Use inline code completion for small, local changes; use chat when you need to ask questions or work through a focused task; and consider an agent when the job spans files or needs repository inspection and tool use. The right choice depends on the task, the assistant’s access and permissions, and how much work you’re prepared to review—not on a universal winner.
What is the difference between completion, chat, and an agent?
Inline code completion
Completion offers a suggestion as you write, typically for the next fragment of code. It is a low-friction fit when you already know what you want to implement and can quickly judge the suggestion in the surrounding code. GitHub documents inline suggestions as one way Copilot can assist in an IDE; see GitHub’s IDE chat and interaction documentation.
Chat-based assistance
Chat lets you state a request in natural language—for example, ask for an explanation, a code suggestion, a unit test, or a focused fix. In an IDE, the assistant may use a selected file or code selection as context, and you can ask follow-up questions without leaving the editor. That makes chat useful when you need to express intent rather than accept the next suggested code fragment.
Task-oriented agents
An agent is given a goal and can take multiple steps toward it. Depending on the product and its permissions, those steps may include inspecting repository files, editing one or more files, and running or proposing terminal commands. This can reduce the amount of manual context-gathering for repository-level work, but it also means you need to understand and review a broader set of changes.
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Which workflow fits the task?
| What you need to do | Best starting point | What to check |
|---|---|---|
| Add a small, repetitive code fragment or continue a pattern | Inline completion | Does the suggestion match the codebase, and can you inspect it in context? |
| Explain a function, draft a test, or suggest a focused fix | IDE chat or inline chat | What file or selection context is available? Can you follow up and review the proposed output? |
| Investigate a repository task or change several files | Task-oriented agent | Which files can it access? What commands can it run? Can you redirect it and review the complete diff? |
| Repeat a task from a terminal or connect work to scripts or CI | CLI agent | Check local tool access, permissions, logs, scriptability, and checkpoints. |
| Handle an ambiguous or high-impact change | Start with a plan; keep human review | Clarify acceptance criteria before work begins, then inspect edits and approve commands where the product supports it. |
These are workflow-fit recommendations inferred from documented capabilities, not results of a head-to-head productivity or accuracy test.
How the main interaction patterns work in practice
Stay in the editor for focused questions
GitHub documents Copilot Chat prompts for code suggestions, explanations, unit tests, and fixes. Its IDE experience can use file or selection context and includes inline interaction; GitHub also documents plan and agent modes. See GitHub’s guide to asking Copilot questions in an IDE.
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When a task expands beyond a focused question, GitHub’s agent mode can stream edits, update the working set, and run terminal commands when necessary. GitHub says users can redirect an active agent, review its edits, and confirm or reject proposed commands unless automatic execution is configured. Each agent-mode prompt consumes GitHub AI Credits, and organization administrators may disable agent mode. Those details are product and policy specifics, so check GitHub’s agent-mode documentation for the current configuration that applies to you.
Choose an agent without assuming it must live in a terminal
Anthropic’s FAQ describes Claude Code as able to read repository files, edit files, run commands, and make multi-file edits; it also contrasts the product with autocomplete and reports extensions for VS Code and JetBrains IDEs. These are Anthropic’s product descriptions, not independent measurements. Details are in the Claude Code user FAQ.
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OpenAI describes Codex CLI as a way to work against a local repository, inspect and edit files, run installed tools, select permissions, and use Codex interactively or in scripts and CI. A terminal-oriented agent can therefore fit both hands-on repository work and repeatable automation, provided its permissions and execution environment suit the task. See OpenAI’s Codex CLI documentation.
The IDE-versus-terminal distinction is not absolute. GitHub’s product page says Copilot Pro+ and Enterprise users get Claude and OpenAI Codex access in VS Code and GitHub. This is plan-specific and may change; check GitHub’s Copilot product page for current availability.
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Review and permissions are part of the workflow
Completion and chat usually keep the scope of a suggestion visible as you work. Agents can operate across a larger scope, so judge them not only by what they can do but by what access they have and how their actions are surfaced for review. GitHub documents streamed edits and command confirmation or rejection in agent mode, with automatic execution as a possible configuration. For Codex, OpenAI describes technical boundaries, constrained execution, network policies, explicit handling of higher-risk actions, and agent logs as elements of its deployment approach in “Running Codex safely at OpenAI”. That is OpenAI’s description of Codex, not a general safety guarantee for every coding assistant.
- For a narrow suggestion, inspect whether the code fits the surrounding implementation before accepting it.
- For chat-generated tests or fixes, check that the output addresses the request and behaves as intended.
- For agent work, review the full diff, consider the file scope, and understand any commands the agent ran or requested.
- For ambiguous or consequential changes, set acceptance criteria first and use approval checkpoints where available.
Is one approach more productive or accurate?
The official materials cited here describe product features and controls; they do not establish that completion, chat, or agents are generally more productive or accurate. They also do not provide a named, attributable statistical comparison of developer outcomes. Treat workflow fit as a practical choice based on task size, context needs, integration, permission model, and the amount of review you want to do—not as a proven ranking.
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Product features, credit accounting, administrator controls, and plan entitlements can change. The details above reflect the cited official documentation and product information checked on October 4, 2026.
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