Why Orchestration is Key for Unlocking Agentic AI

The enterprise landscape is undergoing a seismic shift as Artificial Intelligence (AI) evolves from generative tools into an autonomous agent capable of executing complex tasks. As the technology takes root, enterprises face a dual challenge: unlocking AI-driven value while managing the operational sprawl.

How can we safely incorporate agentic AI into existing processes without undermining trust? How can we govern and audit AI agents while maintaining their dynamism?

The key to overcoming these challenges is orchestration, a powerful way to strategically coordinate and integrate systems, applications, agents, and humans into durable, well-governed, and efficient processes.

In this post, we’ll explore why orchestration will continue to be foundational technology, even for the next decade of AI-native enterprise systems.

The rise of agentic AI

Agentic AI refers to goal-driven systems capable of autonomous action, planning, and decision-making. Unlike AI systems that require explicit instruction for each task, Agentic AI means that AI agents complete tasks in a self-governed manner, even in close collaboration with humans or other agents.

Learn the difference between agentic workflows and AI agents.

The agentic momentum is underfoot. Gartner, Forrester, and McKinsey have all pointed toward Agentic AI as a top trend in 2025. In fact, Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024.

Despite these projections, the road to widespread adoption will be fraught with challenges. Gartner expects that over 40% of agentic AI projects may be scrapped by 2027 due to the costs and risks of actually deploying agentic AI into production. This underscores the need for a robust orchestration framework to enable the successful deployment and integration of agentic AI at scale.

The state of technical overhead today

The adoption of agentic AI doesn’t happen in a vacuum. Enterprises must figure out how to integrate it into their existing systems and technology, which adds to the operational sprawl. Here are some of the tools and challenges that businesses already face.

Phase 1: the age of the monolith

In the early stages of enterprise automation, organizations relied heavily on monolithic architecture for application design, often tapping into BPMN (business process model and notation) tools to speed up development. While these solutions provided centralized control and performance agility, they also introduced significant limitations:

• Tightly coupled components hindered scalability
• Vendor lock-in led to restricted flexibility
• Upgrades and integrations were complex, time-consuming, and costly

Phase 2: APIs and low-code tools

The need for better integration across different systems and services drove the proliferation of low-code tooling. RPA (robotic process automation), APIs, and iPaaS (integration platform as a service) emerged to address the shortcomings of monolithic systems by promoting modularity and interoperability.

While these tools made automation across multiple services much easier, they also led to challenges like increased operational complexity, system brittleness, and technical overhead across a fragmented technology landscape. The accelerated development came at the cost of resilience, visibility, and governance.

Phase 3: the promise of AI agents

AI agents hold much potential to revolutionize business operations by automating dynamic processes, increasing productivity, and providing insights across vast unstructured data. But in reality, incorporating AI agents into business opportunities presents three main challenges: the technical know-how, AI trust and safety, and compliance.

Beyond the first hurdle of getting the right technical expertise and resource to build out an agentic use case, comes the real implementation challenges. Lack of trust in AI agents is cited as one of the top few obstacles, along with compliance risks and the need for human oversight, adding a new set of considerations to existing ones.

Enter orchestration: why it matters

Orchestration is the missing layer that enables enterprises to shift from automation silos to integrated and governable systems with agentic capabilities.

Orchestration refers to the coordination of distributed systems, middleware, and services to execute complex, automated flows. In the context of agentic AI, orchestration serves as the backbone that enables seamless interaction between AI agents, human users, and enterprise applications. Effective orchestration is crucial for:

• Integration: Ensuring that AI agents can access and interact with various data sources, APIs, and services.
• Governance: Maintaining control over AI-driven processes, including compliance with regulatory standards and internal policies.
• Scalability: Scaling up AI-driven processes without compromising performance, speed, or reliability.
• Observability: Providing visibility into AI operations for trust, monitoring, debugging, optimization, and auditing.

Orchestration layer serves as the central control plane over all its moving parts — what each task has done, what triggered a failure, what the AI agent decided on, et cetera. Without a robust orchestration framework, organizations risk exacerbating operational challenges across governance, safety, and performance and introducing unnecessary complexity, instead of realizing the full potential of agentic AI.

