AI-Enabled Coding Interviews (2026): What Changed and How to Practise
In an AI-enabled coding interview you're allowed, sometimes expected, to use an AI assistant while you solve a problem in a realistic codebase. The interviewer isn't grading your prompts. They're grading whether you understand the code, make sound decisions, and catch the AI's mistakes before they ship. Companies are adding these rounds alongside classic no-AI rounds, so in 2026 you have to prepare for both.
Updated 17 October 2026
What is an AI-enabled coding interview?
It's a live coding round where an AI assistant is part of the environment. Usually you work on one extended problem in a small codebase instead of a standalone puzzle. You read existing code, fix a bug, then extend it. The AI can explain, suggest and draft, but the decisions and the final code are yours.
Two well-documented examples:
- Meta's AI-enabled coding round. Meta began rolling it out in October 2025, replacing one of the onsite coding rounds with a 60-minute AI-enabled session. The AI can only respond in a chat panel and can't edit your files directly. The round has three progressive phases built around a single extended problem: bug fixing, core implementation, then optimization. (Source: Hello Interview, Evan King)
- CodeSignal's agentic assessments. CodeSignal has launched an interview format built around agentic coding tools such as Claude Code, Cursor and Codex. (Source: Interview Query)
Adoption is uneven. Karat's 2026 survey of 400 engineering leaders found that 62% of organisations still prohibit AI in technical interviews, even though leaders estimate more than half of candidates use it anyway. CoderPad's data shows a different split: 34% of hiring teams ban AI in assessments and 46% allow it with constraints. (Sources: Interview Query on Karat; CoderPad via the same coverage.) Either way, your loop may contain both kinds of round.
What do interviewers grade?
According to Hello Interview, Meta evaluates the AI-enabled round on the same four competencies as its traditional coding rounds:
- Problem solving: breaking the task down and choosing an approach.
- Code quality: code you can explain and defend, that fits the existing codebase.
- Verification: testing what you, and the AI, wrote, and finding and fixing what's wrong.
- Communication: explaining your reasoning and your decisions about the AI as you go.
An internal source quoted in the same piece sums it up: you "should use AI, but need to show you understand the code."
Not on the list: prompt engineering. You can be marked down for using the AI as a crutch. The candidates who struggle are those who accept AI output without reading it, and the model may be less capable than the one you use daily. One candidate account describes an assistant repeatedly hallucinating on a maze problem mid-round. (Source: Hello Interview, candidate reports)
What's different from a classic coding round?
| Classic coding round | AI-enabled round | |
|---|---|---|
| Starting point | Blank editor, one function | An existing codebase with tests |
| First task | Solve the puzzle | Often: read the code, find and fix a bug |
| Main skill shown | Algorithm design | Judgement: what to delegate, what to verify |
| Typical failure | Can't find the approach | Ships AI-written code that's subtly wrong |
| How you prove it works | Walk through examples | Run the tests, read the failures, fix them |
How to practise for AI-enabled interviews
1. Keep your unaided skills sharp first
Classic rounds aren't going away, and you can't verify code you couldn't have written. Solve a problem on your own, then redo it with the AI.
2. Practise in a codebase, not a single file
Pick problems with an existing interface and a test suite you didn't write. The skill being tested is fitting into code someone else designed.
3. Make verification a habit
For every AI suggestion:
- Read it line by line before you accept it. Could you explain each line to the interviewer?
- Run it against the tests straight away. Don't stack three unverified changes.
- Probe the edges: empty input, concurrency, limits. AI drafts are weakest there.
- Check the APIs: hallucinated methods and wrong signatures are the most common AI bug.
4. Narrate your decisions
Say why you're delegating something ("boilerplate, I'll let the assistant draft it") and why you're not ("this eviction logic is the core of the problem, I'll write it"). That narration is the "judgement" part of the score.
5. Time-box the AI
If two prompts haven't produced something you understand, stop and reason it through yourself. Spinning on prompts is the most expensive way to lose a 60-minute round.
Practise on Cruscible
Cruscible's low-level design problems have the same shape as these rounds:
- An existing contract. Each problem gives you an interface to implement inside a project structure, not a blank page.
- A test suite you didn't write. Your submission runs against a graded suite in an isolated container, against real PostgreSQL 16, Redis 7 or a key-value store.
- Feedback beyond pass or fail. Each submission is scored on functional correctness, performance (1–20 ms p99 targets, depending on the problem) and code quality, so you can compare how code you wrote and code an AI drafted actually perform.
- Five languages: Java, Python, Go, Node.js and TypeScript.
A practice routine that works: solve the problem once unaided. Then solve it again with your AI assistant open in another window, submit, and use the result to practise the verification step. Compare the two scores.
Practice rounds built by your own AI agent. Connect Claude Code, Cursor or Copilot under Settings → AI Agent. Your agent reads your résumé locally, on your own AI plan, and builds a timed practice round for you from Cruscible's problem bank. Cruscible never receives the résumé.
Start with these, which come up often in both machine coding and AI-enabled rounds:
- LRU cache: small, but full of edge cases AI drafts miss
- Sliding-window rate limiter: concurrency and time windows
- Circuit breaker: a state machine where "looks right" isn't right
- Deployment pipeline with rollback: the expert-level codebase-style problem
Browse all LLD problems → · Applied DSA problems →
Frequently asked questions
Is using AI in a coding interview cheating?
Only if the company says no. Many still prohibit it, and some explicitly allow or require it. Ask your recruiter which rounds allow AI, and follow those rules exactly.
Which AI tool will I get?
Usually the company's choice, not yours, and it may be weaker than what you use daily. Practise verifying output rather than depending on one model's quality.
Do I still need to practise LeetCode-style problems?
Yes. Most loops still include at least one no-AI round. Treat AI-enabled practice as an addition, not a replacement.
How is this different from a machine coding round?
A machine coding round tests whether you can build a working system yourself in 60–120 minutes. The AI-enabled round tests whether you can steer, check and fix code inside an existing system with an assistant. They train overlapping skills. See our machine coding guide.