Orchestration is Key to Unlocking Enterprise Value from AI Agents

With AI agents dominating discussions about business automation, it’s important to understand their role in the broader evolution of process automation. Equally essential is recognizing how agentic AI is fundamentally changing the way in which solutions are now taking shape.

According to a report from Futurum Research, 89% of CIOs (chief information officers) consider agent-based AI a strategic priority, and they say: 

“Agent-based AI will drive up to $6 trillion in economic value by 2028, accelerating AI’s role in automating enterprise workflows.” 

In essence, the role of AI in such workflows is to enable machines to understand, process, and respond to human language in a natural, conversational manner. This allows humans to interact with technology as effortlessly as we do with one another. With the proper architecture, it also allows AI agents to become collaborators that can do real work on our behalf.

Eventually, as agentic systems become more deeply integrated into organizations, AI agents can create their own process automations — ushering in a bold new era for both business and technology. 

So how did we get here? One of the core objectives of process automation is to achieve a higher degree of straight-through processing (STP). This is only possible with the right mix of automation tools and capabilities, including agentic AI. However, agentic AI-led automation requires an orchestration layer to improve visibility, reduce complexity, and effectively manage agentic workflows. 

An agentic AI orchestration platform can simplify processes, ease the deployment of AI agents, and enable them to interact with all the tools and systems they need. Keep reading to explore how orchestration unlocks the full potential of agentic AI.

The Evolution of Process Automation Over the Last Decade

For many years, business automation was synonymous with Robotic Process Automation (RPA). A typical RPA workflow might capture information like customer contact information, invoice totals, and items ordered, transfer the data into a database, and alert an employee about the action. 

Even though RPA can reduce human error, improve compliance, and scale up to handle increased workloads, its solutions are brittle, requiring specific inputs to run effectively.

RPA remains a foundational solution for task and process automation, but is limited by its strictly rule-driven nature. It hits a wall when faced with unstructured data. 

In response to the rise of large language models (LLMs), RPA vendors have introduced  Agentic Process Automation (APA), which involves the use of AI Agents to automate processes, employing LLMs to improve decision-making and execution capabilities. Still, APA is only good enough for semi-dynamic workflows that require adjustment based on inputs. 

For dynamic, non-linear workflows that require real-time decision making, agentic AI excels where APA falls short — handling low-volume, highly-unstructured, or complex tasks with greater adaptability. Business Process Automation (BPA) is a less dogmatic approach, in that it trends toward including AI to understand natural language and semi-structured data. BPA might be more complex and span multiple departments, depending on business goals. 

With their predictive prowess, large language models (LLMs) have unlocked new ways to approach this kind of automation. LLMs can pull data out of natural language, whether it’s from a PDF, a website, or a customer request over chat or voice, meaning that these process automations no longer require input from a fixed source. While this creates a wealth of opportunity, it also adds a proportional amount of complexity to how an organization integrates its data and legacy systems into their agentic ecosystem.

This might feel like an extension of BPA or Intelligent Process Automation (IPA), but there is an important distinction. Gartner coined the term “hyperautomation” to describe the state that an organization is in when new automations are continually being designed, deployed, and iterated on. These automations are improving upon the ways in which humans alone are able to complete tasks and they are being designed in close collaboration with the people who best understand the skills that are being automated.

Hyperautomation (Figure 1) involves a solution architecture that might include robotic process automation (RPA), intelligent business process management suites (iBPMS), integration PaaS (iPaaS), process mining, and business rules management systems (BRMSs) or decision management suites (DMSs). Regardless of which acronyms apply to an organization, it’s critical to figure out how they fit into a broader technology ecosystem that can support such a bold and tumultuous journey.

Figure 1: Hyperautomation as a set of automation tools mitigating functional and process silos

Source: Gartner

Achieving a Symphony of AI Agents 

While the initial steps toward hyperautomation are similar to those taken with RPA or BPA — identifying processes and designing automations — the design and development phase are enhanced (and interconnected) through the use of no- and low-code building tools. These tools allow anyone in an organization to build, test, and iterate on solutions.

