I keep going back and forth on whether Agents are good for the software development discipline.
While I think it's extremely short-sighted that we continue to push full steam ahead on AI automating away jobs, I can't deny that LLMs have given my development flow a decent productivity boost. 80% of the time, my workflow with Cursor looks similar to the golden path depicted in this blog post - outline the changes I want made -> review the code -> suggest edits/iterate -> ship it. There's undoubtedly a class of problems where this feature can slot in and start chipping away immediately.
The other 20% of the time, Cursor will hit a wall and is unable to complete the task through just prompting. It will either introduce a subtle bug in its logic, or come across an error that it incorrectly diagnoses. These stumbles can happen for a variety of reasons:
1. Poorly documented code - the LLM infers the wrong responsibility for a piece of code, or is led astray by old comments
2. Misleading or unhelpful errors from 1st/3rd party libraries
3. Task is too complex - perhaps I asked for more than I should have
In any case, the "self-healing" functionality that Agents rely on to iterate is often insufficient. Prompting for a fix usually just leads me in circles or further down the path of a bad solution. In these instances, I have to drop the coding assistant altogether and do things the old fashioned way - gain a sufficient understanding of the code and figure out where the LLM went wrong (or just write the solution from scratch).
I guess going back to my initial point, it feels like the easy answer is that Agents are good if you're a senior/experienced developer. This means that in the short-term the demand for junior engineers will dry up, since we have Agents to do the rote work, but doesn't this mean that we're effectively choking out the pipeline for experienced devs? Though they're low in complexity/value, the tasks we will handoff to Agents are immeasurably useful for building software fundamentals.
It seems like in 2025 we've suddenly forgotten about "teaching a man to fish"...
While I think it's extremely short-sighted that we continue to push full steam ahead on AI automating away jobs, I can't deny that LLMs have given my development flow a decent productivity boost. 80% of the time, my workflow with Cursor looks similar to the golden path depicted in this blog post - outline the changes I want made -> review the code -> suggest edits/iterate -> ship it. There's undoubtedly a class of problems where this feature can slot in and start chipping away immediately.
The other 20% of the time, Cursor will hit a wall and is unable to complete the task through just prompting. It will either introduce a subtle bug in its logic, or come across an error that it incorrectly diagnoses. These stumbles can happen for a variety of reasons:
In any case, the "self-healing" functionality that Agents rely on to iterate is often insufficient. Prompting for a fix usually just leads me in circles or further down the path of a bad solution. In these instances, I have to drop the coding assistant altogether and do things the old fashioned way - gain a sufficient understanding of the code and figure out where the LLM went wrong (or just write the solution from scratch).I guess going back to my initial point, it feels like the easy answer is that Agents are good if you're a senior/experienced developer. This means that in the short-term the demand for junior engineers will dry up, since we have Agents to do the rote work, but doesn't this mean that we're effectively choking out the pipeline for experienced devs? Though they're low in complexity/value, the tasks we will handoff to Agents are immeasurably useful for building software fundamentals.
It seems like in 2025 we've suddenly forgotten about "teaching a man to fish"...