- AI coding agents currently face many limitations in large enterprise environments, where codebases and monorepos can exceed 2,500 files or contain files over 500 KB, causing indexing and searching to fail or severely degrade in quality.
- Many agents lack the ability to understand hardware system context—for example, executing Linux commands on PowerShell or stopping midway due to slow output reading, leading to repetitive errors and time-consuming fixes.
- Recurring “hallucinations”: agents misidentify common characters like brackets or dots in Python code as “malicious,” causing the code generation process to stop repeatedly.
- In reality, instead of reducing workload, developers have to “babysit,” checking every code generation step, canceling, fixing, and re-running multiple times, especially when the AI ignores initial instructions or stops halfway.
- Lack of enterprise-standard coding practices: generating code with weak authentication (key-based), ignoring Entra ID or credential federation; using outdated SDKs, causing technical debt and increasing maintenance costs.
- AI coding agents often lack the ability to recognize the developer’s intent for scalability, leading to duplicate code generation, failure to auto-refactor, and missed opportunities to reuse logic.
- “Confirmation bias” causes agents to often agree with the user even when the request is wrong, reducing code quality and lacking technical critical thinking.
- Although AI helps create prototypes quickly, actual operational capability—security, scalability, maintenance—still relies heavily on humans.
📌 Although AI coding agents have revolutionized code generation, limitations regarding context, system awareness, and enterprise compliance prevent them from operating independently in production. Businesses need to use them “wisely”—leveraging AI to draft and automate tedious parts, while still relying on engineers to verify, secure, and design sustainable systems.