With traditional barriers to technology lowered, organizations can take an approach to automation that’s more agile than ‘Agile’ — where iterations on solutions are happening on an hourly basis, rather than  the weekly or monthly timelines for typical software builds. This level of agility requires an orchestration platform that is flexible enough to leverage all of the best tools in the marketplace at any given moment.

Successful implementation of agentic AI demands an ecosystem where a shared library of information, patterns, and templates join with code-free design tools to produce high-level automation and continual evolution. An orchestration layer improves visibility, reduces complexity, and effectively manages agentic workflows. Users can initiate action by using conversational prompts to provide AI agents with objectives, and agents can take action either by executing existing code or writing their own. Working together in swarms, AI agents can share skills and information to complete increasingly sophisticated tasks, with humans providing training, guardrails, and interventions as needed.

This creates a veritable symphony of automation that can be leveraged in ways that are practically limitless. We’ve already seen a massive retailer use this approach to address challenges in maintaining its high standard of customer service, as inefficient management and staff at retail locations was leading to customer dissatisfaction with call centers.

Over the course of a year, 350 individual production releases were launched across store locations nationwide, quickly leading to a $3 million increase in gross profit, with a projected annual increase of $80 million. The solutions significantly reduced calls to stores by 47% and the company achieved a remarkable NPS (net promoter score) of 65. These significant gains were made by isolating processes that were ripe for automation and applying an agentic AI-first approach to designing solutions.

Agentic AI Orchestration is Essential for Value Realization

It is tempting to want to believe that the proliferation of AI agents available in the marketplace will make integration easier. Unfortunately, hurling isolated agents at isolated workflows is a costly approach that sets organizations back. What drives agentic AI beyond RPA, BPA, APA, and IPA is the ability for AI agents to collaborate with other agents and the humans within an organization to not only execute automations but also seek out improvements to them.

Figure 2: Business Orchestration and Automation Technologies (BOAT) is a Unified Platform for Automation

Source: Gartner

In their “Quick Answer: Beyond RPA, BPA and Low Code — The Future Is BOAT” report, Gartner identifies an emerging class of software that “enables enterprises to automate and orchestrate end-to-end business processes while connecting multiple enterprise systems of records via any applicable integration method.” Using the acronym BOAT, Garter highlights “business orchestration and automation” platforms that encompass BPA and RPA while also leveraging technologies such as integration PaaS (iPaaS) for deep integration and low-code application platforms (LCAPs).

Business leaders need to move past RPA and BPA and look for opportunities to engage critical team members across their organizations to actualize the promise of agentic AI. Gartner estimates that agentic AI will by autonomously resolving 80% of common customer service issues without human intervention by 2029. They project that these heightened levels of autonomous automations will lead to a 30% reduction in operational costs. However, these numbers will only be in reach for organizations that approach these technologies with a clear strategy and a willingness to look beyond what’s worked in the past.

Agentic AI goes well beyond RPA, BPA, APA, IPA, machine learning, LLMs, generative AI, and even AI agents themselves. The goal is to build a technology ecosystem where these various components can be sequenced to work in concert. An emerging class of software is enabling enterprises to connect multiple enterprise systems to automate and orchestrate end-to-end business processes.

Conclusion

The effective orchestration of AI agents goes beyond simply automating business processes. The goal is to create a technology ecosystem where automations are being designed, deployed, and evolved on a regular basis by people across the organization, regardless of their technical background. Any platform for this level of orchestration needs the flexibility to plug and play with market-best tools at any given moment and the openness to leverage existing systems and data sets.

Agentic AI-led automation requires an orchestration layer to improve visibility, reduce complexity, and effectively manage agentic workflows. Such agentic orchestration offers a greater control over the lifecycle and execution of business processes.

Without orchestration of AI Agents, enterprises will be adding more complexity to a spaghetti architecture for automation tools. Such a peace-meal approach, even for experimentation and iteration with Agentic AI, will be difficult to scale for enterprises.

OneReach.ai’s GSX agentic AI orchestration platform allows organizations to simplify processes, streamline AI agent deployment, and enable seamless interaction with necessary tools and systems. It empowers organizations to model, implement, operate, monitor, and optimize their long-running processes.