What is an AI coding agent?

An AI coding agent takes a goal in plain language and carries it out inside a real codebase, using tools like the file system, terminal, and version control. It plans the steps, writes and runs code, reads the failures its own output produces, and corrects them. An assistant waits for you to type. An agent executes multi-step work and verifies the result. What it hands back is a diff, which still needs somewhere governed to run. Major is that destination, the place where the generated software gets a database, permissions, and an audit record instead of sitting in a branch.

What is the difference between an AI coding agent and an AI coding assistant?

Trigger and autonomy separate them. An assistant responds to what you type, proposing completions or returning code you copy and run yourself. An agent takes a goal and executes it, editing files, running terminal commands, and checking its own output against the test suite. The practical difference is who executes the code and who observes the failure. Neither one owns what happens after the merge, which is where Major picks up: the app ships to production with SSO, RBAC, storage, and logs already attached.

Are AI coding agents safe to use in production code?

They are as safe as the process around them, and no safer. Google recommends human pull request review on every agent change, scoped guardrails so agents cannot push straight to production, dependency governance against typosquatted packages, action logging for an audit trail, and SAST and DAST scanning. Prompt injection through issue text or dependency docs is the newest risk, because the agent reads untrusted content while holding credentials. Major removes that last part by routing access through a credential proxy, so a compromised prompt cannot exfiltrate a secret the agent never held.

Can an AI coding agent deploy code by itself?

Technically yes, if you give it credentials that reach your deployment pipeline, which is exactly why most teams should not. The deployment path, permission scope, and audit record matter more than generation quality here. Google's first governance recommendation is that agents must not push straight to live environments. Decide what an agent may do at the moment it acts, rather than after the change lands. Major enforces that boundary in the platform, so deploys happen inside scoped permissions with every action recorded.

What is the best AI coding agent?

No single tool wins for every team, so score candidates against your codebase size, language mix, and how much autonomy your review process can absorb. Judge both halves: generation quality plus review workflow, CI integration, deployment path, credential scope, and audit trail. Most published rankings measure only the first half, and several are authored by vendors ranking themselves. Major is not a competitor in that ranking and will not write your pull requests. It is where the code those agents produce becomes governed, deployable, stateful software.

AI Coding Agent: What Happens After the Code Is Written

Every AI coding agent roundup scores the same things: context window, multi-file edits, memory, price. Not one scores what happens after the code exists. That gap is where the real work lives: review, deployment, permissions, audit.

Jason BaoAugust 27, 2026Updated August 28, 2026

Schematic pipeline showing a small code-generation loop feeding a larger stage for review, deployment, permissions, and audit.

The short answer

An AI coding agent takes a goal in plain language and carries it out inside a real repository: it plans the steps, edits files, runs terminal commands, executes the tests, reads what broke, and tries again. Google calls this agentic coding, "a software development approach where autonomous AI agents plan, write, test, and modify code with minimal human intervention" (Google Cloud).

The word doing the work there is "test." A model that emits a function has produced a suggestion. An agent that runs it, reads the stack trace, and rewrites it has produced a change verified against reality. That loop is the category boundary.

What almost no evaluation covers is the half that comes next. The output is a diff. Someone still reviews it, CI still runs it, someone still deploys it, and there should be a record of which agent did what with whose credentials. Generation gets faster every quarter. That half does not.

What an AI coding agent actually is

The defining property is the loop. Google describes a "reason and act" cycle: the agent breaks a request into sub-tasks, then uses real tools, "such as accessing the file system, running bash commands, or interacting with version control systems." Its example: "an agent might write a test case, run the code, observe a failure, and then rewrite the code to pass the test."

Everything else follows from tools plus a feedback signal. Multi-file editing exists because the agent can list a directory and open what it finds. Dependency installation exists because it has a shell. For the wider category, see what an AI agent is.

The contrast with an assistant is trigger and autonomy. An assistant waits on your keystrokes and hands back text. An agent takes a goal and runs until the tests say it is done.

What a coding agent is not

Three modes get called "AI coding" and share almost nothing operationally. Most products ship all three, which blurs the vocabulary.

| Mode | Trigger | Autonomy | | --- | --- | --- | | Inline completion | Your keystrokes | None. It proposes text at the cursor and stops. | | Chat | A question you type | None. It returns code you copy, paste, and run yourself. | | Agent | A goal you assign | It plans, edits files, runs commands, checks its own output, and iterates. |

What matters for procurement is who executes. In the first two rows a human runs the code and sees the failure. In the third the software does, which means permission to touch a file system, a shell, and usually a git remote. That permission is what your security team will ask about, and it appears on no comparison table.

