A Clear, Practical Path Based on Real Experience
Forget about building a "general agent" right now. Decide on one specific job you want the agent to do.
Don't waste time training your own model. Use GPT, Claude, Gemini, or open-source options like LLaMA and Mistral.
An agent isn't just a chatbot—it needs tools. Decide what APIs or actions it can use to interact with the world.
Start by wiring the basics: user input → model → tool execution → result → model. This loop is the heartbeat of every agent.
Start with short-term context only. Use databases or JSON files for cross-run memory. Only add vector databases when needed.
CLI is fine at first, but eventually give it a simple interface: web dashboard, Slack bot, or local script.
Run real tasks, see where it breaks, patch it, run again. Every reliable agent goes through dozens of cycles.
Resist adding more tools and features. A single well-functioning agent is worth more than a failing "universal agent."
Book a doctor's appointment from a hospital website
Monitor job boards and send you matching jobs
Summarize unread emails in your inbox
Post scheduled content to multiple platforms
This loop — model → tool → result → model — is the core of every AI agent.
The smaller and clearer the problem, the easier to design and debug.
Leverage proven LLMs instead of training your own from scratch.
Agents need real-world interaction capabilities to be useful.
Build, test, break, fix, repeat until it works reliably.
Build one specific agent, end-to-end. Once you've done that, making the next one becomes ten times easier because you already understand the full pipeline.