Key Takeaways
- Missing project context and unclear requirements can lead coding agents to make incorrect assumptions.
- Reusable skills and project instructions help agents follow established engineering standards.
- Model selection and reasoning effort should reflect task complexity and the cost of review and rework.
- Reviewing plans before implementation helps resolve misunderstandings and identify dependencies early.
- Code review and testing remain necessary to verify that generated code behaves as intended.
Coding agents such as Codex and Claude Code can help developers generate, review, refactor, and debug code. Those capabilities can speed up parts of development, but getting consistently good results still requires a clear process.
Sometimes an agent produces a clean implementation that fits naturally into the existing codebase. Other times it creates unnecessary abstractions, duplicates functionality, ignores existing conventions, or confidently implements something that was never actually requested. That inconsistency makes the output difficult to trust.
The natural response is to blame the model, and sometimes that criticism is fair. Large language models (LLMs) can still hallucinate, misunderstand requirements, and make bad technical decisions. Unclear or incomplete requirements can contribute to those failures.
When an agent lacks context about a project, its standards, or the requested change, it may inspect the repository, ask for clarification, or proceed on assumptions. Improving AI-assisted development starts with a process that reduces how much the model has to guess.
Standard Operating Procedures for Coding Agents
Agent skills package reusable instructions for specific tasks or workflows in a SKILL.md file, sometimes alongside scripts and reference material. Teams can use them to guide recurring work without repeating the same instructions in every prompt.
Skills can cover areas such as UI, UX, APIs, and databases. An API skill might explain how endpoints are structured, where validation belongs, how errors should be handled, and how authentication works.
A database skill might describe schema conventions, migrations, relationships, indexes, and the patterns the application expects the data layer to follow. The documentation should focus on decisions the agent would otherwise have to guess.
For projects that lack documentation, an agent can review the repository and draft skills based on existing patterns. Those drafts need careful review: an agent may mistake an inconsistency in the code for an intentional standard.
Engineers also need to explain architectural decisions that cannot be inferred from the code and document business rules or team expectations that only exist in people’s heads.
As the project grows, these skills can also become more specific. A broad API skill might eventually be split into separate documentation for authentication, ingestion, billing, or individual modules. The goal is to give the agent the context relevant to the work it is doing without forcing it to ingest everything about the project every time.
A main README.md can describe the project and act as a table of contents. Standing rules should also live in the instruction files recognized by the coding tool, such as AGENTS.md for Codex. Those instructions can direct the agent to relevant documentation; skills supply additional guidance when invoked for a task.
Matching the Model and Effort to the Task
Model selection and reasoning effort should reflect the scope and difficulty of the work.
A quick repository scan, a formatting change, or a small targeted fix can often be handled by a faster and cheaper model. A feature that touches several parts of the application may require something more capable.
Architecture work, difficult debugging, and changes across multiple systems can benefit from more capable models or higher reasoning effort, where supported. Higher effort generally takes more time and tokens, so it should be used when the task warrants deeper analysis.
The mistake is treating every task the same. Model selection should account for the cost of completing the work, including review and rework. A model with a lower per-token price is not necessarily cheaper if it takes more attempts to produce an acceptable result.
Refining Requirements Before Implementation
For significant changes, planning mode gives engineers a chance to review the approach before the agent starts changing code. A useful plan explains what needs to change, which parts of the system are affected, and what risks or edge cases need attention.
The plan gives the engineer an opportunity to catch misunderstandings early. Accepting it without review skips that opportunity. Correcting the approach before implementation can prevent unnecessary code changes and rework.
A typical request illustrates the problem:
“Extend this API to ingest an additional field of data.”
At first glance, this seems like a simple change. The request describes the desired outcome but leaves out how the new field should behave within the existing system. A developer would need that context before implementing the change. The coding agent does too.
Working through those details with an agent helps surface dependencies and edge cases before they become implementation problems.
The process usually starts with a rough request and an initial plan. The engineer notices an architectural misunderstanding and provides more context. The agent identifies an edge case, prompting a requirement change. Those revisions continue until the plan reflects what needs to happen.
By the time implementation begins, the agent should understand the requirement, the relevant architecture, and what the finished feature is expected to do. That gives the engineer a concrete basis for reviewing its work.
Good AI Code Starts Before the Code Is Written
Working with an AI coding agent has something in common with working with another engineer: the implementation depends heavily on how well they understand the system and the change being requested. Faster code generation makes that preparation more valuable.
Documented standards, appropriate model selection, and clear requirements give agents a stronger foundation. Code review and testing remain necessary to verify that the implementation behaves as intended and fits the system.
When AI-generated code consistently falls short, the development process deserves scrutiny alongside the model. Unresolved requirements and architectural decisions leave more room for assumptions that may not match the intended design.