How to Build Your First Self-Running AI Agent in 15 Minutes
Article summary
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Most engineering workflows treat LLMs as stateless calculators: input a prompt, copy the snippet, kill the tab. The session ends, the context evaporates, and the model's iterative reasoning capacity — arguably its most powerful feature — goes completely untapped. That's a waste. The same model you use for one-off answers can be restructured — with nothing more than a well-designed prompt — into an agent that decomposes a goal, works through it step by step, evaluates its own output, and…
1Key Takeaways
- Most engineering workflows treat LLMs as stateless calculators: input a prompt, copy the snippet, kill the tab.
- The session ends, the context evaporates, and the model's iterative reasoning capacity — arguably its most powerful feature — goes completely untapped.
- The same model you use for one-off answers can be restructured — with nothing more than a well-designed prompt — into an agent that decomposes a goal, works through it step by step, evaluates its own output, and….
2AIWedia Score
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that most engineering workflows treat LLMs as stateless calculators: input a prompt, copy the snippet, kill the tab.
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