LinkedIn has built an artificial intelligence (AI) infrastructure stack around agentic workflows, moving beyond standard generative text features to autonomous AI agents capable of managing complex recruitment tasks.
The technology is now being used to support the global rollout of LinkedIn Hiring Assistant, a product the Microsoft-owned company claims is already saving recruiters an average of four hours per role and reducing candidate profile reviews by 62%.
Speaking to Computer Weekly in a recent interview, Prashanthi Padmabhan, vice-president for engineering at LinkedIn Talent Solutions, said the company’s infrastructure stack has evolved to support an agentic era where AI doesn’t just summarise text, but executes multi-step workflows.
“We took all those manual, labour-intensive parts of the recruitment process and used a combination of agentic flows and a multi-modal agent-based architecture,” Padmabhan said.
Padmabhan said LinkedIn Hiring Assistant is built on the LangGraph agent orchestration framework, allowing AI agents to perform the hard work of scouring LinkedIn’s billion-member database to identify job candidates for hiring teams.
The move comes as the recruitment market faces increasing friction. According to new data released by LinkedIn, about three in four recruiters in key Asia-Pacific markets say finding qualified talent has become significantly harder.
“We are using the power of LLMs [large language models] to process a large set of data and then come back with candidates for the given role,” Padmabhan said. “We’re not just coming back with the results; we’re also giving explanations and evidence for why we think these top candidates are best fit for the role.”
However, standard off-the-shelf LLMs are not effective in catering to the nuances of specialised enterprise hiring. Instead, LinkedIn uses an ensemble of models fine-tuned on its massive dataset of skills, job changes, and professional relationships.
This allows AI agents to interpret natural language requests, such as a recruiter describing a role conversationally, rather than forcing them to construct complex Boolean search strings.
“You need a lot of domain-specific intelligence. We use our own secret sauce – our data and insights – to make sure we are able to bring that into context,” Padmabhan said, adding that the engineering team employs retrieval-augmented generation, reinforcement learning and other techniques to continuously improve model performance.
For enterprise CIOs and HR leaders, the integration of AI agents into existing HR management applications is necessary so companies can manage the entire employee lifecycle, from recruitment and onboarding to engagement and training.
Padmabhan said LinkedIn’s recruitment capabilities can integrate with popular HR platforms and applicant tracking systems (ATS) such as Workday and SuccessFactors, blending LinkedIn profile data with an enterprise’s candidate records.
However, with automation comes the risk of AI hiring biases. Padmabhan stressed that LinkedIn adopts a human-in-the-loop approach, meaning the AI agent provides evidence and reasoning for its selections, but the recruiter makes the final call.
“We are not making the Hiring Assistant make autonomous choices. Its job is to do the hard work and tell you why it chose those candidates with clear evidence,” she said.
She added that before any agentic AI product reaches production, it must pass a battery of tests from a responsible AI team to check for gender and demographic biases, as well as security vulnerabilities like prompt injection attacks.
Early adopters of LinkedIn Hiring Assistant, such as United Overseas Bank (UOB) and blockchain technology company OKX, have benefited from the technology.
Speaking at the LinkedIn Talent Connect event in Singapore, Jay Chan, executive director and head of talent acquisition at UOB, whose team was a charter customer for LinkedIn Hiring Assistant, used the tool to identify the specific candidate he ultimately hired, helping him justify the return-on-investment (ROI) to business leadership.
Tracy Mao, director and head of HR for Singapore and Malaysia at OKX, reported that the tool’s candidate matching capabilities were outperforming manual outreach, saving about six to eight hours of recruiter time. However, Mao noted that the tool currently faces challenges when “pairing up with multi-language candidates.”
Originally written by: Aaron Tan
Source: TechTarget
Published on: 5 February 2026
Link to original article: LinkedIn touts agentic AI to slash recruitment time
