AI orchestration: A beginner’s guide for 2025
To use AI effectively, a business needs to integrate a variety of AI models and tools into their tech stack. However, without a way to coordinate these different AI technologies as part of a unified system, operations become more siloed and disjointed—which defeats the purpose of using AI in the first place.
This raises the question: how do you get these disparate AI systems and tools to work together seamlessly? How do you achieve AI workflow automation? The answer lies in AI orchestration.
In this guide, I’ll walk you through the ins and outs of AI orchestration. You'll learn everything from the basics to best practices, so by the end, you'll be ready to harness your diverse AI capabilities into one unified, automated operation.
What is AI orchestration?
AI orchestration is the process of coordinating and managing AI models, tools, and systems so they work together effectively. It provides the architectural patterns that connect AI components with existing systems, enabling AI to share data, communicate, and collaborate seamlessly as part of a unified whole.
Like the conductor of an orchestra, AI orchestration harmonizes the diverse elements of an AI system, allowing each component to do its part in creating a successful outcome. If orchestrated properly, AI can access data sources, use tools, and function in ways a siloed component never could.
For businesses, AI orchestration not only increases the efficiency, scalability, and effectiveness of AI systems, but it's also the gateway to end-to-end process automation.
What’s the difference between AI orchestration and AI workflow automation?
Mainly, that AI orchestration serves as the foundation for managing and scaling AI workflows, whereas AI workflow automation is about automating specific tasks or processes within a larger orchestrated system.
AI orchestration: Manages the entire ecosystem of AI models, tools, and workflows to ensure their seamless collaboration and scalability.
AI workflow automation: Automates specific tasks or parts of a process within a larger orchestrated AI system.
Through orchestration, businesses can effectively leverage the capabilities of specialized AI tools within workflows to tackle complex problems. Orchestration also takes care of task sequencing, error recovery, as well as data flow and usage across AI-driven processes and workflows, ensuring they’re efficient, cost-effective, and scalable.
In short, AI orchestration enables a business to unlock real business value through AI workflow automation, as it serves to operationalize and optimize individual AI agentic workflows.
Real-life example of AI orchestration
Imagine a ride-sharing platform like Uber. The company uses AI technology for a variety of tasks, including to match drivers with riders, optimize pricing, and predict demand.
AI for ride matching | AI for pricing optimization | AI for demand prediction | |
AI analyzes rider requests and driver availability, assigning the closest driver to reduce wait times. | Another AI system adjusts ride fares in real time based on demand, traffic, and driver supply, ensuring fair pricing and accurate surge rates. | Yet another AI forecasts demand based on historical data, current events, and real-time weather conditions, helping drivers position themselves for peak service delivery. |
Without AI orchestration, these systems might not share data effectively in real time, resulting in inefficiencies—slower ride matching, inaccurate surge pricing, drivers struggling to meet demand—all of which reduces revenue and customer experience.
But with AI orchestration, these systems work together seamlessly:
The demand prediction AI informs the ride-matching AI, ensuring drivers are positioned in high-demand areas before requests surge.
The pricing AI adjusts fares dynamically based on demand forecasts and real-time ride availability, maintaining balance in the marketplace.
The ride-matching AI factors in surge pricing and driver distribution, ensuring optimal pairing between riders and drivers while maintaining fair prices.
Ultimately, AI orchestration enables the AI-powered ride-sharing system to be more efficient, responsive, and cost-effective, creating a smooth experience for drivers and riders while maximizing revenue.
The 3 key components of AI orchestration
AI orchestration serves to bridge the gap between the components of an AI system, ensuring all parts work in harmony to maximize the efficiency and effectiveness of the overall system.
Broadly speaking, the core components of an orchestrated AI system fall into three main buckets:
Integration
Automation
Management
1. Integration
Integration is what enables different AI models, tools, and data sources to work together. Once integrated, all parts of the AI ecosystem can communicate and collaborate as a single unified system.
Key elements:
Data integration: Ensures that data flows seamlessly between systems so AI components have the data needed to function effectively. This requires the creation of data pipelines, or automated processes that collect, process, and move data across systems while optimizing performance, scalability, and reliability.
Model integration: Enables different AI models to interact with each other through frameworks and tool use like APIs. This enables models to share capabilities and outputs without human intervention.
2. Automation
Automation is the use of technology to handle tasks that are traditionally done by humans. For example, an AI chatbot automates customer service, answering FAQs and freeing humans to focus on higher value work.
AI orchestration is what automates the interactions between different AI applications and systems, paving the way for AI workflow automation. By managing dependencies, optimizing decision-making, and synchronizing processes in real time, orchestration is key to reducing manual intervention and improving operational efficiency.
