CoatiPress

Tag: code review

  • When Coding Agents Work in the Background: A Practical Guide to Asynchronous AI Development

    When Coding Agents Work in the Background: A Practical Guide to Asynchronous AI Development

    The New Teammate That Works While You Keep Moving

    Picture a small WordPress plugin team on a Monday morning. A developer is deep in release planning when a support ticket reports a small but frustrating bug: a settings page throws a warning when a field is left blank. Instead of dropping everything, the developer assigns an AI coding agent a narrow task: reproduce the warning, add a failing test if possible, propose a fix on a separate branch, and summarize the change.

    The developer keeps working. Later, the agent returns with a branch, test results, a short explanation, and a pull request ready for human review. The team still owns the decision. The agent did not ship the change. It did something more practical: it converted a bounded issue into draft work.

    That is the core promise of asynchronous AI development. It is not about replacing developers. It is about delegating well-scoped repository chores to background collaborators while humans remain responsible for priorities, architecture, review, and release quality.

    What Asynchronous AI Coding Agents Are

    Most people first meet AI coding tools through chat: ask a question, paste an error, request a function, and get an answer. Chat-style assistance is immediate and conversational. Asynchronous coding agents shift the pattern. Instead of asking for help in the moment, a developer assigns a task to an agent that can inspect a repository, make changes in an isolated workspace or branch, run commands, and return proposed work for review.

    GitHub has described agentic workflows as a way to automate repository tasks, including assigning issues to coding agents that can work on those issues and open pull requests. Google describes Jules as an asynchronous coding agent that runs tasks in a cloud virtual machine and prepares proposed code changes for review. OpenAI documentation describes Codex as a cloud-based software engineering agent that can work on tasks in a repository and produce changes for developers to inspect.

    The important workflow shift is this: the human is no longer using AI only as a typing assistant. The human is acting more like a technical lead for a very fast junior teammate. That teammate needs a clear task, relevant context, limited permissions, and careful review.

    Good Tasks for Background Agents

    Asynchronous agents work best when the task is specific, testable, and reversible. They are especially useful when the work is valuable but interrupts a developer’s main focus. For WordPress and plugin teams, that often means turning support-driven backlog items into small, reviewable improvements.

    • Dependency updates where the expected change is narrow, tests already exist, and the agent can report any breaking changes or deprecations it finds.
    • Small bug fixes with clear reproduction steps, such as a PHP warning, a JavaScript console error, a form validation issue, or a missing null check.
    • Test additions for known behavior, especially when a team wants stronger coverage before refactoring a plugin module or API integration.
    • Documentation updates based on recent code changes, support questions, or release notes that need clearer setup instructions.
    • Reproduction cases for reported bugs, including a minimal failing test, fixture, or step-by-step confirmation that the issue exists.
    • Low-risk refactors behind strong tests, such as renaming internal helpers, simplifying duplicated logic, or moving code without changing public behavior.
    • Issue triage, such as labeling tickets, identifying likely affected files, summarizing related commits, or proposing whether an issue is a bug, documentation gap, or feature request.

    The common thread is boundedness. A good agent task has a clear finish line. “Investigate why logged-in users sometimes hit a chat rate limit and propose a failing test” is much better than “improve the chat system.” “Update the README section for installation and activation” is better than “make our docs better.”

    Poor Tasks for Asynchronous Agents

    Background agents are less reliable when the work requires product judgment, ambiguous tradeoffs, customer empathy, security-sensitive decisions, or broad architectural changes. These tasks may still benefit from AI-assisted research or draft proposals, but they should not be delegated as open-ended implementation jobs.

    • Designing a new pricing model, permission system, onboarding flow, or product strategy without close human direction.
    • Changing authentication, payment, encryption, data export, or personally identifiable information handling without expert review.
    • Large architecture migrations where many modules, tests, release notes, and customer behaviors are affected.
    • Fixing vague issues such as “the plugin feels slow” unless the task is narrowed to profiling, measurement, or a specific suspected cause.
    • Writing tests that simply confirm the agent’s own implementation instead of preserving intended product behavior.
    • Making release decisions, merging pull requests, tagging production builds, or deploying changes without an accountable human owner.

    A useful rule: if you would not hand the task to a new contractor with limited context, you probably should not hand it to an autonomous coding agent without narrowing it first.

