How to Choose an AI Coding Agent for Your Team: A Buyer's Guide

To choose an AI coding agent for your team, compare six things: the interface your developers will use, which models and plans it needs, where the code runs, what it asks before acting, what admins can lock down, and how you check its changes for security problems. Run a short pilot on real work, and record defaults, not demos.

If you are thinking of switching, Safeguard is an alternative that does not make you give up what you already pay for. Bring the Claude, ChatGPT or Codex plan you already pay for and use it inside the Safeguard CLI. Safeguard adds no extra charge for that usage; your plan's own usage limits and terms still apply. You can also connect Claude or ChatGPT to the Safeguard MCP server and use Safeguard from the chat your team already uses. Either way you get an assistant that writes and fixes code on your machine with your approval, knows your vulnerabilities, and checks what any agent changed before you merge.

Below: the criteria, eight common tools compared from their own docs and licence files, a two week pilot plan, and common buying mistakes. No rankings; every third party fact links to a page we read on 21 October 2026.

Six criteria that decide the choice

Most teams start by asking which agent writes the strongest code. That answer shifts every few months as models change. The questions below change less often and decide whether a tool fits your team.

1. Interface: where developers will meet it

Coding agents now ship as terminal tools, IDE extensions, desktop apps, web apps, mobile clients and cloud agents that open pull requests on their own. Many vendors offer several of these. Ask:

• Which surfaces do your developers already live in? A team that works in JetBrains IDEs needs to check JetBrains support specifically.
• Do the surfaces share settings? Claude Code's docs, for example, say each surface "connects to the same underlying Claude Code engine," so project instructions, settings and MCP servers carry across.
• Is there a non-interactive mode for CI? You will want one for scheduled jobs and pipeline checks.

2. Models and plans: what you pay for and who you depend on

Some agents use one vendor's models. Others let you pick from many providers or run local models. Ask:

• Which model providers does it support, and can an admin restrict them?
• Does it work with a subscription people already have, or only with API keys?
• Do the provider's terms allow that subscription to be used in this tool? OpenCode's docs, for example, say "There are plugins that allow you to use your Claude Pro/Max models with OpenCode. Anthropic explicitly prohibits this."
• What happens to cost when context windows or reasoning levels go up? GitHub's Copilot CLI docs note that larger context windows or higher reasoning levels consume more.

3. Where code runs: laptop, cloud, or both

This is the question security teams care about most. A local agent runs commands on the developer's machine with the developer's access. A cloud agent clones your repository into a vendor managed environment. Ask:

• Does the agent run on the laptop, in the vendor's cloud, on your own hardware, or a mix?
• What goes to the model provider? Even a local agent sends prompts and file contents to the model unless you use a local model.
• For cloud agents, what network access and secrets does the environment get?

4. Approvals: what it does without asking

Defaults matter more than features. Two agents can produce the same diff, while one asked before every command and the other asked before none. Ask:

• What is the default for file edits, shell commands and network access?
• What is the most permissive mode, and can it be turned off?
• Is there an OS level sandbox, and is it on by default?
• Are some paths protected even in permissive modes, such as .git?

5. Admin controls: what the organisation can enforce

A developer's own settings are preferences. Managed settings are policy. Ask:

• Can admins deploy settings that users cannot override?
• Can admins restrict sign-in to the company's own organisation or tenant?
• Can admins choose which models or providers are allowed?
• Is there SSO, provisioning and an audit log, and on which plan?

6. Security: what checks the output

None of the agents in the table below document vulnerability matching for the packages they add. Ask:

• How will you know if an agent pulls in a dependency with a known vulnerability?
• Do agent changes arrive as pull requests with required review, or as direct commits?
• Who reviews MCP servers and tools before an agent can call them?
• Do you have a before and after scan for every agent session that touches dependencies?

Common tools compared on the facts

The tables cover eight widely used tools. "Not covered" means we did not find it in the docs we read, not that it is missing. These products change often, so recheck before you buy.

Interface, licence, models and sign-in

Where code runs, approvals and admin controls

Notes on each tool

Claude Code. Anthropic describes it as "an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools." Admins can set forceLoginMethod and forceLoginOrgUUID in managed settings so logins must belong to your organisation, and allowedProviders to limit which services a machine may reach Claude through. Before rollout, note the docs say to use bypassPermissions only "in isolated environments like containers or VMs."

Codex. "Codex CLI is a coding agent from OpenAI that runs locally on your computer." Sandbox modes are read-only, workspace-write and danger-full-access, and the docs recommend read-only for folders not under version control. API key sign-in has no cloud based features such as GitHub code review.

Gemini CLI. Google's README calls it "an open-source AI agent that brings the power of Gemini directly into your terminal." Approval modes are default, auto_edit and plan, and YOLO can only be turned on from the command line. Read the README notice first: for unpaid tier and Google One users, Gemini CLI was replaced by Antigravity CLI on 18 June 2026.

Cursor. Cursor's CLI runs as agent with Agent, Plan and Ask modes. Cloud agents run "in isolated VMs in the cloud with full development environments," work on a separate branch and can open pull requests. Many admin controls, such as SCIM, audit logs and model access restrictions, are listed for Enterprise.