Key Elements:
Automated model selection: Enables AI systems to dynamically select the right model based on context or system conditions, matching models to tasks automatically for optimal effectiveness and efficiency.
Resource allocation: AI orchestration platforms can dynamically allocate computational resources based on the evolving needs and priorities of different tasks.
Self-healing systems: Orchestrated systems can monitor system health to automatically detect and self-correct issues or inefficiencies in AI workflows without human intervention.
3. Management
AI systems must be effectively managed to maintain the health, effectiveness, and compliance of AI components throughout their lifecycle. AI orchestration oversees the entire AI lifecycle, focusing on key areas such as:
AI workflow management: Defining the sequence of tasks to be executed by AI models and its data dependencies within a process to minimize manual intervention.
Performance monitoring: Continuously tracking the performance of AI systems to ensure they operate optimally and making adjustments as needed.
Compliance and security: Ensuring all AI operations comply with relevant laws and regulations, and maintaining robust security protocols to protect data and systems from unauthorized access.
How does AI orchestration work? APIs and cloud computing
AI orchestration is made possible by two essential technologies: APIs and cloud computing. And while not essential, AI orchestration platforms are a great help in streamlining the process of deploying and maintaining AI systems.
Here's a brief summary of the role of each technology:
APIs
APIs are the backbone of AI orchestration, allowing different software components and services to communicate and work together. In the context of AI orchestration, APIs enable the disparate AI models and applications to seamlessly exchange data and functionality, regardless of their underlying programming languages or architectures.
Cloud computing
Cloud computing platforms provide the scalable infrastructure needed for deploying and managing AI systems. They provide the flexibility, cost-efficiency, and computational power needed to process large datasets and execute complex AI algorithms without bottlenecks. Additionally, cloud platforms simplify the integration and management of AI systems across diverse environments and locations.
AI orchestration platforms
AI orchestration platforms automate, manage, and streamline the deployment, execution, and lifecycle of AI workflows across various environments. They coordinate the various AI components—such as machine learning models, data pipelines, APIs, and computational resources—to ensure their seamless integration, scalability, and operational efficiency. By handling various tasks like workload scheduling, dependency management, and resource optimization, they help organizations to deploy and maintain AI systems with manual intervention.
Benefits of AI orchestration
When properly orchestrated, the performance, scalability, and adaptability of AI systems are all enhanced. In this section, we’ll take a deeper diver into the major benefits of AI orchestration:
1. Operational efficiency
By streamlining operations and reducing the need for human intervention in repetitive tasks, AI orchestration represents a giant leap in operational efficiency. It allows a business to process tasks faster, more accurately, and cost-effectively—opening the door to scaling intelligent automation for routine workflows.
For example, without AI orchestration, if you wanted to pull data from multiple sources into a marketing campaign to optimize audience segmentation, this would have to be done manually. But with AI orchestration, you can automate this sharing of data and move closer to end-to-end workflow automation.
In addition to automating end-to-end workflows, AI orchestration adds efficiency through:
Automated deployment: Orchestration allows for new AI models to be deployed automatically, and for automated updates to existing models across environments.
Resource optimization: Orchestration ensures that computational resources are allocated dynamically based on workload requirements. This prevents underutilization during low demand and overprovisioning during peak times, reducing resource waste and operational costs.
2. Improved performance of AI systems
AI orchestration enables a business to harness the specialized capabilities of diverse AI components to tackle problems that would be too complex for a single siloed component.
For example, in healthcare, a AI-powered diagnostic systems integrate multiple specialized AI models—such as medical imaging analysis, patient history, and predictive analytics. When orchestrated, the different models can analyze patient health from multiple angles, then synthesize the separate inputs into one holistic output that’s more accurate and reliable than each model is acting on its own.
Here are a few more examples of how orchestration improves AI system performance:
Continuous learning and improvement: Orchestrated systems can automatically incorporate feedback on their outputs to continuously refine their models and adapt workflows over time. This capability for self-improvement enables AI systems to become more accurate, efficient, and adapted to business needs without manual updates.
Visibility and monitoring: Orchestration enables for centralized monitoring of all AI workflows, helping teams to track progress, measure performance, and identify inefficiencies in AI-driven processes. This visibility leads to better decision-making, risk mitigation, and proactive optimization.
Dynamic workflow adaptation: AI workflows can be orchestrated to adapt dynamically to changing inputs and dynamic environments, ensuring the most effective processes are followed even in unpredictable scenarios. For example, in healthcare, patient diagnostic workflows can be designed with conditional logic so they adapt based on real-time patient data, routing critical cases to clinicians and routine cases to self-care workflows.