    A Practical Workflow: From Issue to Agent Branch to Review

    A healthy asynchronous workflow looks less like magic and more like disciplined delegation. The agent is not wandering through the repository looking for ways to be helpful. It is working from a ticket, a branch, a test command, and a definition of done.

    • Start with a narrow issue. Describe the observed problem, expected behavior, affected environment, relevant files if known, and any non-goals.
    • Add repository-specific context. Include coding standards, test commands, plugin compatibility requirements, WordPress version assumptions, release branch rules, and any areas the agent must not touch.
    • Assign the task in an isolated branch or workspace. The agent should not work directly on the main branch, production systems, or a shared release branch.
    • Require evidence. Ask for a failing test, passing test output, reproduction steps, screenshots, logs, or a clear explanation when a test cannot be added.
    • Limit permissions. Give the agent only the repository, tools, commands, and environment variables it needs. Avoid exposing production secrets or broad write access.
    • Review as draft work. Treat the agent’s branch like a pull request from an unfamiliar contributor: inspect the diff, run tests independently when needed, and verify behavior manually for user-facing changes.
    • Close the loop. If the output is useful, merge it through the normal process. If it is wrong, capture why: missing context, unclear instructions, weak tests, or a task that was too broad.

    For a WordPress plugin team, this workflow can be especially useful around support queues. A support report might become an agent task to reproduce the issue in a local environment, identify the likely component, and draft a test. The human maintainer then decides whether the proposed fix is safe for the next patch release.

    The Main Risks: Hidden Work, Hidden Context, Hidden Authority

    Asynchronous agents can reduce interruption, but they can also create new forms of work. If five agents produce five pull requests that all need careful review, the team has not removed work; it has moved work into the review queue. That can still be a win, but only if the team manages the queue intentionally.

    • Stale context: Agents may work from outdated assumptions, old tickets, or incomplete documentation. Refresh the task with current branch names, recent decisions, and known constraints.
    • Excessive autonomy: Agents should not decide scope expansion on their own. If the task uncovers a larger issue, the better outcome is a summary and recommendation, not a surprise rewrite.
    • Secret exposure: Agentic systems can interact with tools, repositories, and logs. Do not provide production credentials, customer data, or broad environment access unless there is a strong reason and a controlled process.
    • Low-quality tests: Agents may create tests that pass without proving meaningful behavior. Review whether the test would fail for the original bug and whether it protects the intended contract.
    • Review overload: A team can drown in AI-generated pull requests. Limit concurrent agent tasks, prioritize high-confidence work, and make one human owner accountable for each branch.
    • Unclear accountability: The agent is not responsible for the release. A named human should own the merge decision, changelog entry, rollout plan, and rollback path.

    Security and governance deserve explicit attention, even in small teams. OWASP’s Top 10 for Large Language Model Applications identifies risks relevant to agentic systems and LLM-enabled tools, including prompt injection, sensitive information disclosure, insecure plugin design, excessive agency, and supply-chain vulnerabilities. Those risks do not mean teams should avoid AI coding agents entirely. They mean teams should cap authority, log activity, protect secrets, and keep human review in the release path.

    An Adoption Checklist for Small Product Teams

    Small teams do not need a complex platform program to begin. They need a few repeatable habits that prevent background automation from becoming background confusion.

    • Create an “agent-ready” issue template with fields for goal, context, affected files, test command, definition of done, non-goals, and required review owner.
    • Start with low-risk categories: documentation updates, test additions, reproduction cases, dependency notes, and small bugs with clear steps.
    • Use separate branches for every task and require pull requests for all agent output.
    • Set a concurrency limit, such as one or two active agent tasks per developer, until the review load is predictable.
    • Maintain a short repository guide for agents that includes project structure, coding standards, local setup, testing commands, release rules, and forbidden actions.
    • Require the agent to summarize changed files, commands run, tests passed or failed, and unresolved questions.
    • Review tests first, then implementation. If the test does not capture the intended behavior, the implementation is less trustworthy.
    • Keep release authority human. Agents can prepare draft work, but humans approve merges, version bumps, customer communication, and deployment.
    • Track outcomes for a month. Measure how many tasks were accepted, revised, discarded, or caused review bottlenecks. Use that data to improve task selection.

    This approach fits a broader principle across AI-first workflows: better inputs produce better outputs, and phased review prevents rough drafts from becoming finished work too early. Whether a team is generating article drafts, assisting website visitors, prospecting leads, or maintaining a plugin codebase, AI works best when humans define the goal, constrain the process, and review the result.

