Not long ago, programming with AI meant typing a few lines and letting a model complete the next one, or asking for a function or a fix for an error message. That model is quickly becoming outdated. The current generation of coding agents can read an entire codebase, understand a problem, make a plan, edit files, run commands, execute tests and try again when the result is wrong.
Three products now define this race: Anthropic’s Claude Code, OpenAI’s Codex, and Meta’s Muse Code, which entered beta on August 5, 2026. All three share the same basic goal: a person describes what to build or fix, and the agent works out how to do it. Their methods, environments and philosophies differ considerably.
Claude Code: an agent inside the codebase
Anthropic built Claude Code as a terminal agent that works directly inside a developer’s existing repository. It reads files, plans changes, writes code, runs commands and tests, and uses the results to decide its next step. Anthropic describes this as a loop in which the agent gathers context, plans, acts and then checks the outcome before continuing.
One of its main strengths is how far it can be adapted to a team’s conventions. Project instruction files, reusable skills, hooks that run at defined points, and sub-agents let a team tell the agent how the codebase works and which rules to follow. Through the Model Context Protocol (MCP), an open standard for connecting AI systems to external tools, it can also reach internal services and third-party systems.
Sub-agents matter here. A large task can be split into parts and handed to separate agents that run in parallel, while the main agent coordinates the whole job.
Anthropic’s own research on Claude Code usage, based on an analysis of hundreds of thousands of real sessions, offers a useful picture of how this plays out. In a typical session, the person mostly decides what should be done, while Claude mostly decides how to do it. The study also found that more experienced users tend to get more work out of the agent from a single instruction. In other words, Claude Code’s value lies less in generating code than in understanding an existing project and working within it over time.
Codex: a command center for many agents
OpenAI’s Codex takes the same idea in a different direction, with a focus on a full agentic coding environment rather than a single terminal tool. Codex runs in the terminal, in IDEs, in a dedicated desktop app, in the cloud and inside ChatGPT. OpenAI positions it for end-to-end work such as building features, large refactors, migrations, writing tests and reviewing code.
Its signature feature is running multiple agents at once. One agent can work on the front end while another investigates a back-end bug and a third writes tests. Built-in support for Git worktrees, which give each agent its own copy of the repository, keeps their changes from colliding.
Codex also leans into long-running background work. According to OpenAI, it can be scheduled for recurring tasks such as issue triage, alert monitoring and continuous integration chores. The vision is less an AI pair programmer sitting beside a developer and more a command center where one person assigns tasks to several agents and reviews what comes back.
Muse Code: Meta’s entry
Meta’s Muse Code, released in beta on August 5, runs on the company’s Muse Spark 1.2 model and is currently a terminal agent for macOS and Linux. Meta says it can handle complex software engineering tasks in large repositories, including planning changes, writing code and verifying the result.
Its most distinctive feature is persistent background sub-agents. Most tools spin up a new agent for a specific sub-task and discard it afterward. In Muse Code, according to Meta, designated background agents can stay active for the whole session, gathering information, carrying out next steps and reporting back to the main agent when needed. Meta says this reduces repeated searching for the same information and lowers the need for human intervention on complex tasks.
Muse Code also keeps a local event log. Model calls, tool use, user approvals and code changes are recorded in a continuous stream, so if the agent or the machine stops mid-task, the session can resume from where it left off. For long coding jobs, that matters: hours of work should not be lost to a single failure.
Meta ships several prebuilt skills, including one that drafts a full plan and seeks approval before acting, one that critiques that plan, and one that keeps working toward a stated goal until it is met. In one of the company’s demonstrations, Meta ran Muse Spark 1.2 and Muse Code on a GPU kernel optimization task that spanned more than 1,000 tool calls over roughly 24 hours, with the agent writing code, compiling it, measuring performance and iterating on faster versions.
Assistant versus agent
The difference between the old and new approaches is now stark. A traditional coding assistant offered a snippet and left the rest to the developer. A coding agent examines the codebase, locates the problem, makes the change, runs the tests and checks whether the fix actually worked. If it did not, the agent makes the next attempt on its own.
Comparing the three
Declaring an outright winner would be premature. Each product’s current strength points in a different direction:
- Claude Code is strongest in deep agentic workflows on large, real codebases, with team-specific instructions, sub-agents, hooks and MCP integrations. Anthropic has tuned its recent models specifically for long-running agents and software engineering.
- Codex stands out for breadth: the same agent spans terminal, IDE, desktop and cloud, with multiple parallel agents covering the whole software lifecycle.
- Muse Code is new and still in beta, but its persistent background agents, resumable sessions and emphasis on very long tasks represent a distinct bet. Meta says Muse Spark 1.2 was trained alongside Muse Code so the model and the agent harness work well together.
The real contest is no longer about which model writes the best code. The more important question is which system can hold a large goal in mind for longest, understand a whole repository, break work into the right pieces, coordinate several agents, test its own changes and recover from a wrong result without a human stepping in each time.
That shift also says something about the future of programming. Developers may spend less time writing every function by hand and more time specifying problems precisely, setting system design, defining constraints and success criteria, and reviewing the output of several agents. Programming is not disappearing. The job is moving from writing code toward directing the agents that write it, and the competition among Claude Code, Codex and Muse Code is the early form of that era.