The 7 core steps to implement AI orchestration
As we’ve discussed, AI orchestration is a game-changer for businesses looking to make the most of their AI investments. Here are the main steps in the journey toward a more effective approach to AI:
1. Define your needs and goals: Start by identifying the processes that would benefit from AI orchestration. Whether it's customer service, refining code, or data processing, defining clear objectives with measurable KPIs will help you measure success and manage the project scope.
2. Prepare your data: AI systems require vast amounts of data to function effectively. Ensure you've established processes for data collection, data cleaning, and data management so you can get data ready for training models and other systems.
3. Design your architecture: An ideal architecture will allow for the efficient data flow and communication between different AI models, both for current needs and future initiatives.
4. Select your tech stack: Choose an AI orchestration platform that can manage your chosen AI models, data flows, and workflows effectively. Consider the scalability, security, and integration of the platform with existing systems and future AI technologies.
5. Create your AI workflows: Map out the sequence of AI model execution, decision points, and human in the loop (HITL) interactions where necessary. Build the automated workflows using your chosen orchestration platform, including error handling and exception management.
6. Integration and testing: Connect your AI models to relevant data sources through the orchestration platform. It’s crucial to conduct thorough testing at this phase to ensure smooth data flow and real time updates, and address any technical issues that arise.
7. Deployment and monitoring: Continuously track your key metrics to assess the effectiveness of AI models, systems, and workflows. Implement feedback loops to refine models, making adjustments as needed to optimize operations.
Challenges of AI orchestration
Creating the infrastructure to manage multiple AI agents, tools, and systems is a complex process that presents challenges in two main areas:
Technical complexity: For engineering teams, it’s daunting to manage, integrate, and scale a diverse set of AI components with external systems.
The human element: Complicating factors include skill gaps in AI and software engineering, change adoption, and ethical considerations requiring human oversight.
Challenge 1: Implementation complexity
Implementing disparate AI systems is complex and demands advanced technical expertise to integrate tools and systems effectively. Without proper execution, businesses risk downtime, inefficiencies, and disruptive data errors.
Solution: Provide your team with a clear strategy, comprehensive training, and the resources to ensure effective implementation and integration.
Challenge 2: Scalability
Orchestrating AI workflows at scale can lead to performance bottlenecks if resources aren’t managed efficiently, leading to downtime and slowing when faced with large datasets or demand spikes.
Solution: Use cloud-based tools for AI orchestration that dynamically adjust resources based on demand for system elasticity.
Challenge 3: Security risks
AI systems and orchestration tools may handle sensitive customer data, increasing the risk of data breaches, noncompliance, and security vulnerabilities.
Solution: Implement robust security protocols, routine security audits, and making sure that all systems comply with relevant security standards and safeguard customer data.
Challenge 4: Latency issues
Ensuring low latency for real-time AI workflows can be difficult, particularly when multiple models or services are involved. High latency can degrade the effectiveness and user experience of dynamic AI systems used for high-frequency trading, chatbots, or cybersecurity.
Solution: Ensure task efficiency through the use of edge computing, streaming data pipelines, parallel processing for workflows, and data preprocessing.
Challenge 5: Interoperability
There are currently no universal standards for AI orchestration frameworks or tools, which makes interoperability between systems a challenge. This lack of standardization complicates the implementation of new solutions, potentially leading to system fragmentation or vendor lock-in.
Solution: Promote consistency and compatibility by opting for well-established frameworks and tools for AI orchestration, including standardized interfaces and communication protocols. Implement coding and naming conventions to maintain clarity and consistency across scripts, models, and configurations to facilitate collaboration and maintenance.
Best practices for AI orchestration
If you’re considering how to approach AI orchestration in your organization, here are the best practices to help guide you to success:
1. Start with a pilot program
Begin with a small test project that integrates a few AI systems or just focuses on one specific workflow. This allows you to manage the complexities of AI infrastructure and understand its nuances on a smaller scale before expanding to more complex workflows.
2. Focus on data quality and access
AI systems require access to clean, well-organized, and high-quality data to function effectively, especially when it comes to decision-making, interoperability, and real-time workflows. Employ data scientists to validate data pre-processing, quality, and optimize data pipelines.
3. Design modular AI workflows
Organizations can simplify the management of AI workflows and orchestration layers with a modular approach to system architecture. By designing AI workflows as a series of modular building blocks, individual components can be modified or replaced without disrupting the entire system.
Modular architecture is also reusable, helping to promote efficiency and consistency across future projects. Each component is decomposed into small, loosely coupled services that can be developed, deployed, or scaled independently, offering greater value and flexibility to an organization.
