Building a Core Enablement Team for AI Agent Automation and Orchestration

Building a capable team forms the basis for AI agent automation. More than just deploying tools, this team must create an interconnected ecosystem where AI agents collaboratively optimize and adapt to drive hyperautomation.

The core enablement team capable of leading AI agent automation and orchestration resembles the ‘fusion teams’ described by Gartner [1]. These are multidisciplinary groups, blending technology, analytics, and business domain expertise and sharing accountability for business and technology outcomes. Instead of organizing work by functions or technologies, fusion teams are typically organized by the cross-cutting business capabilities, business outcomes or customer outcomes they support. There’s generally at least one IT person on a fusion team, and they work best with a diverse makeup in terms of function, ethnicity, and gender identity.­

One key role sets core enablement teams for agentic AI apart from traditional fusion teams: the Strategic Liaison (SL). This article explores the SL’s unique responsibilities, along with the other essential team members who make up this critical organizational unit as shown in Figure 1.

Figure 1: Dynamics of a Core Enablement Team

Strategic Liaison (SL)

The Strategic Liaison (SL) acts as a bridge between organizational goals and technological capabilities. They’re ­the ­glue, ­binding­ winning ­teams­ around­ a ­vision­ and­ organizing the resources­ needed­ to­ realize­ it.­ The SL understands the­ possibilities­ of AI agent automation and orchestration and­ can nimbly evangelize the benefits it will bring ­throughout­ the ­organization.

The SL has a strong understanding of both business strategy and technology, excellent communication skills, and ability to align business needs with technical solutions. The SL is adept at collaborating with stakeholders to identify problems and opportunities. They bring together the business groups involved around a strategy and introduce them to the tools, training, and processes that bring agentic AI to life.

Over the course of a single day, the SL might find themselves ­engaged­a wide array of tasks, such as:

• engaging in­ technical­ work­ with­ the­ operations­ team
• refining the ­design ­of­ a contract ­renewal­ process with the legal department
• adapting skills used by AI agents in one ­department­ for workflows in another
• monitoring interactions as they happen and optimizing them on the fly
• optimizing workflows based on user feedback.

The SL navigates the organization like a connective thread, weaving together machines, users, processes, and outcomes so that AI agent automation and orchestration can thrive. These are the other key roles that make up an effective core enablement team.

Lead Experience Architect (LXA)

The Lead Experience Architect (LXA) is responsible for facilitating, generating, and executing great user experiences. The quality and consistency of experiences offered by AI agents are under this person’s purview. This is a leadership role that demands established knowledge of human-centered design, interaction design, and research. Acting as a coach, mentor, and leader throughout the process of AI agent automation and orchestration, the LXA gets their hands dirty working to map the journey for interacting with skills and IDWs in close harmony with the core enablement team, and conversational experience designers in particular. The LXA brings experiences to life while empowering business groups to independently create and evolve them — without always relying on the core enablement team. This person has the crucial duty of building and managing the road map of skills being created and improved by the core enablement team in collaboration with various business groups.

Aaron Cooper, Senior Director of Digital Experience at Banner Health has been doing the work of an LXA dating back to his time at Honeywell leading internal automations for human resources. This included use cases for HR professionals in call centers trying to answer employee questions about time off or pay schedules. “[It could also] be a manager wanting to understand ‘what’s my regrettable turnover look like,’” Cooper said on the Invisible Machines podcast.“It really spanned personas for employees as well as managers and it spanned technologies as well.” Cooper described this role as “delivering new value through new experiences” and it often involves working closely with other leaders across departments to make sure design thinking is part of the general strategy. [2]

Experience Designer (XD)

The experience designer (XD) takes high-level requirements and turns them into flows that support the right experience. An XD can emerge from any department. They don’t need development experience but must have great communication and problem-solving skills. XDs are versed in conversational design principles and have a strong enough command of an organization’s agent-building platform to train others. The work of the XD becomes the public face of an organization to anyone interacting with its AI agents.