How the agentic loop works in practice

Call it the JPY rounding fix. A ticket says invoice totals are wrong for yen, because the formatter assumes two decimal places everywhere.

Orient. The agent greps for currency formatting, opens the money helper, and finds two call sites in the invoice service and one in the PDF renderer.
Write the failing test. It asserts a JPY total of 1,200 renders with no decimal places, runs the suite, and confirms the test fails for the expected reason.
Change the code. It replaces the hardcoded exponent with a lookup against a currency exponent table, then reruns the suite.
Read the damage. The new test passes and two older ones fail. It updates the one asserting the old wrong behavior, then stops on the second because it encodes a finance rule the codebase cannot confirm.
Hand off. It opens a pull request with the diff, the test output, and a note naming the case it refused to touch.

Steps one through four are what gets benchmarked. Step five is where the cost lives. Who reviews that PR, and how long is their queue? Did the agent hold a token that could push to main, or only open a branch? Six months on, what record shows which agent edited the money helper, on whose instruction? None of it is a model question.

What the roundups score, and the dimension they all skip

Every criterion the top guides use sits upstream of the merge button. Vellum's rubric uses nine: codebase context, multi-step task handling, memory across sessions, security model, language coverage, integration surface, revision quality, pricing clarity, transparency. Augment Code scores architectural reasoning, multi-file accuracy, security posture, speed, and cost. Faros tiers tools by developer sentiment drawn largely from Reddit.

"Security model" there means whether your API keys reach the model, a real question and a narrow one. "Transparency" means whether you can watch the session. Neither addresses review workflow, CI, deployment path, credential scope at the moment of action, or the audit record after a change lands. That is the missing half.

One structural thing shapes what you read here. Both of the best-built roundups on page one rank their own product first on a rubric they authored, and one of those products is an LLM application platform rather than a coding agent. Call it the economics of the format rather than bad faith, but your criteria came from parties with an interest in the result.

Set that against Google's page, which is not selling a rank. Its longest section is a governance checklist: guardrails so agents cannot push straight to production, dependency governance against typosquatting, action logging for an audit trail, human pull request review on every change, prompt-injection monitoring, and SAST and DAST over agent-generated code. The company with the most to gain from selling agentic coding is the one saying the hard part comes after generation. The wider version of this gap is covered in from front-end demos to production.

How to evaluate a coding agent (the full checklist)

One checklist, two halves. The first is well covered by the market. The second is where teams get hurt.

Half one: generation

| Dimension | What to ask | Why it matters | | --- | --- | --- | | Context handling | How does it choose what to read from a repo it cannot fit in context? | Retrieval strategy decides correctness on large codebases more than window size. | | Multi-file editing | Can it change a signature and fix every call site in one pass? | Single-file agents push coordination work back onto you. | | Tool and terminal access | Which commands can it run, and can you restrict the set? | Execution is what makes it an agent, and also the blast radius. | | Session memory | What survives the session: conventions, decisions, prior failures? | Re-explaining the project every task is a tax in tokens and attention. | | Revision quality | When it fixes one test, how often does it break two others? | Net change quality sets review load. | | Model choice | Can you pin, swap, or route models per task? | Model regressions are outside your control unless you can hold a version. |

Half two: what happens after the code is written

| Dimension | What to ask | Why it matters | | --- | --- | --- | | Review workflow | Does output arrive as a reviewable PR with test evidence, or as edits already applied? | Review capacity binds once generation is cheap. | | Rollback | How do you revert an agent change that passed review and failed in production? | Speed of generation raises the rate of changes needing reversal. | | CI integration | Does it read CI results and iterate, or stop at the local suite? | Local green and pipeline green are different claims. | | Deployment path | What stands between a merged agent change and production traffic? | Google's first governance rule: agents must not push straight to live environments. | | Credential scope | What token does it hold while acting, and is it scoped per action? | A broad token makes the agent an unattributable actor with production access. | | Audit trail | Six months on, can you show which agent made this change, on what instruction? | Compliance asks about a merged line, not a chat session. | | Prompt-injection containment | What happens when a fetched doc or issue body contains instructions? | The agent reads untrusted text with tools in hand. |

Build a process around the second table. Taking an agent from prototype to production and control at the point of action go deeper.

Where coding agents break down

They are strong on well-specified work in a codebase with tests, and weak wherever the spec is the hard part. On a large unfamiliar system they confidently produce changes that are locally correct and architecturally wrong, because nothing in the repo encodes why.