The future of AI is orchestrated

Today, organizations spend more on technology maintenance rather than on innovation. Legacy systems have also been shown to hinder efficiency, slowing down agility in pivoting and adopting emerging technologies like agentic AI.

Orchestration offers the opportunity to streamline fragmented and outdated processes and tools, flipping that ratio from maintenance to innovation. It’s pivotal enough that Gartner has coined the term Business Orchestration and Automation Technologies (BOAT), which are a class of technologies expected to:

• Enable end-to-end automation of business processes
• Connect disparate enterprise applications in a platform-agnostic and hybrid manner
• Augment business operations with embedded AI capabilities
• Provide a unified interface for managing diverse workflows deployed across different formats (web applications, conversational chatbots, etc)

There’s no doubt that the enterprise landscape is converging around orchestration, connectivity, and agentic AI.

Agentic use cases across industries

The possibilities for agentic AI are vast, spanning across high-volume, manual work like:

• Data and analytics —
  • Agents that conduct financial analysis and research, enabling human advisors to focus on strategy rather than data collection.
  • Agents that analyze transactions and flag suspicious patterns, improving security and regulatory compliance.
• Case management —
  • Agents that conduct rigorous safety audits quickly and accurately, improving the turnaround time from weeks to hours.
  • Agents that manage customer queries, file and escalate tickets, and resolve common issues, providing more in-depth support at speed.
• Assistive copilots —
  • Agents that draft legal contracts, assign risk scores, and recommend ways to optimize operational impact.
  • Agents that track brand mentions and interact with followers on social media to manage online brand presence.

In most cases, autonomous agents are incorporated into a wider business process with discrete stages. For example, an audit agent that reviews dozens or hundreds of documents for compliance will eventually need to pass off its findings to a human reviewer, and later combine all the audits into a single summary report. Orchestration enables organizations to plug new technologies like AI agents into existing flows, with proper governance and observability.

Orkes Conductor: the Enterprise Orchestration Platform

Orkes Conductor is an orchestration platform designed to meet the demands of modern enterprises embracing agentic AI. Originating from Netflix, Conductor has been widely adopted by organizations across industries to manage complex workflows and integrate AI capabilities.

Key features

• Built for resilience: Supports dozens of resilience patterns like retries, timeouts, fallbacks, idempotency, and even event-driven architecture support.
• Comprehensive integrations: Supports seamless connectivity with dozens of LLMs, vector databases, and other enterprise services, connecting legacy and modern systems without replatforming.
• Human-in-the-loop support: Facilitates workflows that require human approval or intervention, ensuring compliance and oversight.
• AI-ready: Comes with built-in support for prompt engineering, testing versioning, and runtime injection.
• Security and governance: Provides granular role-based access control (RBAC), secret management, audit logs, and compliance features to maintain data integrity and security.
• Scalable infrastructure: Enables millions of concurrent workflows with minimal latency across APIs, event-driven systems, enterprise applications, LLMs, agents, and more.
• Flexible development: Facilitates no-code, low-code, or full-code language-agnostic development options with seamless switching across each option.

Real-world use cases

Organizations leveraging Orkes Conductor have reported significant improvements:

• Banking: A national bank enhanced its open banking integration, reducing issue resolution times and operational support costs.
• Healthcare: A leading provider achieved a 70% reduction in development lifecycle and a 60% cost reduction in operations by streamlining onboarding processes.
• Manufacturing: An electric vehicle manufacturer optimized its supply chain management, redirecting 90% of development time toward innovation.

Summary

As enterprises navigate the complexities of integrating agentic AI, orchestration emerges as the foundation for success. By providing a cohesive framework for managing AI agents, human interactions, and system processes, orchestration platforms like Orkes Conductor empower organizations to unlock the full potential of AI-driven transformation.

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Ready to orchestrate complexity into coordination? Conductor is an enterprise-grade orchestration engine for process automation, API and microservices orchestration, agentic workflows, and more. Check out the full set of features, or try it yourself using our free Developer Playground.