    The Skill Is Clearer Delegation, Not Hands-Off Automation

    The most successful teams will not be the ones that simply turn agents loose. They will be the teams that learn to delegate with precision. They will break work into smaller tickets, write clearer definitions of done, maintain better tests, document repository conventions, and protect the review process from overload.

    Asynchronous AI coding agents are best understood as background teammates: fast, tireless, and useful when given the right job, but still dependent on human judgment. They can draft the bug fix, update the docs, add the test, or prepare the reproduction case while developers keep moving. The final responsibility remains where it belongs: with the people who understand the product, the users, and the release.

    Sources and Fact Check References

    • GitHub Blog – GitHub has described agentic workflows that automate repository tasks, including assigning issues to coding agents that can work on issues and open pull requests.
    • Google Blog – Google describes Jules as an asynchronous coding agent that runs tasks in a cloud virtual machine and prepares proposed code changes for developer review.
    • OpenAI Help Center – OpenAI documentation describes Codex as a cloud-based software engineering agent that can work on tasks in a repository and produce changes for developers to inspect.
    • OWASP Foundation – OWASP’s Top 10 for Large Language Model Applications identifies risks relevant to LLM-enabled and agentic systems, including prompt injection, sensitive information disclosure, insecure plugin design, excessive agency, and supply-chain vulnerabilities.
  • The New Code Review: How Humans Should Review Work From AI Coding Agents

    The New Code Review: How Humans Should Review Work From AI Coding Agents

    AI Can Write the Diff. Humans Still Own the Decision.

    AI coding agents are changing what code review is for. In a traditional review, a teammate usually explains the problem, writes the code, and opens a pull request with human intent behind every major choice. With an AI coding agent, implementation can arrive faster, broader, and sometimes more confidently than the underlying reasoning deserves.

    That does not make review less important. It makes review more judgment-heavy. The reviewer’s job is no longer just to spot syntax mistakes, suggest cleaner names, or ask for one more test. It is to decide whether the change should exist, whether it solves the right problem, whether it fits the system, and whether the team can safely maintain it later.

    Recent industry research points in the same direction: AI adoption in software work is rising, but trust, accuracy, and human verification remain central concerns. Stack Overflow’s 2025 Developer Survey found that more developers distrusted the accuracy of AI tools than trusted it, while DORA’s 2025 research reported broad workplace use of AI among technology professionals alongside ongoing questions about effective, reliable adoption. In practice, strong teams treat AI-generated code as a fast draft from a capable but non-accountable contributor. Useful? Often. Final? Not until a human has reviewed it.

    Why AI-Written Code Needs a Different Review Mindset

    AI coding agents are good at producing plausible code. That is both their strength and their risk. A human junior developer may ask clarifying questions, hesitate around unfamiliar systems, or leave obvious gaps. An AI agent may produce a complete-looking implementation even when the task is underspecified, the repository patterns are unclear, or the business rule is ambiguous.

    Reviewers should assume three things until proven otherwise: the agent may have optimized for local correctness instead of system fit, it may have filled in missing requirements without saying so, and it may have changed more than the task required. This is not a reason to reject AI assistance. It is a reason to review from the outside in.

    • Do not start by admiring the diff. Start by restating the user need or engineering goal.
    • Do not assume a passing test means the behavior is right. Ask whether the test proves the intended outcome.
    • Do not treat confident code as explained code. Require traceable reasoning for important changes.
    • Do not reward large, sweeping changes if a smaller change would have solved the problem.
    • Do not let the AI agent’s speed pressure the team into lowering review standards.

    Before Reading the Diff, Check the Assignment

    The most useful review often happens before the reviewer opens the changed files. If the task is vague, the code review will become a guessing game. For AI-generated work, reviewers should first inspect the prompt, ticket, acceptance criteria, or issue description that guided the agent.

    Ask whether the agent was given a clear target. What behavior should change? What should stay the same? Which files, APIs, roles, devices, permissions, or data boundaries matter? What constraints were stated? What constraints were assumed? If the task asks for “improve checkout validation,” the reviewer needs to know whether that means better error messages, stricter server-side rules, accessibility improvements, fraud prevention, or all of the above.

    • What exact problem is this change supposed to solve?
    • Who benefits from the change: user, admin, developer, support team, or business stakeholder?
    • What are the acceptance criteria, and are they measurable?
    • What areas of the system were intentionally out of scope?
    • Was the AI agent allowed to add dependencies, change database schemas, alter public APIs, or refactor unrelated code?
    • Is there a human-readable summary of what the agent changed and why?