The trust data agrees. In the 2025 Stack Overflow Developer Survey, 84% of developers report using or planning to use AI tools, while 33% trust their accuracy and 46% actively distrust it. The top frustration, cited by 66%, is "AI solutions that are almost right, but not quite," and 45.2% say debugging AI-generated code takes more time.

That gap is the honest limit. Generation throughput rose sharply and human review throughput did not move. A team that doubles its merge rate without changing how it reviews has moved the queue rather than shortened it.

Prompt injection is structurally new. The agent reads issue text, dependency docs, and fetched web pages while holding a shell, so instructions hidden in any of that reach a process with credentials. Google pairs monitoring for this with dependency governance so agents install only from approved sources. More in the agent threat model.

One caveat on shelf life. The generation half of this advice ages in months, and today's names (Cursor, Claude Code, GitHub Copilot, Aider, Amazon Q Developer, Windsurf, Replit Agent) will have shuffled by the time you read this twice. The post-generation half ages in years, because review, permissions, deployment, and audit belong to your organization rather than any vendor's release notes.

The Major take

The constraint is easy to state. A coding agent's output is a diff, and a diff is not working software. Review, tests, deployment, credentials, permissions, logging, and audit turn one into the other, and none of that speeds up when generation does. Nothing accumulates either. The agent that worked out how to reconcile a payment file this morning reasons through the same steps tomorrow, at the same cost, with the same variance.

Major starts from a different artifact. It is the enterprise platform where agents build the software they run on, so what an agent produces is a deployed app rather than a snippet, with SSO, permissions, audit, a managed database, and storage handled at the platform layer. That removes the operationalize step instead of speeding it up. Once the app exists, the agent runs it instead of reasoning through the steps again: repeatable work executes as deterministic code, state lives in the app's own database and logs rather than a context window that ends, and the model is spent only on judgment. Cost is front-loaded and then flat instead of climbing with every run. This does not replace the coding agent in your editor. It is an argument about what the finished artifact should be.

The question worth asking about a coding agent is not how much code it writes. It is what the code becomes. Reason once, run forever.

If the workflow you keep re-explaining to an agent is one your team runs weekly, turn that one into an app: the reconciliation step, the tool behind a ticket queue, the reporting job in someone's terminal history. Describe it once and it ships with permissions and an audit trail attached. Get started on Major and build the first app your agent stops re-reasoning.

Frequently asked questions

What is an AI coding agent?: An AI coding agent takes a goal in plain language and carries it out inside a real codebase, using tools like the file system, terminal, and version control. It plans the steps, writes and runs code, reads the failures its own output produces, and corrects them. An assistant waits for you to type. An agent executes multi-step work and verifies the result. What it hands back is a diff, which still needs somewhere governed to run. Major is that destination, the place where the generated software gets a database, permissions, and an audit record instead of sitting in a branch.
What is the difference between an AI coding agent and an AI coding assistant?: Trigger and autonomy separate them. An assistant responds to what you type, proposing completions or returning code you copy and run yourself. An agent takes a goal and executes it, editing files, running terminal commands, and checking its own output against the test suite. The practical difference is who executes the code and who observes the failure. Neither one owns what happens after the merge, which is where Major picks up: the app ships to production with SSO, RBAC, storage, and logs already attached.
Are AI coding agents safe to use in production code?: They are as safe as the process around them, and no safer. Google recommends human pull request review on every agent change, scoped guardrails so agents cannot push straight to production, dependency governance against typosquatted packages, action logging for an audit trail, and SAST and DAST scanning. Prompt injection through issue text or dependency docs is the newest risk, because the agent reads untrusted content while holding credentials. Major removes that last part by routing access through a credential proxy, so a compromised prompt cannot exfiltrate a secret the agent never held.
Can an AI coding agent deploy code by itself?: Technically yes, if you give it credentials that reach your deployment pipeline, which is exactly why most teams should not. The deployment path, permission scope, and audit record matter more than generation quality here. Google's first governance recommendation is that agents must not push straight to live environments. Decide what an agent may do at the moment it acts, rather than after the change lands. Major enforces that boundary in the platform, so deploys happen inside scoped permissions with every action recorded.
What is the best AI coding agent?: No single tool wins for every team, so score candidates against your codebase size, language mix, and how much autonomy your review process can absorb. Judge both halves: generation quality plus review workflow, CI integration, deployment path, credential scope, and audit trail. Most published rankings measure only the first half, and several are authored by vendors ranking themselves. Major is not a competitor in that ranking and will not write your pull requests. It is where the code those agents produce becomes governed, deployable, stateful software.