Agentic Coding: 6 Best Practices You Need to Know

Agentic coding means a software development approach where AI agents interact with your codebase and environment to generate, modify, and maintain code with minimal human intervention. During agentic coding, AI agents use large language models (LLMs) and can perform tasks like debugging, optimizing, and even deploying code, learning from their mistakes, and adapting to different coding styles and projects.

Anthropic AI launched Claude Code, a command-line tool designed to integrate its AI, Claude, more deeply into developers' daily routines. Claude Code offers a direct line to the AI model's capabilities without setting strict operational structures, resulting in a tool that is adaptable, open to scripting, and built with safety in mind, and this very adaptability means users benefit from developing their own methods for interaction. Let’s check out the 6 best practices for agentic coding.

A quick tour of standout features

• Plain-text on-ramp: Dip a CLAUDE.md file anywhere in a project; the tool reads it automatically and adopts the repo's norms.

• Permission guardrails: Every potentially destructive command is blocked until the user grants a one-time or persistent allow-list entry.

• "Think" budget control: Phrases like "think harder" or "ultrathink" give Claude more computation time for thorny problems.

• Shell and MCP integration: Anything the terminal can do, custom scripts, gh-CLI, and Puppeteer servers become part of Claude's skillset.

• Headless mode: A single flag turns the agent into a non-interactive worker that fits neatly into CI jobs and cron tasks.

Here are the 6 best practices for agentic coding you should know

1. Customize your setup

1. Shape the context with CLAUDE.md

Claude Code starts every session by reading markdown files named

CLAUDE.md. Teams can use them to document habitual shell snippets, style guides, or caveats that trip up new contributors. The file can sit at the repo root, nested in a sub-folder for monorepos, or live globally in ~/.claude/. Because the agent re-reads the file on each invocation, treating it like code, reviews changes, keeps it concise, and iterates on phrasing, it pays off quickly.

2. Tune the allow-list instead of fighting prompts

Out of the box, Claude asks before writing a file, committing, or running a custom binary. Adding trusted commands (for example, Edit or Bash(git commit:*)) via /allowed-tools or the session flag --allowedTools cuts friction without opening the door to mishaps. Teams that script deployments often keep a repo-local settings.json checked in, so every developer shares the same baseline.

2. Give Claude more tools

Because the tool inherits the developer's shell, it can call linters, code-generators, or provisioning scripts just like a human would. For more exotic tasks, engineers expose REST endpoints or MCP servers; a Puppeteer MCP instance, for example, allows the agent to take screenshots and run browser regression tests. Documenting unfamiliar commands in CLAUDE.md or teaching the agent to run --help once per session keeps it oriented.

3. Give Claude more tools

Explore → Plan → Code → Commit

The classic flow begins with reading relevant files (but not writing) and then asking Claude to outline a solution. Trigger words that extend thinking time make the plan stronger. Once the outline looks solid, the agent writes code, self-checks logic, and finally commits. Anthropic's engineers report big drops in re-work when steps one and two are never skipped.

Red-green-refactor, super-charged

When teams practice test-first habits, Claude can write the failing tests, commit them, and only then build the implementation. In repetitive cycles, the agent writes code, runs tests, and patches errors continuously until green. A secondary agent can watch for over-fitting, providing a second set of "eyes," much like a peer review.

Visual targets accelerate UI work

Paste a design mock or drop a path to a screenshot; Claude will compare its rendered page against the image, adjust CSS, and iterate until the pixel gaps disappear. With diagrams, the same mechanism doubles as a debugging help: the agent marks up screenshots to explain layout quirks before proposing fixes.

Safe "YOLO" in a sandbox

Passing --dangerously-skip-permissions is tempting when hammering out boilerplate. Anthropic's own guidance: run that flag only inside a container or throw-away VM with the network disabled. The agent moves fast, but it is as capable of deleting a database as any human with a root.

4. Optimize your workflow

Regardless of the workflow, certain general principles can improve your experience:

• Specificity is Key: Clear, detailed instructions lead to better results, especially on the first try.

• Use Visuals: Claude works well with images. Paste screenshots, drag-and-drop images, or provide file paths, especially for UI development or data analysis.

• Reference Files Directly: Use tab-completion to point Claude to specific files or folders.

• Provide URLs: Paste web addresses for Claude to fetch and read content. Consider adding frequently used domains to your allowed list to avoid repeated permission prompts.

• Correct Course Actively: While Claude can work autonomously, guiding it generally yields better outcomes. Ask for a plan first. Use Escape to interrupt and redirect, or double-Escape to go back and edit a previous prompt. You can also ask Claude to undo the changes.

• Manage Context with /clear: In long sessions, the context window can become cluttered. Use /clear between distinct tasks to reset it.

• Checklists for Complexity: For large, multi-step tasks, have Claude use a Markdown file or a GitHub issue as a checklist and scratchpad.

5. Use headless mode to automate your infra

Headless mode (claude -p' your prompt') streams JSON that downstream tools can parse. Repositories already use it to label GitHub issues, lint prose for stale comments, or migrate thousands of files between frameworks. Spreading out multiple invocations from a Python harness or a simple Bash for loop can scale the pattern across large codebases without juggling API tokens or prompt templates.

6. Uplevel with multi-Claude workflows

For more complex scenarios, running multiple instances of Claude Code can be very effective:

• Separate Coding and Verification: Have one Claude instance write code while another reviews or tests it. This separation of concerns can lead to higher-quality results.

• Multiple Repository Checkouts: For parallel but distinct tasks, some developers create several local copies of their repository, running a Claude instance in each.

• Git worktrees: A more streamlined alternative to multiple checkouts is using git worktree, allowing you to have multiple branches checked out simultaneously in different directories, each with its own Claude session while sharing the same underlying repository history. This is excellent for working on independent features in parallel.

• Custom Harnesses with Headless Mode: Programmatically integrate headless Claude into larger workflows. This can be used for "fanning out" tasks (e.g., processing many files) or "pipelining" Claude's output into other tools.

Conclusion

Claude Code is a terminal companion that can read files, run shell commands, open pull requests, and even launch its own sub-agents. It is a disciplined collaboration between a human and a capable model. Teams that adopt the practices above will find the agent less of a new concept and more of a routine part of daily engineering, one that keeps improving with every iteration.