    A Layered Review Workflow for AI Coding Agents

    A practical human-in-the-loop review works best in layers. Instead of reading every line from top to bottom immediately, move from purpose to risk to implementation detail. This helps reviewers avoid getting distracted by polished code that may not solve the right problem.

    1. Product Intent: Does This Solve the Right Problem?

    Start with the outcome. If the change is user-facing, verify that it matches the intended workflow, language, permission model, and failure states. If it is internal, verify that it improves the developer or operational experience without creating hidden obligations.

    AI agents can accidentally implement a nearby idea instead of the actual requirement. For example, an agent asked to “add admin filtering” might build a new search interface when the real need was a simple status dropdown on an existing table. The code may work, but the product judgment is wrong.

    • Does the change match the original request, not merely a related interpretation?
    • Are edge cases defined from the user’s point of view?
    • Could the new behavior surprise existing users?
    • Are copy, labels, errors, and empty states clear and appropriate?
    • Does the change respect role permissions and business rules?

    2. Architecture Fit: Does It Belong Here?

    Next, check whether the implementation fits the existing system. AI agents often infer patterns from nearby files, but they may miss deeper conventions: service boundaries, domain ownership, performance assumptions, release constraints, or framework-specific best practices.

    A good reviewer asks whether the change makes the codebase easier or harder to reason about six months from now. A solution that adds a new abstraction, helper, dependency, or background job should justify the extra moving parts.

    • Does the change follow existing project patterns?
    • Is the logic located in the right layer, such as UI, API, domain service, or data access?
    • Does it duplicate behavior that already exists elsewhere?
    • Does it introduce a new abstraction before the codebase needs one?
    • Would another developer know where to look when this feature breaks?

    3. Data, Security, and Privacy Risk: What Could Go Wrong?

    AI-generated code deserves careful review anywhere it touches authentication, authorization, payments, personally identifiable information, customer data, logs, file uploads, external APIs, or database writes. These are areas where a small plausible mistake can become a serious incident.

    Reviewers should pay special attention to silent trust changes. Did the code move validation from the server to the client? Did it expose extra fields in an API response? Did it log sensitive input? Did it make an admin-only operation reachable from a lower-privilege path? These problems may not stand out in a diff unless the reviewer is looking for them.

    • Are authorization checks still enforced on the server?
    • Are inputs validated and outputs encoded in the right places?
    • Does the change expose new data through responses, logs, analytics, or error messages?
    • Are secrets, tokens, and credentials handled safely?
    • Do database migrations preserve existing data and support rollback?
    • Does any new dependency increase supply-chain risk?

    4. Test Evidence: What Has Been Proven?

    For AI-generated work, reviewers should not ask only “Are there tests?” A better question is “What claim do these tests prove?” AI agents can create tests that mirror their own assumptions, assert implementation details, or cover the happy path while missing the real failure mode.

    Useful tests connect back to acceptance criteria. If the task is about permissions, tests should cover allowed and denied users. If the task is about data transformation, tests should include messy inputs. If the task is about a user interface, tests or review evidence should cover keyboard navigation, screen states, and error handling where appropriate.

    • Do the tests fail without the production change?
    • Do they cover the bug, feature, or risk described in the task?
    • Are negative cases included, not only happy paths?
    • Are edge cases represented with realistic data?
    • Is there evidence from local runs, CI, screenshots, logs, or manual verification when automated coverage is not enough?

    5. Readability and Maintainability: Can Humans Own This Code?

    AI agents can generate code that is syntactically correct but oddly shaped. The reviewer should make sure future humans can understand, debug, and extend it. Cleverness is not a virtue if it makes the team dependent on another AI pass to understand the implementation.

    Look for unnecessary generalization, inconsistent naming, overly defensive branches, and comments that describe what the code does without explaining why. Also watch for large formatting churn that hides the meaningful change.

    • Is the simplest reasonable solution used?
    • Are names consistent with the domain language of the project?
    • Can the code be understood without reading the original prompt?
    • Are comments used to explain non-obvious decisions rather than restating the code?
    • Does the diff avoid unrelated cleanup, formatting churn, and opportunistic refactors?

