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For most engineers, this is not an either-or choice. Keep the IDE that supports your language, navigation, debugging, refactoring and project tooling; add AI assistance where you can check its output efficiently. Use more autonomous agents only for clearly scoped work with reviewable changes. Whether AI helps your team is something to measure, not assume.
Are AI coding assistants a replacement for an IDE?
No. An IDE provides the working environment: editing, code navigation, refactoring, debugging and access to project tools. An AI assistant adds ways to generate, explain or change code, and it may run inside that same environment. The practical choice is usually how much AI help and autonomy to add—not which of two mutually exclusive categories to keep.
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AI features vary by product and version, so labels such as “chat” or “agent” do not guarantee identical capabilities. Microsoft’s description of Copilot agent mode, for example, says a developer can intervene, review edits and undo changes while the agent works; that is a product-specific description, not a guarantee about every agent: Microsoft’s May 19, 2025 overview.
What are the main ways to use AI while coding?
| Mode | How it works | Best suited to | What the engineer must check |
|---|---|---|---|
| Inline assistance | Suggestions appear as you edit; you accept, reject or modify them. | Small, local drafts or completions where the surrounding code is clear. | Whether the suggestion matches the code’s intent, conventions and behavior. |
| Chat assistance | You ask a question or request an explanation or draft, then decide what to incorporate. | Explaining unfamiliar code, brainstorming, or preparing a first draft. | Whether the answer fits the actual repository and is correct before you rely on it. |
| Agentic assistance | You give a broader task; the agent may plan and edit across files while you supervise. | Work with explicit acceptance criteria and changes that can be inspected as a diff. | The plan, every relevant change, project checks and the final result—not just whether the agent reports success. |
These are interaction patterns, not universal product specifications. Confirm what a particular tool can access and what controls it provides in your current environment.
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Does AI coding assistance actually make developers more productive?
The evidence supports neither a blanket productivity guarantee nor the claim that AI has no effect. Adoption, self-reported benefit and measured workflow behavior tell different parts of the story.
Adoption is high in one large 2026 survey
JetBrains’ Developer Ecosystem Survey 2026 reports that 90% of more than 15,000 professional developers surveyed worldwide used AI coding agents at work at least weekly, and 68% used them daily during May–July 2026. The report defines its professional-developer population and describes regional quotas and statistical reweighting. These are survey estimates for that population and period, not a census of all engineers—and adoption does not establish effectiveness.
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Telemetry shows changed behavior, not a direct measure of value shipped
In a two-year observational study, JetBrains Research analyzed anonymized IDE activity from 800 developers—400 AI Assistant users and 400 non-users—from October 2022 through October 2024, alongside a 62-person survey and interviews. In its 2026 report, the AI-user group’s monthly typed characters rose by nearly 600 per developer on average, compared with about 75 among non-users. More than 80% of surveyed AI users also reported a slight or significant productivity increase. The typing result is a behavioral signal, while the survey result is self-reported; neither establishes that AI caused more valuable work to be delivered. See JetBrains Research’s study and methods.
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Perceptions and telemetry do not line up neatly. Nearly half of survey respondents perceived some improvement in code quality and about 10% perceived a decline. For readability, 43.5% reported an increase, 6.5% a decrease and half no change. The behavioral measures cannot settle code quality on their own, and perceptions do not substitute for measured defects or maintainability. Read the findings as evidence that workflows and experiences changed, not as a single verdict on code quality.
Published productivity results are task-specific
A JetBrains summary of a systematic review covers 90 studies first made public between January 2022 and November 2024. The review’s categories overlap: 74 studies addressed impact, 28 design and 19 code quality; GitHub Copilot was the subject of 36. Only 13 of the 74 impact studies measured productivity. One controlled task found Copilot users built a JavaScript HTTP server up to 55.8% faster; other studies reported 26–35% gains on more complex multi-file proprietary tasks. These are outcomes in particular study settings, not a forecast for an engineer’s everyday work. The review also says checking suggestions, refining prompts and reworking generated code took up to half of a developer’s time in studies reporting that cost, and warns that plausible-looking suggestions can contain errors. Its evidence base largely predates today’s agent landscape, so it cannot settle how current agents compare. Details are in JetBrains Research’s review summary.
Which coding tasks should you give to an AI assistant or agent?
Start with tasks whose output is easy to inspect and whose failure is easy to catch. A survey of 481 programmers examined feature implementation, test writing, bug triage, refactoring and natural-language artifacts. Participants identified tests and natural-language artifacts among work they would like to delegate. The study also identified trust, company policy and insufficient context about project size as reasons developers did not use assistants. That points toward evaluating tasks individually rather than adopting or rejecting AI categorically; see Sergeyuk et al.’s study.
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- Drafting tests: check that cases reflect the specification, include relevant edge conditions and pass in the project’s normal test environment.
- Explaining unfamiliar code: compare the explanation with the actual implementation and documentation before using it to make a decision.
- Documentation and other natural-language artifacts: verify that descriptions match current behavior, interfaces and team conventions.
- Small, bounded changes: use a precise request and inspect the diff before accepting the work.
Use more caution as scope and consequences grow
Feature work, refactoring and bug triage can involve dependencies and conventions that are not obvious from a short prompt. For multi-file or agent-led changes, define acceptance criteria, inspect the plan and diff, and run the project’s tests and other checks. If you cannot tell whether the result is correct, the task is not meaningfully reviewable yet; narrow it or keep the work in the conventional workflow.
Best Value
How should an engineering team choose and evaluate its setup?
Compare actual tools and workflows against the work your team does, rather than choosing from marketing labels. A small pilot can show whether assistance saves time after review and rework are included.
- Keep the IDE fit as the baseline. Confirm that the environment supports your language, repository navigation, debugging, refactoring and required project tooling.
- Choose a bounded task and an interaction mode. Begin with a reviewable task such as a test draft or code explanation; move to an agent only when scope and acceptance criteria are clear.
- Check context and control. Determine whether the tool can use the relevant repository and conventions, and whether you can inspect changes, interrupt work, run checks and undo edits.
- Review policy before sharing work. Confirm that the task and any data involved are permitted by your organization. Trust and company policy were reported barriers in the 481-programmer study.
- Measure the whole task. Track completion time alongside defects, rework, review burden and maintainability. Do not treat generated characters, adoption or self-reported speed as a sufficient success measure.
- Check the current service terms. Before selecting a specific product, verify its present environment support, model or plan details, privacy, retention and data-use terms in official vendor documentation; these details can change.
What is the practical recommendation?
Keep the IDE that already handles the project’s core engineering work. Add AI assistance where its output can be checked against code, tests and team conventions; reserve broader agent autonomy for bounded tasks with inspectable changes and a responsible human reviewer. Decide whether to expand use based on your team’s results, including defects and review time—not on adoption figures or generated code volume alone.
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