Before anything else, what are AI Agents?

AI Agents are autonomous versions of LLMs used for decision-making, and the main reason is they are highly effective at complex tasks by breaking them down into smaller, manageable steps. In addition, they can perform interactive actions such as retrieving data from databases, making API calls, or even reaching out to humans for additional information.

AI Agents are often compared with LLMs, but their usage in enterprise systems is very different. Today, microservices are considered best practice and can be written in multiple languages like Java or Python. Traditional coding is great at highly complex algorithm and deterministic requirements, but AI Agents can be replaced some of those microservices if particular module requires large number of business logic, especially those business rule often changed.

AI Agents can replace some of your microservices, especially where business rules change frequently.

This is especially true in Supply Chain & Logistics, where processes like inventory management, order fulfillment, and route planning rely on countless business rules. These rules often vary from one customer to another and change frequently due to seasonal demand, market fluctuations, or regulatory updates. AI Agents are particularly useful in these scenarios because they can adapt quickly to evolving requirements, reducing the need for constant manual intervention or system reconfiguration.

Which case applies to you?

When deciding between traditional coding and AI Agents for a microservice, the choice largely depends on the nature of your business logic and system requirements. Traditional coding works best when dealing with a small number of complex, deterministic rules. However, it comes with higher maintenance costs, as every change requires manual updates, testing, and deployment. In contrast, AI Agents excel when there are numerous basic rules that frequently change. They offer dynamic adaptability, learning from feedback and new data to adjust logic without heavy developer intervention. Their goal-based approach reduces maintenance costs and allows for error handling through supervised learning, making them ideal for fast-evolving environments like Supply Chain & Logistics.

The relentless growth of e-commerce has placed an unsustainable strain on traditional, manual methods of product catalog management. The imperative to provide rich, accurate, and engaging product information at scale has created a critical need for intelligent automation solutions that can navigate the complex trade-offs between creative quality, factual integrity, operational speed, and cost.
— Ryan M., Cloud Principal Architect, Retail at Google

Conclusion

As Supply Chains & Logistics grow more dynamic and complex, relying solely on traditional coding for every business rule update can slow you down. AI Agents offer a powerful alternative—bringing flexibility, adaptability, and intelligence to processes that demand constant change. Whether your goal is to boost automation, lower maintenance costs, or build resilience, now is the perfect time to explore where AI Agents can fit into your architecture.

P.S. coding is still here, not going anywhere 😎