GitHub Copilot. Copilot CLI asks before it uses a tool that can change or execute files, and you can approve once, approve for the session, or reject. The docs warn that with --allow-all-tools, "Copilot has the same access as you do to files on your computer." Copilot CLI can also connect to your own OpenAI compatible, Azure OpenAI or Anthropic provider.

OpenCode. OpenCode calls itself "the open source coding agent" and is MIT licensed. Its default build agent has full access and its plan agent is read only. Set permissions on purpose, since most default to allow.

Aider. Aider is "AI pair programming in your terminal," Apache 2.0, and commits changes to git with generated messages so you can review and undo with normal git tools. Check activity before you standardise on it: its latest GitHub release is v0.86.0 from August 2025, and the repository was last pushed in May 2026.

Cline. Cline describes itself as "the open source coding agent in your IDE, terminal, & desktop." In Act mode, "Every file edit and terminal command requires your approval" unless you turn on auto-approve. The JetBrains plugin is not open source, so check which build your team installs.

Run a two week pilot

A pilot answers the six questions on your own code. Here is a plan that works for most teams of five to fifty developers.

Step 1: write down what you are testing

Pick two or three tools plus your current setup as a baseline. Put the six criteria in a scorecard with a column per tool, and decide in advance what "pass" means, such as "Shell commands ask before running by default."

Step 2: choose real tasks

Pick five to ten backlog tasks that represent normal work: a bug fix, a dependency upgrade, a test for untested code, a refactor across a few files. Avoid toy tasks.

Step 3: record the defaults before anyone changes them

Install each tool fresh and write down:

• the approval mode it starts in,
• whether a sandbox is on,
• which model it uses,
• what settings file it reads and whether a managed one can override it.

This is the configuration most of your developers will actually run.

Step 4: scan before and after every session

A line by line review will not show that a new package version has a known vulnerability. Run a scan before and after each session with the Safeguard CLI:

safeguard scan --dir .
cp output/vuln.json vuln-before.json
# run the agent session under test here
safeguard scan --dir .
diff vuln-before.json output/vuln.json

Anything new in the second file came from the session. Log it on the scorecard against the tool and the task.

Step 5: test the admin controls

Have one person act as admin. Try to lock the approval mode, restrict providers, and restrict sign-in to your organisation, using only what each vendor's docs describe. Then, as a normal user, try to undo each setting. Write down what held.

Step 6: decide with the scorecard

After two weeks you have facts: defaults, admin controls that held, new vulnerabilities per task, and developer feedback. Teams often keep more than one tool. That works if every agent's output goes through the same security check.

How Safeguard fits: do the work properly with the Safeguard CLI

Safeguard fits in two ways. You can switch to it as your coding assistant, keeping the plan you already pay for. Or you can keep any agent from the tables and use Safeguard as the check every change passes through. Many teams do both.

The Safeguard CLI scans code, container images and SBOMs on your own machine, sends only the results to the platform, and includes an assistant that can fix what it finds. It writes code, not only scans.

Install

macOS and Linux:

curl -fsSL https://cli.safeguard.sh/install | bash

Windows, in PowerShell:

irm https://cli.safeguard.sh/install.ps1 | iex

It is a single static binary, with no Node, Python or JVM needed. It runs on macOS, Linux and Windows, on x86_64 or arm64 (Windows is x86_64 only).

First session

1. Run safeguard with no arguments in your project to open the interactive terminal.
2. Type /login to sign in through the browser.
3. Type /scan . to scan the current folder.
4. Ask in plain language, for example "how many critical vulns do we have".
5. Use @ to attach a file from the folder you started in, and /resume to pick up the last session there.

Writing and fixing code

The assistant has tools for your local files and for your Safeguard data, and it picks whichever the question needs. Ask "fix the lodash CVE in this repo" and it edits the code and runs commands, with your approval. It also works as a general terminal assistant on your machine: find and read files, edit code, run commands, plus everything security.

Use your Claude, ChatGPT or Codex plan

Bring the Claude, ChatGPT or Codex plan you already pay for and use it inside the Safeguard CLI. Safeguard adds no extra charge for that usage; your plan's own usage limits and terms still apply. Type /model to see or switch the model, and see the CLI docs for connecting one.

Teams that live in a chat app can stay there. This demo connects Claude to the Safeguard MCP server with OAuth and chooses which tools Claude can use.

Click through it at your own pace, or open it full screen.

Approvals and admin controls

Tools that change things, such as writing a file, editing code or running a command, ask before they act. /permissions sets the mode:

• Ask prompts before every action.
• Auto approves known actions and asks for anything new. This is the default.
• Yolo approves everything.

On the platform side, admins decide which models people may use. This demo shows choosing the model for each conversation: Zero z20 answers with no language model at all by turning the question into a platform query, Griffin, Eagle and Lion are Safeguard's models, a tenant admin chooses which model families people may use, and you can bring your own model on your own provider account and route jobs such as chat or summaries to it.

