---
id: "lib-2026-10-28-hiring-handed-to-agents"
type: "library"
title: "Hiring handed to agents"
title_original: "KARPOV.CONF 2026: найм и развитие команды отданы ИИ-агентам"
lang: "RU"
org: "Hyperskill (KARPOV.CONF 2026)"
source_type: "Talk"
date: "2026-10-28"
week: "2026-W44"
url: "https://nosov.ai/library/lib-2026-10-28-hiring-handed-to-agents/"
original_url: "https://www.youtube.com/watch?v=P-VACeCI3W4&t=12489"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "A founder (ex-Stepik, CEO of Hyperskill) describing a step-by-step recruiting pipeline with before-and-after time figures, but at small scale, self-reported and presented on his own agent platform, so it doubles as a product pitch."
summary_basis: "transcript"
original_minutes: "27"
tags: ["future of work", "recruiting agents", "hr automation"]
---

# Hiring handed to agents

Original title: KARPOV.CONF 2026: найм и развитие команды отданы ИИ-агентам

Tier B (Useful, limited): A founder (ex-Stepik, CEO of Hyperskill) describing a step-by-step recruiting pipeline with before-and-after time figures, but at small scale, self-reported and presented on his own agent platform, so it doubles as a product pitch.

Summary based on the full transcript.

## In short

A 27-minute talk at KARPOV.CONF 2026 (28 October 2026) by Ruslan Davleshin (surname as given by the host, unverified), formerly of Stepik, now CEO of Hyperskill and founder of an agent-workspace startup whose name is garbled in the captions. He describes two internal cases built on his own platform: a hiring funnel where agents score CVs, chase missing information, check inflated CVs, prepare questions and write interview reports; and a team-development set-up where agents keep a running file on each staff member from meeting transcripts and chat. He frames the work as three levels: personal automation, agents for other people, and agents that run inside other people's agents.

## Key numbers

- 150-200: applications a week for engineering and product-engineering roles, against 40-60 a recruiter could review by hand
- 18 h to ~3 h: recruiter time across all stages (CV review 5 to 0, preparation 6 to about 2, interview processing 7 to 1)
- 25 to 10 days: time from entering the funnel to an offer
- 1 h vs 2-3 days: time to get an interview report after each stage

## What it reports

- Baseline: 150-200 applications a week; a recruiter could review about 40-60 by hand, so strong candidates were lost and there was no time for new sourcing channels or employer brand.
- CV scoring: an agent scores each CV 0-100 against role expectations, using the team's prior assessment know-how.
- A sub-agent emails candidates whose CVs are thin, usually two rounds of three questions. This forced a change of applicant tracking system, because the old one's API did not return card comments.
- A second sub-agent checks CVs over-tailored with AI by looking at LinkedIn history, running web research and following project links.
- For each interview stage (screening, case, technical, final) agents generate questions, analyse the transcript and send a report; the interviewer accepts or comments, and a sub-agent re-scores the candidate. The final report proposes a grade and a compensation range; the founder and recruiter decide.
- A "founder" agent trained on the speaker's own interview transcripts feeds his criteria into the recruiter's question and scoring agents.
- The recruiter built and maintains all hiring agents without developers, using the platform's agent architect.
- Results: all applications are reviewed and ranked; interview reports arrive within an hour instead of two to three days; recruiter time fell from about 18 hours to about 3 hours; time to offer fell from about 25 to 10 days. Freed time went to new sourcing channels and the employer brand.
- Team development: three agents process 1:1 transcripts into a per-person file, pull positive peer feedback and value-aligned behaviour from Slack, the tracker and team meetings, and draft 1:1 agendas. An HR-built 360 agent sets goals from the same file. Results are qualitative only.
- Level-three examples: a product-manager agent and SOC 2 compliance agents that feed context into Claude Code and Codex through MCP.
- Q&A: on privacy, he said the team quickly got used to all meetings being recorded. For clients bound by Russian data law (152-FZ) the system runs on-premises with local models.

## How they got it

The speaker reports on his own company's use of his own product. Time figures are his estimates of recruiter hours per stage before and after; no hiring-quality measure (offer acceptance, retention, performance) is given.

## Limits

- Self-reported by a vendor about its own platform; the cases are presented partly as a product demonstration.
- Small scale: one recruiter and a weekly flow of 150-200 applications. The 18-hour figure is not stated as per week or per hiring cycle.
- No data on scoring accuracy, false rejections or bias in CV scoring, which are the main risks in automated screening.
- The privacy answer rests on staff getting used to recording; no consent or data-retention rules are described.
- Source is YouTube auto-captions: the startup name, the speaker's surname and several numbers ("150 тире2", "4060") are garbled and reconstructed.

## Why it matters for companies

Recruiting is one of the first HR processes firms try to automate, and this talk is a concrete map of where the time goes: CV triage, chasing missing information, writing interview feedback. The fastest gain came from reports written straight after each interview, which removed days of waiting on engineers' feedback. The ATS change is a practical warning that agents need API access to everything the process touches. The employee-file case shows the trade-off clearly: continuous recording of 1:1s and chat gives agents context, and raises consent and trust questions that need explicit rules, plus a check of automated screening for bias.

## Key passages in translation

> А, и к нам примерно в неделю приходит 150 тире2 заявок. А, и до появления, ну, и я рекультёр руками успевал разбирать примерно 4060.

We get about 150 to 200 applications a week, and before this a recruiter could review about 40 to 60 by hand.

> Раньше у нас было где-то 25 дней. ... Сейчас это сократилось за 10 дней благодаря э использованию идеагентов.

Before, it took about 25 days. Now it is down to 10 days thanks to the agents.

> У нас получилось так, что мы как будто бы достаточно быстро привыкли к тому, что все встречи всегда записываются.

It turned out we got used fairly quickly to all meetings always being recorded.

Original: https://www.youtube.com/watch?v=P-VACeCI3W4&t=12489