What is AI orchestration?

In addition to AI models and AI agents, AI systems also include computational resources, data stores, and the data flows and pipelines that transmit data across an organization. Many AI systems connect models with tools through application programming interfaces (APIs).

Effective AI orchestration streamlines the end-to-end AI lifecycle at every stage. Orchestration platforms automate AI workflows, track progress toward task completion, manage resource usage, monitor data flow and memory and handle failure events.

As large language models (LLMs) and generative AI (gen AI) become more popular, organizations are implementing LLM orchestration techniques to build and maintain more capable chatbots and other AI apps.

With a unified AI system, enterprises benefit from greater efficiency, scalability, responsiveness and effectiveness.

AI orchestration works by bridging the gaps between the components of an AI workflow. The three pillars that facilitate AI workflow orchestration are:

• AI integration

• AI automation

• AI management

AI integration connects AI tools, databases and other system components in an AI solution.

Critical to AI integration are data pipelines—the automated processes that organize, store and move data through an organization. Data engineers design and build data pipelines for efficient data transfer, reliable data quality, ease of data maintenance and access for data integration and analysis. Data flow diagrams are useful tools that illustrate the movement of data through an organization and are helpful when building AI tools.

Integration also covers real-time communication and collaboration between machine learning (ML) models, linking them with tools through APIs for function calling.

Orchestration platforms enable the creation of AI ecosystems that chain models together in complex workflows to autonomously fulfill high-level tasks that are too demanding for one model on its own.

Automation is the completion of tasks without human intervention. Automated processes can range from simple "if-then" code to entire app workflows.

Many AI applications automate some portion of a workflow or process, which in theory simplifies the life of the user. For example, AI apps can summarize and translate documents, generate code snippets, check code and conduct research.

Automation in AI orchestration is the use of orchestration tools to automate AI-related processes and decision-making, such as a function call from an LLM to a tool through its API.

Orchestration platforms can also self-manage compute use, prioritizing memory and resources where they are needed most to address urgent demands.

In other cases, automation can include ongoing maintenance as the platform monitors an AI system for errors and other losses in performance and then addresses those issues. Patches, updates and even new models can be automatically deployed to minimize disruptions to the user or customer experience.

AI management is essential to an organization’s ongoing commitment to data governance and AI ethics. Orchestration use cases in AI management cover the oversight of an AI application’s entire lifecycle.

Data scientists can benefit from performance monitoring in the data processing workflows that provide the clean, reliable data that AI models need for accurate results.

Management is also crucial for an organization’s security, reporting and compliance obligations. Strong data protections uphold commitments to protecting user data while keeping enterprises in line with legal requirements.

AI agents are singular machine learning models that can autonomously plan and execute tasks. AI orchestration is the integration of AI agents with other models, tools and data sources to automate and manage larger AI systems.

Imagine an AI agent as a traffic light connected to a traffic flow sensor. This traffic light can autonomously determine when to change colors and does a reasonable job of managing the traffic flow at its intersection.

However, it has no idea what the overall traffic conditions are across the city—or even a block away at the next light.

On roads where the traffic lights are not synced or correctly timed, traffic jams are often the result, with impatient drivers subjecting nearby residents to the symphony of their frustration.

The AI orchestration tool in this scenario would be the system that coordinates the timing of the traffic light changes to keep vehicles moving smoothly along the roads.

AI orchestration helps businesses apply AI technology toward the creation and deployment of systems and apps that scale efficiently, run smoothly and avoid performance interruptions. The benefits of AI orchestration include:

• Greater scalability

• Increased efficiency

• Better collaboration

• Improved performance

• More reliable governance and compliance

One of the primary concerns organizations must address when compiling an AI strategy is how to scale AI systems with business growth and changing use cases. Orchestration enables enterprises to adapt to changing demands and shifting workflows with the appropriate resources in the right places.

For example, developers can use Kubernetes to automate and administer the deployment, management and scaling of container-based AI applications. Orchestration platforms dynamically allocate resources in real time to address shifting demands and priorities as businesses scale and need change.

Orchestration creates automated workflows that remove the need for repetitive, tedious tasks. As an example of how this seamless integration can optimize business practices, consider a situation in which employees need to regularly reference company data.

Traditionally, they might consult manuals, training videos and spreadsheets, or ask colleagues in other departments to find the information they need.

However, AI provides alternative solutions. Open-source orchestration frameworks such as LangChain make it possible to modularly construct AI applications, with some offering low-code or no-code interfaces.

Retrieval augmented generation (RAG) connects a database with a natural language processing (NLP) LLM to create a chatbot that gives users access to internal data through conversational prompts. Organizations can implement such an application to give employees efficient access to the data they need.

Like other types of cloud-based platforms, orchestration tools provide a centralized workspace in which teams can collaborate both internally and with other teams on projects. Rather than keep each component of an AI app in a separate silo, all project stakeholders can work together in the same environment.

The enhanced knowledge-sharing and collaboration of a unique workspace extends to the postdeployment stage of an AI product’s lifecycle. When bugs and other challenges arise, everyone can team up to effectively troubleshoot and resolve the issues.

AI orchestration opens the door to more complex problem-solving because it allows AI app creators to use multiple models, tools, data sources and other assets.

AI models are specialists. Machine learning algorithms are designed to achieve specific tasks. Orchestration facilitates the creation of an AI system that brings the strengths of various models to bear on the challenges they are uniquely designed to solve.

For example, a computer vision model and natural language processing model can collaborate to scan and summarize physical documents. The former “reads” the text with optical character recognition, and the latter delivers the summary.

Troubleshooting is also enhanced by the real-time monitoring capabilities offered by many orchestration tools. Organizations can use the ongoing performance data to tweak workflows, fine-tune models for better outputs, and adjust data flows as needed.

AI orchestration tools are the singular point of control for an entire AI app, system or workflow. With the ability to manage all the components in one place, organizations can better ensure that their AI initiatives meet legal and regulatory requirements.

The status of the AI system can be tracked and monitored in real time, granting insight and transparency into its processes as it works.

Transparency is paramount for the responsible use of AI in healthcare and other industries involving sensitive data, and orchestration platforms can help in making obscure AI systems more explainable.

Reliable governance and compliance are especially important in fields with strict privacy regulations, such as when applying generative AI in finance, medicine or law.