    6. Operational Impact: What Happens After Merge?

    Some changes are correct in isolation but risky in production. Reviewers should consider deployment, monitoring, performance, support, and rollback. AI agents may not know which parts of the system are fragile, expensive, rate-limited, or heavily used unless the prompt and repository context made that clear.

    • Could this increase latency, memory use, API calls, database load, or background job volume?
    • Does the change need feature flags, staged rollout, or migration sequencing?
    • Are errors observable through logs, metrics, or alerts?
    • Can the change be rolled back safely?
    • Will support, documentation, or customer-facing guidance need updates?

    When to Ask the AI Agent for a Self-Review

    A useful habit is to ask the AI coding agent to review its own work before the human review begins. This is not a substitute for human judgment. It is a way to surface assumptions, summarize changes, and generate a checklist of likely risk areas.

    Sources and Fact Check References

    • Stack Overflow Developer Survey 2025 – Stack Overflow’s 2025 Developer Survey found that more developers distrusted the accuracy of AI tools than trusted it.
    • DORA 2025 Research – DORA’s 2025 research reported broad workplace use of AI among technology professionals and examined reliable adoption of AI in software delivery.
  • Before You Hand Work to an AI Coding Agent: A Practical Guardrail Checklist for Small Teams

    Before You Hand Work to an AI Coding Agent: A Practical Guardrail Checklist for Small Teams

    The Shift From Autocomplete to Agentic Development

    AI coding tools are moving beyond autocomplete. The important shift for small teams is not just that models can suggest a function faster; it is that coding agents can inspect a repository, plan a change, edit multiple files, run commands, summarize results, and sometimes prepare a pull request. Gartner described enterprise AI coding agents as part of a shift from AI-assisted development toward agentic software development across the software development life cycle, including planning, creating, and reviewing code.

    A coding agent, in plain language, is a software assistant that can take a development goal and perform steps toward it. Depending on the tool and configuration, it may read project files, modify code, run tests, use a terminal, search documentation, create commits, or draft a pull request for review. OpenAI describes Codex as a coding agent for real engineering work such as building features, refactors, migrations, and pull requests, while Anthropic describes Claude Code as an agentic assistant that can read code, edit files, run commands, search, and use git from a terminal workflow. That makes guardrails essential. The question is not whether an agent is useful. The question is what it is allowed to touch, how its work is verified, and who remains accountable.

    A Guardrail Checklist Before the Agent Edits Anything

    Small teams do not need enterprise bureaucracy to use coding agents responsibly. They do need a short, written checklist that turns vague trust into concrete controls. Before giving an agent repository access, decide which permissions, environments, and approval gates are required for each type of work.

    • Repository permissions: Start with the least access needed. Prefer read-only access for exploration tasks and limited write access for scoped implementation tasks. Do not give an agent broad organization-level permissions by default.
    • Sandboxing: Run agent-generated commands in a disposable local environment, development container, or isolated cloud workspace. The agent should not be able to alter production data, shared credentials, or developer machines without explicit approval.
    • Branch strategy: Require agents to work on short-lived feature branches with descriptive names. Avoid direct commits to main, release, or production branches.
    • Test coverage: Define the minimum verification bar before the task begins. For example, relevant unit tests must pass, integration tests must pass where applicable, and the agent must explain which tests it ran and which it did not run.
    • Secret handling: Never paste API keys, customer data, private tokens, database dumps, or production credentials into prompts. Use secret scanning and environment variables, and treat prompt history as information that may require governance.
    • Dependency-change review: Require human approval for package upgrades, new dependencies, lockfile changes, build tool changes, or generated code that introduces a new runtime requirement.
    • Prompt-instruction files: Maintain a project instruction file that states coding standards, testing commands, architectural boundaries, security expectations, and files the agent should not modify without approval.
    • Human approval gates: Require a human review before database migrations, authentication changes, payment logic, permissions logic, production configuration, release packaging, or changes to public APIs.
    • Logging and audit trails: Keep a record of what the agent was asked to do, what files it changed, what commands it ran, and which human approved the result. This matters when a regression appears later.
    • Rollback plans: Before merging agent-written changes, confirm the rollback path. That may mean a revertable pull request, a database migration rollback, a feature flag, or a staged release plan.

    Local, Cloud, and IDE-Integrated Agents: What Changes?

