Reflecting on the last few weeks, here are my core takeaways from the code trenches:
Stochastic vs. Deterministic: Writing code for agents requires a mindset shift. You are essentially managing probabilistic behavior within deterministic control flows. Handling that randomness is an art form.
Tool Use: The moment you see an LLM autonomously decide to query a database or run a Python script to answer a question, the paradigm shifts.
State Management: Standard chatbots are stateless. Agents require memory—short-term for the current task context, and long-term for personalization.
The shift from standard LLMs to Agentic Workflows is massive. We are no longer just "talking" to AI; we are designing systems where AI can plan, execute, and use tools to solve complex problems autonomously.
The biggest unlock? Realizing that an Agent is just an LLM with a loop, a memory, and a toolkit.
Can't wait to ship what I’ve been building. 🤖✨
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