover letter, such as formal, friendly, or academic, allowing for customization aligned with the applicant's personality, and the job and company culture.

• Additional Notes: An optional input field allows users to provide specific instructions to the agent, such as emphasizing a particular skill or experience not prominently featured on their LinkedIn profile. This flexibility ensures the final output aligns with the user's goals.

2. Core Functionality:
After collecting the inputs, the agent integrates the parsed job posting in a structured format and LinkedIn profile data into a tailored writing prompt for the AI. The prompt ensures that the resulting cover letter:

• Highlights the user’s key skills and achievements relevant to the job description.
• Addresses specific company and role details.
• Includes placeholders for missing personal details (e.g., phone number, email), which users can fill in manually.

Advanced Features Used

1. Invoking Python Utilities via Serverless Functions: Serverless functions are used to parse the LinkedIn URL into the username, streamlining the input process for users.
2. Agent.ai Actions: Leveraged the “LinkedIn Profile” action to dynamically pull user data.
3. Process Invocations: Integrated a secondary agent for job post parsing, maintaining a modular design for scalability and future enhancements.
4. Custom Prompts: The AI’s writing prompt dynamically adapts based on user inputs, ensuring personalized, high-quality outputs.

Results

This agent simplifies a common but time-consuming task by automating the creation of professional and personalized cover letters. It not only saves time but also ensures the letters are targeted and effective, helping users stand out in competitive job markets.

By integrating advanced Agent.ai capabilities, this solution showcases the potential of AI to enhance productivity and deliver meaningful, user-centered tools.

Demo

Try the Cover Letter Writer for yourself!

Agent.ai Experience

My experience with Agent.ai was both rewarding and insightful. Here's a breakdown of my journey:

What I Loved

Agent.ai’s interface is clean, intuitive, and well-suited for building dynamic, AI Agents. I particularly enjoyed the ability to use tools like serverless functions and secondary agent invocations to create flexible and modular workflows. The platform's ability to dynamically pull data (e.g., LinkedIn profiles) and leverage processes like custom prompt generation made building my Cover Letter Writer feel both creative and productive.

The modular design philosophy stood out to me. By breaking down tasks into smaller agents (e.g., a separate "Job Post Summarizer"), I could build a scalable system that balances simplicity and functionality. This approach aligned well with the flexibility of agentic systems, where the agent takes control over how tasks are completed.

This allowed me to systematically break down the entire task of writing a well-researched Cover Letter, then add the necessary actions for each step.

Challenges I Encountered

As with many early-stage tools, there were some rough edges. Specifically:

• Documentation Gaps: Certain features, like executing serverless functions, accessing agent variables, and defining output variables, lacked detailed documentation. This added some trial-and-error to the process.
• Debugging Serverless Functions: While the ability to run Python utilities via serverless functions is incredibly powerful, the process for debugging and understanding the execution environment required some reverse engineering. I ended up creating test agents and carefully analyzing outputs to better understand the system's capabilities and constraints.

How I Worked Through the Challenges

Despite these hurdles, I found the problem-solving process to be a ton of fun! By experimenting with the platform and analyzing the environment, I gained a deeper understanding of how Agent.ai operates. This hands-on exploration was invaluable, and I appreciated how the platform’s design allowed me to iterate quickly.

What Could Be Improved

To enhance the developer experience, I’d love to see:

1. Expanded Documentation: More guides and examples for advanced features like serverless functions, variable handling, and output definitions would save time and lower the barrier to entry for new users.
2. Debugging Tools: Built-in tools for debugging serverless functions or visualizing the agent’s environment during execution would be a game-changer for efficiency. The existing debug tab is great, iterating on this and making it more powerful would be awesome! Specifically if we could "re-run" agents from a specific step with saved inputs would make debugging and iterating even faster.

Final Thoughts

Overall, Agent.ai is an awesome platform with a lot of potential for building innovative, agent-driven utilities. While there’s room for improvement in terms of documentation and developer support, the core functionality is robust and well designed, and the process of building with Agent.ai is both fun and empowering.