Click through it at your own pace, or open it full screen.

CI with safeguard exec

safeguard scan --repo https://github.com/your-org/your-repo
safeguard scan --image your-image:latest
safeguard validate sbom.cdx.json
safeguard exec "summarise the critical findings in this repository"

Scans write JSON files such as vuln.json and license.json under ./output. safeguard validate checks CycloneDX, SPDX and VEX documents, and safeguard exec runs one non-interactive assistant turn. Pass or fail decisions come from policies and gates, which are evaluated in the Safeguard platform, not by the CLI. The platform also offers reachability analysis and autonomous remediation with fix pull requests, which is off by default. When the local runner handles platform scan jobs, your source code never leaves the machine; only SBOMs and findings are uploaded.

The same platform is on every surface on the download page: the CLI, a desktop app for Windows, macOS and Linux, iOS and Android apps, IDE and browser extensions, and cloud options including a sandbox tenant that is free for seven days with no card. For more, read how to check what a coding agent changed and our comparison of AI coding agents.

Common mistakes when buying

Choosing on a demo. Demos show the happy path in the vendor's chosen configuration. Your developers will run the default. Score the default.

Assuming the sandbox is on. Gemini CLI's sandbox is opt-in. Cursor's own docs say its Auto-review mode "is not a security boundary." Turn on what you need, then test that a write outside the project fails.

Letting individuals pick the permissive mode. Most agents have a mode that approves everything. Claude Code, Codex, Gemini CLI and Cursor document ways for admins to restrict it. Use them before rollout, not after an incident.

Connecting a subscription the provider has not approved. Read the model provider's terms before connecting any plan to any tool. OpenCode's own docs flag Anthropic's position on Claude Pro and Max plans in third party plugins.

Forgetting cloud agents need their own review. A cloud agent that opens pull requests is easier to audit than direct commits, but only if branch protection requires a human review.

Reviewing code but not dependencies. Diffs do not show known vulnerabilities in the versions an agent pulled in. Scan before and after.

Ignoring maintenance. For open source tools, check the latest release and last push before you write team guidance around one.

FAQ

What should a team look for in an AI coding agent?

Six things: the interface your developers use, the models and plans it needs, where code runs, approval defaults, admin controls, and how you check its output for security problems. Score the defaults in a pilot on real tasks.

Which AI coding agents are open source?

From the licence files we checked: Codex CLI, Gemini CLI, Aider and Cline are Apache 2.0, and OpenCode is MIT. Cline's JetBrains plugin is not open source. Claude Code's licence file reserves all rights, Copilot CLI's licence forbids modification, and Cursor's public repository does not contain its source.

Can I switch to Safeguard and keep my Claude, ChatGPT or Codex plan?

Yes. Bring the Claude, ChatGPT or Codex plan you already pay for and use it inside the Safeguard CLI. Safeguard adds no extra charge for that usage; your plan's own usage limits and terms still apply. You can also connect Claude or ChatGPT to the Safeguard MCP server and use Safeguard from that chat.

Do local agents keep my code off the cloud?

They run commands on your machine, but they send prompts and relevant file contents to the model provider unless you use a local model. With the Safeguard CLI, when the local runner handles platform scan jobs, your source code stays on the machine and only SBOMs and findings are uploaded.

Which agents ask before running commands by default?

Claude Code in Manual mode, Gemini CLI in default mode, Copilot CLI, and Cline in Act mode ask by default. Codex's Auto preset runs inside the workspace sandbox and asks before acting outside it. The Safeguard CLI's default, Auto, approves known actions and asks for anything new.

Can admins restrict which models developers use?

Several vendors document it. Claude Code has allowedProviders in managed settings, Cursor lists model access restrictions on Enterprise, and in Safeguard a tenant admin chooses which model families people may use.

Does Safeguard replace the agent we already use?

It is not a feature for feature copy of any tool here. You can switch to it to write and fix code with an assistant that knows your vulnerabilities, or keep your current agent and use Safeguard to scan what it changed before merge.

Try it yourself

This demo sets up the runner on your own machine: pick the platform, start it, approve it with its device code, then use the same CLI to scan from the terminal.

Click through it at your own pace, or open it full screen.

Install from safeguard.sh/download and read the CLI docs. If open source agents are on your shortlist, see our guide to OpenCode alternatives.

Quick check

Sources

• Safeguard CLI documentation
• Safeguard CLI interactive terminal
• Safeguard download page
• Claude Code overview
• Claude Code authentication
• Claude Code permissions
• Claude Code licence file
• Codex agent approvals and security
• Codex pricing and plans
• Codex repository
• Gemini CLI repository
• Gemini CLI sandbox
• Gemini CLI configuration
• Cursor CLI overview
• Cursor run modes
• Cursor cloud agents
• Cursor enterprise
• Cursor models
• Cursor public GitHub repository
• About GitHub Copilot CLI
• GitHub Copilot plans
• GitHub Copilot CLI licence
• OpenCode repository
• OpenCode providers
• OpenCode permissions
• Aider repository
• Cline repository

Last reviewed: 21 October 2026.