A practical framework for engineering and talent leaders — grounded in what leading organizations are actually doing.
The problem with your current process
Your technical interview was built to assess a job that no longer exists.
For the last 15 years, the standard architecture looked like this: a recruiter screen to qualify logistics, a take-home assessment to filter on coding ability, and a series of technical interviews evaluating algorithmic knowledge and code correctness. It was a reasonable proxy for a role defined by writing functionally correct code from a detailed spec.
That role has fundamentally changed. AI can write functionally correct code from a spec faster and more reliably than most engineers. What organizations need now is engineers who can direct AI agents effectively, evaluate what they produce, and build on top of it, not engineers who can out-code a model in 45 minutes without assistance.
The interview process hasn’t kept up. This guide outlines how to change that.
What you’re actually trying to assess
Before redesigning your process, get explicit about what signals you’re optimizing for. In the agentic era, the two pillars worth building toward are:
Software engineering fundamentals. Data structures, algorithms, system design, code quality, not because candidates will be writing everything from scratch, but because strong fundamentals are what allow someone to evaluate what AI generates, catch what’s wrong, and direct it more precisely. Fluency and fundamentals reinforce each other. Engineers who lack fundamentals tend to over-trust AI output. Engineers with strong fundamentals tend to use AI more effectively.
AI fluency. Not tool familiarity — judgment. Does the candidate prompt with clarity? Do they verify output or accept it? Can they identify where the model went wrong? Can they articulate why? This is a harder signal to assess than code correctness, which is exactly why it requires a different kind of interview.
Redesigning the funnel: three stages
Stage 1: Compress the top of funnel with an AI screen
The traditional recruiter screen + take-home assessment is a two-step process spread across days, often with a third step to review the take-home before scheduling interviews. This is expensive in time and introduces a gap between when a candidate applies and when they get real signal.
An AI-powered screening round, like HackerRank Chakra, compresses all three into one session. The candidate completes a conversational screen that covers:
- Role logistics: work authorization, location, start date, compensation alignment
- Fundamental evaluation: a hands-on coding question, conducted conversationally. The AI interviewer asks the candidate to walk through their approach before writing code, digs into reasoning when the candidate responds, and probes edge cases when they finish
Because the evaluation is dynamic rather than static, it’s harder to game than a traditional take-home. A candidate can submit AI-generated code. They cannot as easily demonstrate understanding of what they submitted when the interviewer follows up.
What this replaces: recruiter screen, take-home assessment, and take-home review — typically reducing three scheduling events to one.
What this does not replace: human-led interview rounds. The AI screen produces an interpretable report with scoring rationale and transcript highlights. It is a recommendation, not a decision.
On compliance: Any AI screening tool operating in EMEA needs to meet data privacy requirements, conduct bias audits, and ensure that no automated rejection is possible without human review. Chakra is designed with these constraints as a baseline; all scores are accompanied by citations explaining how they were reached, and human review is a required step in the workflow. Organizations in Europe should verify that any AI assessment vendor they work with can demonstrate bias auditing and GDPR compliance, not just assert it.
Stage 2: Human-led interview — Plan/Build/Review
The human-led interview is where AI fluency gets evaluated. The framework increasingly adopted by HackerRank customers is built around three phases that mirror how AI-assisted engineering actually works.
Plan
The candidate receives a real-world task in a code repository, not a contrived algorithm, but a task representative of the actual role. Before writing any code, they walk through their approach: How do they break down the problem? What do they clarify before starting? What constraints do they surface? What would they prompt the AI agent to do first?
The plan phase is where engineering judgment shows up earliest. A candidate who asks clarifying questions, identifies edge cases before they become problems, and describes their approach in terms the AI can act on is demonstrating the kind of thinking that makes AI more useful, not less.
Build
The candidate works through the task with AI assistance. The evaluation here is not whether they complete it. It is how they work: What do they do when the output isn’t what they expected? Do they verify test cases? Do they read the code the agent generated or just run it?
The build phase has traditionally been where all the evaluation weight sits. In the new model, it carries less weight than it used to, because AI is increasingly doing the build. That’s expected, and the evaluation should reflect that.
Review
The candidate evaluates what was produced. This might look like reviewing a pull request generated by the agent, writing comments, identifying issues, or extending the solution under a new constraint added mid-session.
