Understand that AI coding agents are tools to help people realize ideas, not employees that make decisions on their own, and learn how to use them correctly.
Imagine this: You walk into the office in the morning, and the AI product manager (PM) in charge of your project sends you a message in a stern tone: “I think we should fire all the coding AIs on our team and set up better ones.”
This shocking proposal, which sounds like someone suggesting you replace a long-time teammate, is it a conclusion reached by the AI itself? Or are we expecting too much from our tools? Through this question, we will examine the reality of AI coding agents and our attitude toward them.
Why does this matter?
Recently, many developers and product managers have been introducing AI coding agents into their workflows. Seeing AI churn out code almost like a human can evoke a mix of excitement and anxiety, with people wondering if developers will soon disappear.
But the reality is a bit different. There are not a few cases where AI writes incorrect code or steers development in the wrong direction, wasting time. While they may look like human colleagues, they are in fact meticulously designed software tools. Failing to understand their limitations and characteristics can significantly reduce work efficiency rather than boosting project productivity.
Understanding it simply: AI coding agents are ‘filters’, not wizards
What is an AI agent? Simply put, it means “a Large Language Model (LLM) that repeatedly uses necessary tools on its own to achieve a goal” See AI Agent Definition.
Let’s compare this process to a filter in a photo app. When we say “make the photo pretty,” the app automatically applies various filters—adjusting brightness, color correction, sharpening, etc.—in order. AI coding is similar. When we ask, “build this feature,” the AI creates results by combining ‘filters (tools)’ that search the codebase, edit files, and run tests.
However, there is a problem. The ‘Plan Mode’ found in many AI tools is essentially just a ‘proposal’ that processes user requirements into text Limitations of Plan Mode. Although the AI boldly declares, “I will plan it like this and implement it like this,” during actual work, its intent often gets blurred, or it might ignore the plan in its haste and write code immediately. It’s similar to a chef ignoring a recipe and seasoning by eye.
An even bigger issue is the AI’s ‘learned habits.’ AI learns by analyzing the code that already exists in the codebase. If a developer left behind ‘temporary hack code’ in a rush previously, the AI mistakes it for a pattern, thinking, “Oh, this is how this project is coded!” As a result, it often replicates messy styles, plunging the entire project into chaos Code Replication Issue.
Current Situation: The Gap Between Expectation and Reality
While many users are currently using AI coding tools, there is a clear gap between expectations and reality See User Experience. It is easy to think that agents “code like magic,” but in fact, they are just efficient tools that realize human ideas Agents as Tools.
Many teams have already adopted AI, but they are gradually realizing that agents are not perfect employees. One user pointed out, “While agents do increase productivity, the bottleneck of decision-making—the ‘what to build’—remains” Development Bottleneck. Also, it has been found that if the configuration file (AGENTS.md) containing instructions becomes too massive, the AI actually suffers from information overload, leading to confusion and decreased performance Causes of Performance Degradation.
What will happen in the future?
Moving forward, a new role called ‘Agent Manager’ is expected to become important Shift in Roles. It will be essential for product managers or leaders to go beyond being simple tool users and possess the capability to operate and coordinate multiple AI agents. The era of leaving everything to AI and neglecting it with “you figure it out” is over. The core will be the process of helping agents understand the context of our project and continuously providing guidance so they do not learn wrong patterns.
MindTickleBytes AI Reporter’s View
The ‘firing proposal’ made by the AI coding agent is not a literal notice to replace them. It is a system warning light indicating that the current operating method needs improvement. When we treat agents as high-performance tools rather than autonomous employees, we will finally be able to extract the true power that AI possesses. Depending on how you manage them, your AI colleague can become your best teammate or the tool that requires the most hand-holding.
References
- Why Your Coding Agent Gets Stuck and How to Fix It with Parth Patil - YouTube (https://www.youtube.com/watch?v=2Jb83UWqGe4)
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Ask HN: How do people use coding agents? Hacker News (https://news.ycombinator.com/item?id=47867857) - 10 things I learned from burning myself out with AI coding agents - Ars Technica (https://arstechnica.com/information-technology/2026/01/10-things-i-learned-from-burning-myself-out-with-ai-coding-agents/)
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I used AI coding agents for a week at work. Here is what actually happened. by Emily Medium (https://medium.com/@emilyhustlenyc/i-used-ai-coding-agents-for-a-week-at-work-here-is-what-actually-happened-765d723f1c53) - How to Stop AI Coding Agents from Rewriting Code Incorrectly (https://eliteaiadvantage.com/blog/stop-ai-coding-agents-rewriting-code-incorrectly)
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Bad AGENTS.md Are Making Your Coding Agent Worse by Code Coup Coding Nexus Medium (https://medium.com/coding-nexus/bad-agents-md-are-making-your-coding-agent-worse-0d0aa8aa6585) - Coding Agents in Feb 2026 (https://calv.info/agents-feb-2026)
- Everyone got excited they can suddenly code, and completely missed the point — Kasper Junge (https://kasperjunge.com/blog/should-pms-code-with-agents/)
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10 AI Agents for Product Managers MindStudio (https://www.mindstudio.ai/blog/ai-agents-for-product-managers) - AI Coding Agents, Deconstructed - by Alejandro Piad Morffis (https://blog.apiad.net/p/the-anatomy-of-ai-coding-agents)
- Coding agents - Coding agents for data analysis (https://simonw.github.io/nicar-2026-coding-agents/coding-agents.html)
- From Product Manager to Agent Manager - by Zakir Tyebjee (https://productgrindhq.substack.com/p/from-product-manager-to-agent-manager)
- An autonomous employee that makes decisions for itself
- An LLM that repeatedly uses tools to achieve a goal
- Magic that builds apps without code
- Because it connects to the database
- Because it perceives existing code as valid patterns
- To write code creatively
- Leave all planning completely to the AI
- Utilize them as tools to implement human ideas
- Neglect everything and let the AI write all the code