HackerRank's AI Interviewer Chakra Goes Live After 500,000 Tests

HackerRank released Chakra, its agentic AI interviewer for tech hiring, on Monday, October 5, 2026. The launch followed about six months in beta. Co-founder and CEO Vivek Ravisankar discussed the product in an interview with TechCrunch TechCrunch.

Chakra conducted more than 500,000 interviews during testing. Testers included Snowflake, Snorkel and Capgemini, alongside HackerRank's own internal use. That volume gives the company a large set of sessions to use for calibration and further tuning.

The product moves away from the isolated prompt-and-response test. In a Chakra interview, a candidate works on a task inside a real-world code repository, meaning the full set of files for a project, in a workspace that includes an AI assistant. The system observes the work as it happens and scores how the candidate arrived at an answer, not only the final answer. In HackerRank's description, Chakra evaluates judgment, critical thinking and AI fluency HackerRank.

For readers who hire engineers, the mechanics are specific. HackerRank defines an AI interviewer as software that runs a structured technical or behavioral interview on its own HackerRank. In Chakra, that means adjusting questions in real time and flagging suspicious behavior during the session. Process counts more than output.

On integrity, HackerRank said suspicious-activity flags were 70% to 80% lower in Chakra interviews than in comparable traditional HackerRank assessments TechCrunch. The company attributes the drop to the design. When AI help is built into the workspace and its use is logged as part of the score, candidates have less reason to reach for unapproved tools outside it.

The broader context here is a shift in what technical hiring tries to measure. HackerRank launched at TechCrunch Disrupt in 2012. It now counts more than 3,000 business customers, including Amazon, Nvidia, Clay and Replit, and a community of over 30 million developers worldwide. It calls its rebuilt interview offering HackerRank 2.0. Asking candidates to write code in an empty browser tab made sense when that was a fair stand-in for the job. It is a weaker stand-in when daily engineering means opening an existing codebase, prompting an assistant, reviewing its output, and explaining trade-offs.

In my view, hiring teams should read Chakra less as automation of the interviewer and more as instrumentation of the interview. A traditional test keeps the final diff, the changed code. An agentic session keeps the trace. Which files were opened, what was asked of the assistant, what was accepted, what was rewritten, when the candidate backtracked. That trace maps more directly to AI fluency as a skill. It also creates new checks. Teams will need data on how scores relate to job performance, how consistent the agent is across runs, how it handles different stacks and seniority levels, and how candidates experience such close observation.

Worth flagging here is that the cheating problem does not disappear, it changes shape. Letting candidates use an assistant removes one reason to bypass controls, which likely explains part of the flag reduction. The harder issues move elsewhere. Prompt injection into the repository, meaning hidden instructions placed in code, theft of test material, and coaching tools running beside the interview are all possible with current software. A lower flag rate is welcome, but managers will want to understand false negatives as well as false positives, and how audit logs are stored and reviewed.

Looking at what this enables, the upside is practical. Structured interviews that run on their own can operate at hours and volumes human panels cannot cover. Every candidate starts from the same task, with adjustment from there. That consistency helps distributed teams and high-volume hiring even before claims about prediction. I have watched my own children enter workplaces where asking AI for a first draft is assumed, and where people are judged less on recall than on what they do with the answer. Chakra starts from the same assumption. If interviews are to remain a useful filter, they will need to test that second step.