HackerRank

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.

Everyone is hiring for AI fluency. Almost nobody agrees on what it means. This piece answers the most common questions engineering and talent leaders are grappling with right now, based on conversations with hiring teams at global technology companies navigating this shift. What is AI fluency in software engineering? AI fluency is a software engineer’s ability to work effectively with AI tools — not just use them, but direct them, evaluate their output, and know when not to rely on them.

At a recent HackerRank customer advisory board in London, an engineering leader said something that stopped the room. “We need to find people who know what good looks like. But the people we have now don’t know how to evaluate it. It’s a contradiction in terms.” He wasn’t wrong. And nobody in the room disagreed. Across the table were hiring leaders from organizations with mature, global recruiting functions.

The regulatory ground shifted in February 2026. Most employers haven’t caught up. UK organisations using AI to screen or score job candidates are operating in materially different legal territory than they were 18 months ago. Two pieces of legislation — the Data (Use and Access) Act 2025, which came into force in February 2026, and the EU AI Act, whose core employment obligations apply from August 2026 — have created a compliance landscape that most technical recruiting teams haven’t fully mapped.

June edition A 24-hour hackathon to design, build, and ship an agent The role of a developer has evolved. You’re not just writing code, you’re orchestrating agents to solve real problems. Anyone can prompt. Few can build systems that actually deliver. This is your chance to prove you can. Your challenge arrives at via email. A real-world problem that needs an agent-based solution. Use whatever tools you work with, Claude Code, Cursor, Codex, or anything else.

AI fluency has become one of the most used terms in technical hiring. It’s also one of the least precisely defined. Some teams use it to mean “comfortable using AI tools.” Others use it to mean “can write good prompts.” Others have given up trying to define it at all and treat it as a gut-feel judgment at the end of an interview. None of those definitions are wrong exactly. But none of them are measurable, which means they can’t drive consistent hiring decisions.

In May 2026, HackerRank ran Orchestrate, a 24-hour hackathon in which participants built an AI support triage agent from scratch. The agent had to classify tickets, assess urgency, and decide whether to reply or escalate, using a corpus of 774 documents as its only knowledge base. 12,885 people registered from 48 countries. Every submission was evaluated on four independent signals: code quality, output accuracy, AI chat transcript, and a 30-minute AI judge interview.

The agentic interview doesn’t just change what you test. It changes what you have access to evaluate. When a candidate plans, builds, and reviews with a real AI agent on a real codebase, three distinct levels of capability become visible. The difference between them isn’t speed, or which tools they use, or how much code they produce. It’s judgment, and it shows up at three specific moments. Here’s exactly what each level looks like.

Here is an example of what a technical interview should look like in 2026… A candidate opens a repository for Shipway, a software deployment platform. They receive a ticket the same way they would on day one of the job: pulled from a project management tool, assigned to them, with full context. The ticket: customers are reporting issues with deployment creation. The agent is available immediately. The interviewer watches everything live.

Engineers today don’t write code line by line. They plan, prompt, and manage AI agents to do the work. The role has fundamentally changed. But walk into most technical interviews happening right now and you’ll find candidates typing code manually into a plain editor, reversing linked lists from memory, stitching frameworks together by hand. Mechanics that an AI agent can perform in seconds. The interview hasn’t caught up with the job.