Review is where the signal is richest. Strong reviewers demonstrate that they understand the code well enough to improve it. They catch assumptions the model made that don’t hold. They write comments at the right level of abstraction. This is the work that senior engineers spend the majority of their time doing, and it’s the capability that’s been most underrepresented in technical interviews.
Evaluation weight: Plan and Review together should carry significantly more evaluation weight than Build. If your current scoring is centered on “did they complete the problem,” you’re measuring the phase AI handles best.
Stage 3: Supporting rounds
Depending on the role and level, two additional rounds are worth preserving:
System design. System design remains a high-signal fundamental skill, especially for senior roles. It is not easily delegated to an AI agent, good system design requires contextual judgment that models don’t reliably provide. Keep this round, but update the prompts to reflect current realities: how does this design account for AI-generated components? How would you evaluate an agent’s output at each layer?
Product sense (where relevant). As the line between engineering and product management continues to blur, a product sense round — give me a customer problem and show me a working prototype, can surface whether a candidate can identify what’s worth building, not just build it. This round tests a different kind of judgment that’s increasingly valuable as engineers take on more product ownership.
The integrity question
Static assessments are increasingly gameable. The long-term solution is not more proctoring. It’s making your evaluation dynamic.
A candidate who can recite what an AI model would generate cannot fake their way through a follow-up question about why the code works, what happens when an edge case is introduced, or how they’d change the approach under a new constraint. Conversation is the integrity mechanism.
Proctoring tools — browser activity detection, session recording, tab-switch alerts, remain useful as signals. They should be treated as context for human reviewers, not as binary pass/fail triggers. A candidate who switches tabs once during a three-hour session is not necessarily cheating. A candidate who cannot explain their own solution is.
Common objections and responses
“Our legal and compliance team will push back.”
This is real, especially in Europe. The distinction that matters most in regulatory conversations is whether the AI tool makes decisions or produces recommendations. No AI assessment output should result in automatic rejection. Every score needs to be interpretable — hiring teams should be able to read why a candidate received a particular evaluation, not just what the score was. Establish this as a non-negotiable requirement with any vendor.
“We don’t have the time to redesign the whole process.”
You don’t have to redesign everything at once. The highest-leverage change is usually the AI screen: replacing a recruiter screen and static take-home with a single dynamic session reduces scheduling overhead and produces better signal. That’s a scoped change that doesn’t require rebuilding your entire interview loop.
“Our engineers don’t know how to evaluate AI fluency.”
This is the most common blocker and the most important one to address. Provide concrete rubrics focused on observable behaviors: Did the candidate read the AI output before accepting it? Did they identify the edge case? Did they ask a clarifying question before prompting? These behaviors are assessable even by engineers who have never formally evaluated AI fluency before. Structured rubrics, not abstract definitions, are what make the evaluation consistent.
“We need to validate that this approach predicts performance.”
The honest answer is that predictive validity data for AI-era interview methods is still accumulating. The qualitative argument is strong: the closer an interview mimics the actual job, the better a signal it produces. An interview that requires working with AI tools to produce better output than either could alone is a more accurate proxy for an AI-augmented job than an interview that bans AI use entirely. Track your own data, time-to-productivity, manager ratings at 6 months, and build the validation internally as you go.
Where to start
If you’re redesigning your process from scratch, the sequence that’s working for organizations in HackerRank’s customer base:
- Audit your current process against the two pillars: where are you assessing fundamentals? Where are you assessing AI fluency? Most processes have the first, almost none have the second.
- Replace your recruiter screen and take-home with an AI screen. This is the quickest win — it reduces time-to-signal and compresses three scheduling events into one.
- Rebuild your first human-led interview around Plan/Build/Review. Give candidates a real-world task, require AI use, and shift your evaluation weight toward plan and review.
- Update your scoring rubrics to focus on observable behaviors rather than abstract definitions of AI fluency.
- Establish a bias audit cadence. Any new evaluation methodology should be reviewed for demographic consistency within the first few months of deployment.
The goal is an interview process that looks like the job — not like the job looked five years ago.
HackerRank provides technical hiring infrastructure for the agentic era, including Chakra (AI-powered screening), next-generation interview environments, and the Plan/Build/Review framework. To discuss how these tools apply to your hiring process, contact the team.