4. Invest in AI skills
AI orchestration requires expertise with machine learning, natural language processing, data science, software engineering, and complex integrations. Equip your development team, data scientists, and engineers with the skills required to tackle AI orchestration and infrastructure effectively. This involves ongoing training and AI education to keep pace with evolving best practices and AI technologies.
5. Establish governance and security
Establish guidelines for data privacy, security, and responsible AI usage that are compliant with industry regulations. To mitigate risk, design orchestrated workflows with privacy, data, and compliance in mind. Also implement robust security measures within data processes—such as secure APIs, data encryption, and regular security audits—to protect against vulnerabilities.
6. Monitor and optimize
Continuously monitor and optimize your orchestrated AI systems and workflows to ensure they deliver business value. Track the efficiency and performance of data processes in analytics, and gather user feedback to identify areas for improvement as part of a data-driven, iterative approach to success.
The leading AI orchestration tools
AI orchestration requires a varied toolkit for managing, deploying, and optimizing the operations of AI systems. Typically, a central orchestration platform coordinates the interactions of the different AI components, enabling AI workflow automation as well as managing and tracking data flow, memory, resource usage, and handling failure events.
Here are some of the leading tools for AI orchestration:
Apache Airflow
Airflow is an open-source platform for orchestrating complex end-to-end workflows. It allows you to define, schedule, and monitor AI agentic workflows, plus set up and manage data pipelines required to feed data into AI models, ensuring models are trained, up-to-date, and performing optimally. Often used in machine learning and data science pipelines.
Kubernetes
Kubernetes is an open-source platform that automates the deployment, operations, scaling, and management of containerized AI applications. It provides the foundation for orchestration in cloud environments, serving as a central hub for efficient management across the AI lifecycle, and is used to manage microservices and distributed applications. Kubernetes can also manage AI workloads, providing features like resource allocation, scaling capabilities, and monitoring tools for workload optimization.
Apache NiFi
Apache NiFi is an open-source data integration tool that supports the automation of data flows. Essentially a data pipeline builder, it provides a user interface to create data pipelines that are flexible and scalable. It supports data routing, transformation, and system integration, and is commonly used for data ingestion, transformation, and movement in AI and data analytics workflows.
MLflow
MLflow is an open-source platform for managing and streamlining the end-to-end machine learning cycle. It supports multiple cloud platforms and ML frameworks, and offers components for tracking experiments, code packaging, and deploying models.
Flyte
Flyte is an open-source workflow orchestration platform for building, scaling, and managing production-grade AI pipelines. Built to handle large-scale data processing, it offers the scalability to meet demanding workloads across different teams and technologies in an organization. Flyte also focuses on reproducibility, allowing you to create ML and data pipelines that are repeatable and auditable through versioning and tracking.
Trends in AI orchestration for 2025
The market for AI orchestration is forecast to grow by 21% from 2024 to 2032. Driving this demand is the increasing complexity of business processes, the need to manage large volumes of data in real-time, and the pressure to get real value from AI projects.
Here are a few more trends in AI orchestration to watch for 2025:
1. Specialized AI models: The innovation of vertical AI agent solutions allows for using multiple specialized AI models that are fine-tuned to specific tasks instead of general purpose models. This promises to increase effectiveness, accuracy, and ROI of orchestrated systems.
2. Model gardens: This is a repository of tried and tested AI models. For AI orchestration, this could enable a business to quickly switch between models at any time. Not only does this increase flexibility when faced with new use cases, but it also reduces reliance on any single AI system.
3. Multi-cloud integration: AI orchestration is evolving to manage AI operations across hybrid and multi-cloud environments, enabling more robust data management and processing capabilities
4. Autonomous systems: There's a growing emphasis on creating self-managing and self-healing AI systems that can operate without human intervention, enhancing resilience and reducing downtime.
AI orchestration in 2025
With AI tools proliferating, it’s becoming clear that the true value of AI comes from seamlessly integrating a diversity of intelligent tools into core processes to create efficiencies and cost savings.
This is where AI orchestration comes in. It coordinates AI tools and processes, maximizing their scalability, efficiency, and cost-effectiveness to allow for the possibility of effective AI workflow automation.
Deploying AI orchestration platforms, however, demands a substantial investment in both technology and skilled personnel. As a result, few have taken the leap. But as AI adoption continues, those adept in orchestration will enjoy a competitive edge in the form of enhanced efficiency, scalability, decision-making, and end-to-end automation capabilities.
Interested in learning how Sendbird can help with AI orchestration for AI agents? Contact our friendly team of AI experts.
