From concept to reality: A practical guide to agentic AI deployment

The Rise of Agentic AI

The buzz around agentic AI and agent development is inescapable. That shouldn’t be surprising based on a study that shows “by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.” The future of AI is agentic. Agentic AI is an AI system that uses components that can act autonomously to complete tasks and achieve goals. These AI systems are evolving beyond having conversations and are progressing to a state of getting things done. While there are plenty of articles that discuss building agentic AI solutions, not many cover agentic AI.

Challenges Faced by AI Teams

AI teams face many complex challenges as they work to develop and scale their agentic AI systems. The traditional approaches for implementing AI have focused on AI chatbots, single declarative agents or synchronous processes. As the industry moves to take advantage of more complex solutions, we are seeing multi-agent architectures and dynamic workflows orchestrating agents to perform tasks. These more complex solutions require more focus and thought on the activities needed for deployment and operations.

The Role of Agentic AI DevOps

Agentic AI DevOps pushes beyond existing and traditional DevOps practices and activities. Agentic AI development is more complex than other types of AI application development. More activities come into play, including business logic and workflows, natural language, machine learning, data management, security, monitoring and more. All these pieces need to come together seamlessly for a successful deployment.

The Art and Science of Deployment

There is no one-size-fits-all when it comes to agentic AI. As you build out your agentic AI solution, keep in mind that no single architecture will fit all agentic scenarios. However, the design factors and architecture you choose will affect the deployment and operations activities. Deploying AI solutions has been a change from traditional deployment activities. Agentic AI is another change. Agentic AI moves you away from delivering code at specific milestones to a process of continual deployment with testing and evaluation activities that are ongoing.

Experiment and Build Phase

Ensure that you understand the following from the experiment and build phase to know what needs to be done in the and phases.

Evaluate and Test Phase

As you evaluate and test the solution, keep in mind that outages aren’t only the results of hackers, but typically the result of preventable glitches and oversights in the development process. These issues can be found and mitigated during this phase and are essential to make sure that the consumers of your solution don’t lose patience.

Transition to the Deployment Environment

During the transition to deployment, we need to ensure that we have automated the entire process. We need to automate the testing and evaluation activities and the full deployment process. The most crucial safeguard around recovery from outages and ensuring a smooth software delivery, with more reliability and efficiency, is to automate the software development pipeline.

Deployment: Automating the LLM Operations Lifecycle

Keep in mind that everything surrounding artificial intelligence and agentic AI is still evolving. We are seeing models being released faster, which introduces model management activities that we didn’t have to manage previously. Tooling is evolving and new frameworks are being released that make processes easier and more streamlined and that can reduce technical debt. You need to ensure your AI solution evolves as well.

Key Takeaways

Fully automating the LLM operations lifecycle will enhance efficiency, consistency and reliability, while also supporting continuous improvement, cost-effectiveness and compliance.