Data Analyst/Architect (DA)

The DA is responsible for creating and structuring processes that measure outcomes, extract insights, and generate predictions across an organization. They design and implement data workflows that facilitate real-time analytics, enabling immediate feedback and iteration within an ecosystem. DAs have an analytical mindset, experience with data visualization, and familiarity with databases. They will take on challenges like mapping interaction data and designing analytics for real-time reporting. They create the backbone that allows agentic AI systems to process vast data, learn continuously, and adapt in real time.

Technical Architect/Developer (TA/D)

A Technical Architect/Developer (TA/D) plays a vital role in the development and maintenance of extensible cognitive architectures for multi-agent ecosystems. These are the key responsibilities and capabilities associated with this role:

• Designing and implementing microservices: TA/Ds break down workflow skills into modular, reusable components known as microservices. These are shareable, flexible pieces that AI agents can sequence into flows and used in orchestrations across an organization.
• Customizing and fine-tuning: TA/Ds develop custom steps, views, and dashboards. They fine-tune microservices at both macro and micro levels to adapt automations to changing needs.
• Supporting code-free creation: TA/Ds enable non-developers to build or update AI agents using visual, drag-and-drop tools, making complex integrations accessible across the organization.
• Managing architecture and security: This role ensures that the infrastructure supports low latency, flexible integrations, security, compliance, and proper versioning.
• Continuous improvement: TA/Ds help refine skills, flows, and microservices iteratively by leveraging real-time analytics, debugging, and feedback loops.

The TA/D is pivotal in establishing and maintaining open, flexible, and scalable cognitive architectures that enable agentic AI to flourish inside of organizations. This role supports the evolution towards organizational AGI (Artificial General Intelligence) — or self-aware organizations — by ensuring AI agents operate with agility and adaptability.

Figure 2: Diverse Makeup of a Core Enablement Team

Human-in-the-Loop (HitL)

Human-in-the-Loop is both a human role and technological capability. When an AI agent needs assistance, it calls on HitL. That human can emerge from anywhere in the organization. The requirement is that they are the person who can either provide the AI agent with the information it needs or carry the workflow forward. HitL is a core requirement of any platform for AI agent automation and orchestration. It is the glue that holds an emerging ecosystem of AI agents together. As OneReach.ai co-founder and CEO Robb Wilson says in his bestselling book, Age of Invisible Machines, “This role lets people play directly to their strengths, requiring little training. To prepare for this role, a HitL only needs to learn how to communicate effectively with AI agents.” [3]

Steering Committee (SC)

A steering committee is a group of senior leaders responsible for guiding the organization’s strategy for agentic automation and orchestration. They will be tasked with ensuring initiatives align with and drive business objectives, which requires a deep understanding of the way that value accumulates with agentic automation and orchestration. The SC will need to make sure that everyone from c-suite down is embracing an endeavor that begins with failure. These early failures are fundamental to building a flexible architecture for multi-agent orchestration, by helping organizations zero in on what processes to automate and how to automate them best. Agentic AI also provides the opportunity to iterate on failures quickly — as fast as daily or hourly — and the SC understands why this creates a competitive edge.

The SC also functions as the center of excellence. They will work closely with the strategic liaison to ensure the overall vision is clear and cogent. Overseeing risk and compliance and grappling with the ethical considerations that come with agentic AI are also key functions of the SC. As are removing obstacles to adoption and accelerating the release of valuable agentic experiences for employees and customers.

Conclusion

The task of agentic automation and orchestration is herculean, but assembling a core enablement team distributes the lift. By focusing on key technical requirements for this level of automation while also evangelizing the value that it will bring across the organization, this team drives opportunities for innovation. As business leaders build this critical group, they should look for team members who are cross-disciplinary thinkers, advocates for open ecosystems, and skilled communicators. By assembling a team that encompasses these roles, leaders can ensure their organization is well-equipped to harness the full potential of agentic AI and move in the direction of organizational AGI.

OneReach.ai’s GSX AI Agent 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.