Finally Got Small Company Running with 100% AI Agents : Part 3

Full Article

● First with the Confession
I literarily burned myself in last 4 days to get this simple startup running completely using AI Agents, learned a lot in process, made a lot of mistakes, and finally got it working, yes, all Autonomous Agency !

○ Key lessons learned:
◐ Different language models (LLMs) are needed for different AI agents.
◐ A combination of remote and local LLMs is optimal.
◐ The backstory and task descriptions for AI agents are crucial.
◐ Identifying the appropriate LLM for each AI agent is a vital skill.

● Lets learn our components
● Our Startup Database
○ A robust database of potential candidates was created with the help of AI. ○ This database serves as a talent pool for simulations and future hiring decisions.
○ Each entry represents a potential team member with their qualifications, experience, and skills.

● .env setup and Modelfile
○ The .env file is used to configure the API keys for the language models.
○ The Modelfile allows customization of the local language model's behavior and setti[ngs.

●](http://agents.py/) Agents.py
○ The RecruitmentAgents class creates specialized AI agents for different recruitment tasks.
○ Agents include Job Hunter, Resume Analyst, Candidate Engagement Specialist, Company Investigator, and Workflow Orchestrator.
○ Each agent has a specific role, goal, backstory, tools, and language mod[el.

● custom_](http://custom_tools.py/)tools.py
○ The JobScrapeQueryRun class is a tool for scraping job listings from Google Jobs using the SerpApi service.
○ It can extract data for individual job listings or search for multiple job listings based on a query.

● tasks.py
○ The RecruitmentTasks class defines the key steps involved in the AI-powered recruitment process.
○ Tasks include job search, resume analysis, candidate outreach, company research, and final matching.
○ Each task has a description, instructions for the responsible agent, and the expected output.

● main.py
○ This class orchestrates the simulated recruitment process using AI agents and tasks.
○ It generates dummy resumes, creates agents and tasks, forms a crew, and executes the recruitment workflow.
○ The final results showcase successful placements of candidates in suitable roles and companies.

● Setup and Action
○ The author (AI Agent?) shares their journey of setting up the codebase and running the recruitment simulation.
○ The AI agents collaborate to find job openings, analyze resumes, engage candidates, research companies, and make final matches.

The article provides a detailed walkthrough of building a business using AI agents, covering the various components, challenges, and the final successful implementation.