    Not all coding agents carry the same risk profile. A local agent runs close to a developer’s workstation and may have convenient access to project files and local tools. The OpenAI Codex repository describes Codex CLI as a coding agent that runs locally on a user’s computer, and Anthropic’s Claude Code documentation says local execution gives the agent access to the user’s files, tools, and environment. That can be fast, but teams must be careful about shell access, environment variables, and unreviewed command execution.

    A cloud agent can work in an isolated or managed environment and may be easier to audit, but it raises questions about repository permissions, data exposure, network access, and log retention. An IDE-integrated agent sits inside a familiar coding workflow, which lowers friction but can encourage developers to accept changes too quickly. The practical rule is simple: match the agent environment to the risk of the task. Asking an agent to rename a UI component, add inline documentation, or draft tests may require lighter controls. Asking it to change authentication, perform a schema migration, modify permissions, or alter a release workflow requires stronger isolation, explicit approvals, and a rollback plan.

    A WordPress Plugin Example

    Imagine a small team working on a WordPress plugin admin screen. A well-scoped agent task might be: “Refactor the settings page into smaller view components, preserve the existing option names, do not add new dependencies, and run the plugin’s PHP and JavaScript tests.” That prompt gives the agent a useful target while setting boundaries around compatibility and package changes.

    The team should still keep higher-risk work under human review. Database migrations, option schema changes, user capability checks, release packaging, WordPress.org readme updates, and deployment steps should not be silently delegated. For an AI-first software company such as CoatiPress, which builds products in the WordPress ecosystem, these guardrails are especially relevant: the faster the tools become, the more important it is to preserve quality, security, and clear ownership.

    What to Put in an Agent Instruction File

    A project-level instruction file is one of the simplest ways to improve agent output. Claude Code documentation describes CLAUDE.md as a markdown file for project-specific instructions, conventions, and context, and the Codex repository itself includes an AGENTS.md file, reflecting the broader pattern of storing agent guidance in the repository. Keep the file short enough that developers will maintain it, but specific enough that the agent can follow it.

    • State the project stack, supported language versions, package managers, and required local services.
    • List the commands for formatting, linting, unit tests, integration tests, builds, and static analysis.
    • Define protected areas such as migrations, release scripts, payment code, authentication code, permissions logic, and production configuration.
    • Explain code style preferences that are not obvious from existing files.
    • Require the agent to summarize changed files, tests run, assumptions made, and remaining risks.
    • Tell the agent when to stop and ask for human approval instead of continuing.

    The Review Standard Should Not Drop Because an Agent Wrote It

    Agent-written code should go through the same review path as human-written code, and sometimes a stricter one. Reviewers should look for plausible but wrong assumptions, unnecessary abstractions, silent behavior changes, hidden dependency updates, weak error handling, and missing tests. The best review question is not “Did AI write this?” It is “Is this change correct, maintainable, secure, and reversible?”

    Teams should also watch for automation bias. When an agent produces a polished summary, the work can feel more complete than it really is. Require evidence: test output, diffs, screenshots for UI changes, migration notes, and a clear explanation of tradeoffs. A confident paragraph is not a substitute for verification.

    A Balanced Takeaway for Small Teams

    Coding agents can accelerate repetitive development work, reduce blank-page friction, and help small teams move through maintenance tasks faster. But they are not magic coworkers, and they do not remove accountability from the people shipping the product. The safest teams will treat agents as powerful contributors operating inside explicit boundaries: limited permissions, isolated environments, strong tests, careful secret handling, human approvals, audit trails, and rollback plans.

    The goal is not to slow everyone down. The goal is to make speed repeatable. When guardrails are clear, developers can hand off appropriate tasks with confidence, reviewers can verify the result, and founders can adopt AI-first workflows without turning their codebase into an experiment with no safety net.

    Sources and Fact Check References

    • Gartner – Gartner described enterprise AI coding agents as part of a shift from AI-assisted development toward agentic software development across the software development life cycle, including planning, creating, and reviewing code.
    • OpenAI Codex – OpenAI describes Codex as a coding agent for real engineering work such as building features, refactors, migrations, and pull requests.
    • OpenAI Codex GitHub repository – The OpenAI Codex GitHub repository describes Codex CLI as a coding agent that runs locally on a user's computer.
    • Anthropic Claude Code documentation – Anthropic documentation says Claude Code can read code, edit files, run commands, use git, and operate across local, cloud, and remote-control execution environments.
    • Anthropic Claude Code documentation – Anthropic documentation describes CLAUDE.md as a markdown file for project-specific instructions, conventions, and context that Claude should know in each session.