Purpose-Built Enterprise AI Agents Augment Human Capabilities Through Context and Trust

Key Quotes

"But you know what won the agentic race? Narrow, purpose-built agents. You know.... those built off large swaths of data to do one very specific thing well."

Prashanthi Padmanabhan explains that the successful approach to AI agents in the enterprise was not general-purpose agents attempting to mimic human roles, but rather specialized agents designed for a single, well-defined task. This highlights the importance of specificity and focused capability in AI agent development for practical application.

"And when you really think about agents and use cases what do you really want to think about is like hey is there a set of workflows and processes that today customers do to achieve a goal right if you really think about recruiters what is their primary job their primary job is to make sure that they are able to find the best talent for any given role that they are set out to actually fill."

Padmanabhan emphasizes that effective AI agents should be designed by understanding existing human workflows and the core goals of a job, such as a recruiter's primary function of finding talent. This perspective suggests that AI agent development should focus on augmenting or automating specific, goal-oriented processes rather than attempting to replace entire job functions.

"And for you to do that really well if you really think about the process of how do you define the product requirements for something like that how do you define the user experience for something like that how do you define where intelligence is important where experience is important how do you blend these things together I'll tell you one thing Jordan you cannot do you can't do that by just sitting in a boardroom and writing specs you just can't."

Padmanabhan argues that defining product requirements and user experiences for AI agents cannot be achieved solely through theoretical specification in a boardroom. She stresses the necessity of direct customer involvement and iterative development to truly understand and blend intelligence with user experience for these complex tools.

"So one of the things we learned early in the game of building LinkedIn's hiring assistant is we started working with our customers from day one of doing this... So from the get go we picked like a meaningful set of customers that we decided we're going to work from the beginning from the get go iterated on the product like crazy with them so the version of the hiring assistant that you're seeing today in the market is not where we started."

Padmanabhan shares a key learning from developing LinkedIn's hiring assistant: early and continuous collaboration with customers is crucial. She explains that the product evolved significantly through iterative development with a select group of users, indicating that the final, market-ready version was a result of this ongoing feedback loop, not an initial concept.

"So we evolved the experience so that we actually show the process we the agent will show you what it's doing it will tell you what it's looking for how many resumes it's looking at what it's finding in the resumes and when we find a match we show you the evidence we show you hey we think this candidate is actually one of the top fits for this role because this is what we found in the resume this is what we found in their screening back and forth this is what we found in probably in the future their github work their you know patterns that they've published right so showing that evidence was very important in the experience for the customers to build trust around it."

Padmanabhan highlights the importance of transparency in building trust for AI agents, particularly in critical applications like hiring. She explains that the LinkedIn hiring assistant evolved to show its process, including what it's searching for and the evidence found in resumes, to demystify the agent's reasoning and build customer confidence.

"So you know you call that like very domain specific fine tuning and you call that purpose built agents by purpose built what we really mean is we take something that is a general purpose model you blend it with your own platforms unique data and insights and you fine tune that model for that use case which is a very specific use case here like the sourcing use case and the talent matching against a role as a use case and you're iterating that model to get this right."

Padmanabhan defines "purpose-built agents" as a process of taking a general-purpose AI model and enhancing it with a platform's unique data and insights. She explains that this fine-tuning is essential for specific use cases, such as talent sourcing and matching, and requires iterative refinement of the model to achieve optimal performance.