You should expect the same classes of failures you’d expect from a fast-moving assistant that doesn’t truly “own” your codebase: missing context, incorrect assumptions, overconfident explanations, and patches that pass a narrow test but break a broader invariant. The most common failure mode is API hallucination: the model calls a helper that doesn’t exist, assumes a library has a certain method, or uses an internal service contract incorrectly. A close second is scope creep: it touches more files than necessary, “cleans up” unrelated code, or changes behavior you didn’t ask to change. These failures aren’t unique to GPT 5.3 Codex, but they’re predictable enough that you can design guardrails to catch them early.
Another set of failure modes appears in long-running, tool-driven tasks: infinite loops (“it keeps rerunning tests without changing anything”), brittle command execution (wrong working directory, missing env vars), and “fixing the symptom” (muting a warning, loosening an assertion, or adding a retry) rather than addressing the underlying bug. This is where you should enforce strict policies in your harness: cap iterations, require each iteration to include a diff plus a short rationale, and forbid disabling tests unless explicitly requested. OpenAI’s system materials discuss long-horizon agent behavior and mechanisms like compaction to sustain progress across long horizons, which is a reminder that long tasks need structured state and periodic summarization to prevent drift. In plain terms: if you let a model run for a long time without clear checkpoints, it can drift.
The best mitigation is a combination of retrieval and validation. Retrieval prevents many “wrong assumption” failures by supplying the correct internal references at the moment of coding. Put your internal docs, known-good patterns, and “how we do X” snippets into Milvus or managed Zilliz Cloud, retrieve the top-k relevant context, and explicitly instruct GPT 5.3 Codex to use only those patterns. Validation catches what retrieval can’t: run tests, static analysis, type checks, and security scans. If you build your workflow so the model must propose a patch, then prove it with tool outputs, failure modes become manageable: they turn into “it didn’t pass the checks yet,” not “it silently shipped a bad change.”