The LinkedIn Generative AI Application Tech Stack:...

The LinkedIn Generative AI Application Tech Stack: Extending to Build AI agents Published on Sep 10, 2025 Categories: Company News, Data and insights, Featured News

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Last year, we unveiled LinkedIn's generative AI application tech stack, which enabled us to build GenAI applications at scale. At the time, we touched on developing agents for members and customers and had just launched Hiring Assistant, LinkedIn's first AI agent for recruiters, to our charter customers. Since then, we've been focused on improving the Hiring Assistant experience, and we're excited to make it globally available in English to customers by the end of September.

To scale to this level and improve the value of our agent, we relied on strategic tech stack updates. This post will cover extensions to the GenAI application platform that have enabled us to build AI agents that think, plan, and act in collaboration with users, marking the next phase in LinkedIn's GenAI evolution. These foundational changes ultimately help us build products that are proactive, personalized, and adaptive for our members and customers.

We'll share details on how we've thought about and adapted to building agentic experiences with key learnings on:

Reusing existing infrastructure and providing strong developer abstractions are key to scaling complex AI systems efficiently.

Designing for human-in-the-loop control ensures trust and safety while enabling agents to operate autonomously when appropriate.

Observability and context engineering have become essential for debugging, continuous improvement, and delivering adaptive, personalized experiences.

Finally, adopting open protocols is critical to enabling interoperability and avoiding fragmentation as agent ecosystems grow.

We're all learning as an engineering community as we build tech stacks, platforms and frameworks to help our businesses grow and scale. That's why it's important to continue to share lessons and insights to help others as we all build better and more valuable AI products.

Anatomy of a GenAI agent A GenAI agent is an autonomous or semi-autonomous system powered by large language models (LLMs) designed to perform complex, long-running tasks-often in dynamic environments-by reasoning, planning, and interacting with systems and humans. Unlike traditional software, Gen AI agents dont just follow hardcoded logic; they use LLMs to interpret goals, make decisions, adapt to changing conditions, and even break tasks into subtasks before solving them.

Given the cognitive limitations of current LLMs and to ensure that human operators have control, agents need to operate with a human in the loop (HITL) approach. What this means is that agents seek clarification, get feedback, or request approvals at key decision points, balancing autonomy with control. By combining LLM reasoning with task memory, tools, and human collaboration, Gen AI agents mark a shift from one-off prompts to continuous, contextual AI workflows.

Figure 1: Illustration of a typical agent reasoning loop control flow.

For most use cases, the agent is not a single monolithic application, but rather a facade over multiple such agentic applications. This approach offers the following key advantages.

Modularity: Each agent handles a specific task, making the system easier to build, test, and extend.

Scalability: Tasks can run in parallel, improving performance for long-running or complex workflows.

Resilience: Failures in one agent don't crash the whole system-errors can be retried or isolated.

Flexibility: New capabilities can be added by plugging in new agents, enabling rapid iteration.

However, for this to work at scale, we need a way for application developers to define agents and an intelligent orchestrator that manages task flow, dependencies, communication between agents and HITL steps.

Defining and registering agents within our systems As mentioned, one of the key enablers to crafting and scaling agent-based systems is to help define those agents in an accessible way for the system and meaningful way for the application developers.

Since agents run online in production, we decided to reuse the same mechanism we use for service to service communication at LinkedIn, aka gRPC, and came up with a standard gRPC service schema definition. Developers simply annotate this definition with some platform defined proto3 options that describe the metadata of their agent, and register it via a build plugin into the skill registry (a central service that tracks available agents, their metadata, and how to invoke them) as described in our previous blog post.

Laying the groundwork for multi-agent orchestration Having a single monolithic agent application isn't tenable for many of the complex tasks and workflows that our members and customers engage in across LinkedIn. So by defining our agents, we can then lay the groundwork to orchestrate multiple agents - increasing their capability and effectiveness in tackling a task.

In order to run multi-agent systems at scale, we need to fallback to the classic distributed systems concepts of horizontal scaling with compute deployed across multiple regions for better scalability, performance and availability. This in turn means that all the classic distributed systems problems of consistency, availability and partitioning return with an additional twist; namely a highly non-deterministic workload given the nature of GenAI agents.

Rather than solve this from scratch, we looked to our existing production systems for inspiration and realized that our messaging system best emulated the characteristics we wanted in a multi-agent orchestrator. Long lived tasks could be broken down into a sequence of messages with guaranteed first in first ou

LINK:	https://news.linkedin.com/2025/linkedin-s-ai-stack--expanding-to-build...
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