What is a CLI?
Data, integration and interfaces
CLI stands for Command Line Interface. It is a text-based way to operate software by entering commands in a terminal or shell instead of clicking through menus or dashboards. A CLI can be used to run tools, configure services, automate repetitive tasks, and reproduce operational steps in a script. In practice, teams rely on CLIs because they are fast, precise, and easy to automate. That does not make a CLI superior for every user. It makes it especially useful where repeatability, scripting, and operational control matter.
What this means
A CLI is less about appearance and more about control. Rather than navigating a graphical interface, a user types commands and options directly, sees output, and can often pipe that output into other tools. That makes CLIs common in infrastructure work, cloud administration, build systems, package management, testing, and developer tooling.
For leaders who rarely use a terminal, the important point is that a CLI turns an action into something that can usually be repeated exactly. If a colleague can show the command that created a resource, generated a report, or deployed a change, that action is easier to review, script, document, and automate than "click through the console and do it again".
Why it matters
CLIs matter because operational work becomes more reliable when common actions are explicit and repeatable. A typed command can be copied into a runbook, checked into a script, called from a pipeline, or reviewed by another engineer. A sequence of clicks inside a web console is harder to capture and easier to perform differently each time.
This is especially useful for teams managing cloud resources, build jobs, SDK packages, test runs, deployments, and data tasks. A CLI can reduce friction for experienced operators and make automation feasible. It can also create a better handover path when knowledge needs to move from one person to another.
For AI-enabled systems, many of the surrounding tools are CLI-driven too: model packaging, evaluation jobs, environment setup, batch processing, and infrastructure changes. Even when the end product is graphical, the operational layer often is not.
How it works
A typical CLI is built around a command structure: a main executable, a subcommand, and then arguments or options. The user runs the command in a terminal, the CLI performs an action, and the result is returned as text or machine-readable output. Because commands are text, they can be placed in scripts and executed again later in the same form.
This is why CLIs connect naturally to automation. A team can run a build command locally, then call the same command in CI/CD. It can use a cloud CLI to inspect resources, a package CLI to pin a version, or an infrastructure CLI to generate a reviewed plan before apply.
In many software ecosystems, the CLI, SDK, and API sit together: the API provides the service contract, the SDK helps write integrations, and the CLI helps a person or script operate the service directly.
Examples
An operations engineer might use a cloud CLI to inspect a production resource, a package CLI to update a dependency, and an infrastructure CLI to generate a plan for a network change. Because those actions are explicit commands, they can be pasted into a change record or turned into a script after review.
A development team may rely on a CLI to create a new project scaffold, run tests, build an artefact, and publish it through the same toolchain that CI/CD later uses. That keeps local and automated workflows closer together.
In an AI workflow, a team might use CLIs to prepare data, launch batch evaluations, configure environments, install SDK packages, or deploy revised infrastructure for a model-backed service. The value is not that typing commands is glamorous. The value is that the steps can be repeated and documented.
A support team might also use a CLI to gather diagnostics or restart a controlled service in the same way every time. That consistency reduces guesswork during incidents and makes post-incident review easier because the exact steps are easier to reconstruct.
Common misunderstandings
One misunderstanding is that a CLI is old-fashioned and therefore inferior to a graphical interface. In practice, the two solve different problems. Graphical tools can be more accessible for occasional users and better for visual exploration. CLIs are often better for speed, scripting, and exact repeatability.
Another misunderstanding is that a CLI is inherently insecure. It is not. Risk comes from how it is used: copied commands no one understands, over-privileged accounts, secrets left in shell history, destructive commands run in the wrong environment, or scripts that nobody reviews.
Risks and boundaries
CLI use can become risky when convenience outruns control. Credentials may be stored insecurely on laptops or left in environment variables. Commands may be pasted from old tickets or internet posts without understanding what they do. A script may target production because a context flag was wrong. High-impact tools may run under accounts with far more privilege than the task requires.
Auditability is another concern. A command typed on a workstation is not always captured in the same way as an action performed through a managed platform or pipeline. That is why important operational work often needs logging, privileged access controls, and clear rules about which tasks should be done manually and which should be run through reviewed automation.
What to do next
Leaders should ask where CLIs sit in their operating model. Which critical tasks are performed through command-line tools? Which of those are manual, and which are wrapped in scripts or pipelines? Where are credentials stored? How are privileged commands approved, logged, and restricted?
Where the blast radius is high, move from individual memory to documented workflows. A reviewed script, a runbook, or a CI/CD job is often safer than expecting engineers to recall complex commands under pressure. At the same time, avoid banning productive tooling simply because it looks unfamiliar to non-specialists. The better question is whether the command-line workflow is controlled, observable, and proportionate to the risk.
Where the blast radius is high, prefer commands that are wrapped with confirmations, safer defaults, or environment checks instead of relying on operator memory alone. That is usually easier to govern and train new staff around.
FAQs
Is a CLI only for developers?
No. Developers use CLIs heavily, but so do platform engineers, security teams, data teams, support engineers, and administrators. The common factor is not job title. It is the need to run precise, repeatable tasks efficiently and often automate them later.
If a vendor has a GUI, why would a team still use the CLI?
Because the CLI is usually better for repeatable work. Teams can script it, document exact steps, use it in pipelines, and avoid drifting through a set of manual clicks. GUIs remain useful for discovery and occasional tasks, but CLIs are often better for operational consistency.
Should sensitive production actions be done through a CLI?
Sometimes, yes, but with safeguards. High-impact commands may still be appropriate through a CLI if the organisation controls identity, device trust, logging, approval, and environment targeting carefully. In many cases, the safest pattern is to use a CLI inside reviewed automation rather than as an unrecorded one-off action.
Sources
Risk manage administration using tiers (UK NCSC). Privilege tiers and the impact of administrative compromise.
Protect your administration interfaces (UK NCSC). Trusted admin devices, MFA, secure protocols, privileged access, and credential protection.
Principle 7: Ensure all connectivity is logged and monitored (UK NCSC). Logging, monitoring, anomaly detection, and change visibility for high-impact connectivity and administration.