Introduction
Tools like OpenAI's Codex CLI and Anthropic's Claude Code are reshaping how developers work: they can read, modify, and run code locally, acting as an agentic pair-programmer in your terminal [1].
This guide distills practical best practices from official docs, engineering posts, and real-world anecdotes so you can get reliable, reviewable output rather than "AI spaghetti." We use Anthropic's engineering post on Claude Code as a structural reference [2].
🧩 1) Customize Your Setup and Context Files
Persist key project context.
Claude Code automatically includes a project file (e.g., a CLAUDE.md) in its prompt context; use it to encode coding conventions, test/run commands, and project quirks. Keep it concise and living in version control so the assistant consistently follows house rules [2].
Codex CLI configuration.
Codex CLI is open-source and runs locally; configure its project settings (e.g., via config files or flags) and keep the repo-level guidance close to the code [1].
Don't overload context.
Over-stuffing the model with giant code dumps degrades focus. Prefer brief, relevant snippets and let the agent fetch what it needs. Anthropic's best-practices emphasize scoped context and iterative refinement [2].
🔄 2) Work Iteratively: Plan → Change → Verify
Start with a plan.
Have the agent propose a short plan (5-7 steps) before editing. Then execute small, reviewable diffs (about "a commit's worth") so you can easily test and back out if needed [2].
One intent per iteration.
Avoid mixed patches (bug fix + refactor + style). Keeping a single intent per run yields cleaner diffs and fewer regressions [2].
Verify immediately.
Run tests/builds right after a change ("proximate verification"). If something fails, paste logs back to the agent and let it diagnose or roll back [2].
🧠 3) Extend the Agent's Skills (Tools, LSPs, MCP)
Give the agent more tools.
Claude Code inherits your shell environment; you can expose linters, test runners, doc search, or custom scripts, and even run it headlessly in CI [3].
Use MCP for deeper integrations.
Anthropic's Model Context Protocol lets you wire secure, two-way connections between tools/data and the model. This is handy for code search, issue trackers, or knowledge bases [4].
Example: LSP-style code understanding.
Community projects like Serena demonstrate LSP-like analysis plugged into Claude Code for smarter retrieval/editing in large repos (fewer tokens, better diffs) [5].
🧷 4) Choose Autonomy Levels Deliberately
Start conservative.
Keep manual approvals on while you learn the tool's behavior. As trust grows, enable auto-edit for routine refactors in a sandboxed directory. (Claude Code's docs and posts show headless/automation modes; many guides caution against turning approvals fully off on prod repos.) [3]
Use full-auto sparingly.
Codex CLI and community guides document "full-auto/no-approval" profiles; use them for experiments/CI on throwaway branches, not mainline development. Also note that newer Codex versions introduced safety/permission changes affecting full-auto behavior, so check release notes/discussions [6].
🧯 5) Common Pitfalls (and Fixes)
| Pitfall | Why it happens | Fix |
|---|---|---|
| Overloading context | Huge dumps dilute attention | Keep prompts scoped; let the agent fetch files as needed [2] |
| Letting AI design architecture | Agents excel at implementation, not high-level design | You pick the patterns; let the agent implement/test [2] |
| Bloated, mixed diffs | Scope creep during one run | Enforce one intent per session; split refactors [2] |
| Ignoring test failures | Silent errors snowball | Paste logs back; iterate or roll back with the agent [2] |
| Full-auto chaos | No checkpoints/approvals | Use approvals or sandbox; reserve full-auto for CI [3] |
📎 6) Real-World Anecdotes & Tips
- Codex CLI capabilities in the wild.
Community threads summarize what Codex CLI does well: editing files, running commands, working under version control/sandboxing, and using repo docs (e.g., acodex.md) for extra context [7]. - Agent discipline over "magic prompts."
Developers emphasize disciplined workflows (small plans, tight loops, immediate tests) over one-shot prompts as the real productivity unlock. (Recurring theme across best-practices and agent forums.) [2], [8] - Headless/CI usage.
Claude Code supports headless mode so you can wire it into pre-commit hooks, CI, and build scripts. This provides a practical path to safe automation [3].
🧭 Conclusion
Treat CLI coding agents as junior pair-programmers: give them crisp context (CLAUDE.md/config), insist on small, testable steps, and turn up autonomy only in safe, reversible environments. Do that, and tools like Codex CLI and Claude Code can take on the grind while you stay focused on design and quality [1].
References
- OpenAI. "Codex CLI Documentation." OpenAI Developer Platform, 2025. Available: https://developers.openai.com/codex/cli/
- Anthropic Engineering Team. "Claude Code: Best practices for agentic coding." Anthropic Technical Blog, 2025. Available: https://www.anthropic.com/engineering/claude-code-best-practices
- Anthropic. "Claude Code Headless Mode Documentation." Claude Documentation, 2025. Available: https://docs.claude.com/en/docs/claude-code/headless
- Anthropic. "Introducing the Model Context Protocol (MCP)." Anthropic News, 2025. Available: https://www.anthropic.com/news/model-context-protocol
- Oraios. "Serena: A powerful coding agent toolkit." GitHub Repository, 2025. Available: https://github.com/oraios/serena
- SmartScope. "Codex CLI No Approval Setup: Full-Auto & Claude Integration." SmartScope Blog, 2025. Available: https://smartscope.blog/en/generative-ai/chatgpt/codex-cli-approval-modes-no-approval/
- Reddit Community. "OpenAI releases Codex CLI, an AI coding assistant built for developers." r/singularity, 2025. Available: https://www.reddit.com/r/singularity/comments/1k0qc67/
- Reddit Community. "Best practices for coding AI agents?" r/AI_Agents, 2025. Available: https://www.reddit.com/r/AI_Agents/comments/1k7okkg/