Big AI models aren’t just for chatbots anymore. While simple chatbots get a lot of attention, they only scratch the surface of what AI can do in business. Companies are now embedding AI into their existing systems to improve filtering, summarization, translation, voice recognition, and more. The key is treating AI as just another tool in the software stack, something that extends what existing systems can do.

Integrating AI into Business Software

Using AI within enterprise applications requires understanding both what it can do and its limits. Instead of building everything from scratch, many companies are repurposing familiar tech. For example, LinkedIn has developed an AI framework that builds on traditional distributed application methods, especially messaging systems. This approach makes it easier to scale AI applications and keep things running smoothly.

It’s common to see AI agents working through messaging architectures. These messages handle structured data from business systems or user profiles. For instance, LinkedIn’s recruitment platform uses messages to gather resumes and profile info. These messages also keep a conversation history, helping AI understand user intent. So, if someone asks about software engineers in San Francisco, and then in London, the system recognizes the similarity and context.

The Role of the Agent Life-Cycle Service

At the core of LinkedIn’s AI setup is an “agent life-cycle service.” Think of it as a coordinator that manages all the AI agents, data sources, and applications. This service is stateless, meaning it doesn’t store data itself but relies on outside memory stores for context. That makes the platform easy to scale horizontally across cloud servers, handling lots of users at once.

The service uses familiar tools like gRPC interfaces and protocol buffers (proto 3). Developers can attach metadata to their agents, making it easier to manage and monitor them. One feature of this system is handling long tasks and switching between real-time interactions and batch processing. It’s similar to how Microsoft has been working on contextual computing for decades, where AI remembers previous interactions to better understand user needs.

Keeping Humans in the Loop and Ensuring Trust

An important part of this AI framework is keeping people involved. Since LinkedIn handles sensitive data, security and privacy are critical. The system supports role-based authentication and requires human oversight for certain actions. For example, if an AI generates an email, the user should be able to review and send it manually. This approach helps maintain trust and transparency.

LinkedIn’s first public application built with this system is an update to its Hiring Assistant. This tool helps recruiters filter candidates faster using natural language queries. It’s embedded within familiar screens and designed to save time, not replace human judgment. The goal is to assist people, not automate everything. Recruiters can clarify decisions, give feedback, and improve the system over time through ongoing interactions.

Monitoring and Observability for Reliable AI

To keep AI running smoothly, LinkedIn relies on observability tools like OpenTelemetry. These tools track how agents interact with services and what data they use. Monitoring helps identify issues, debug problems, and ensure compliance with privacy rules. It’s like keeping an eye on traditional software to make sure everything works as it should.

Ramgopal emphasizes that the same standards for monitoring traditional systems apply to AI. Knowing what’s happening under the hood is essential when deploying AI at scale. This focus on observability bridges the gap between pilot projects and everyday enterprise tools, making AI more reliable and trustworthy.

Overall, LinkedIn’s approach shows that integrating AI into large, distributed systems is both feasible and beneficial. It’s not about replacing humans but augmenting their capabilities with tools that are secure, transparent, and scalable. As more companies adopt these methods, AI becomes a natural part of the enterprise software landscape.

Inspired by

• https://www.infoworld.com/article/4054974/how-linkedin-built-an-agentic-ai-platform.html

Sources

• gmpg.org
• protobuf.dev
• podigee.com
• podigee.com