Scaling Agentic AI Across the Enterprise

To effectively scale Enterprise Agentic AI, organizations must shift focus from individual chatbots to a multi-agent system orchestration strategy. This ensures that the right agent performs the right job, working in tandem with other specialized agents to deliver end-to-end resolution.

Real-World Application:

Consider an employee filing a complex support ticket. A standard bot might simply log the text. A Scalable Agentic System understands the full context, autonomously routes the request to a specialized “IT Agent” or “HR Agent,” and triggers a remediation workflow—all without human intervention.

Building the Foundation: Governance and Reinforcement Learning

Before scaling AI agents, organizations must establish a “trust layer.” This involves robust Reinforcement Learning from Human Feedback (RLHF) frameworks that allow Agentic AI systems to ingest and learn from enterprise knowledge, such as historical ticket resolutions and dynamic knowledge articles.

This Responsible AI approach ensures two critical outcomes:

1. Trust & Alignment: Learning is strictly governed by enterprise security and policy guardrails.
2. Continuous Improvement: The system creates a flywheel effect, where every resolved ticket makes the entire agent network smarter.

Selecting the correct platform for building and scaling this enterprise-wide deployment is not just a technical choice—it is the critical factor that determines the long-term success of your AI strategy.

Some of the key components of Enterprise agentic AI strategy, and hence its platform, are:

Domain-specific deployment helps build a modular and scalable backbone to the organization’s agentic AI system, all while minimizing costs that would otherwise be spent on foundational LLMs.

It is important to have specific domain-oriented agents that can perform domain-specific tasks. These tasks will use domain-specific LLMs and other AI techniques to understand contexts and fulfill the task.

A modular structure means having one agent for IT tickets, one for customer cases, one for assisting users with drafting RFPs and sales collaterals, etc. Having this level of modularity helps train and continuously evolve that agent to better understand the domain and comprehend the context of user queries and needs.

While building and deploying enterprise agentic systems, it is important to ensure that there is a single point of service for all user interactions. This means that the system, and not the user, needs to understand which agent to route a certain user query to, even when there is ambiguity in user utterance. This is done using a universal agent supported by an AI reasoning and context disambiguation engine.

The universal agent acts as a single entry point for user queries and uses the reasoning engine to break down the query into tasks for various domain-specific agents. It is important to note that the user queries would be in a conversational manner and hence could contain multiple tasks across multiple agents and domains.

The context disambiguation service would work with the user in clarifying the purpose and context in cases where the original ask by the user is not clearly reasoned and classified for a unique domain-specific task agent. This is usually done by asking clarifying questions.

Modularity also helps make deployments manageable and scalable. The organization can start with one domain and benefit from its machine learning while implementing the next domain. As needs change, more agents can be added. Also, existing domain-specific agents can start supporting more tasks, have better context, and benefit from the continuously learning reasoning engine.

It is important that the entire agentic AI system is trusted, responsible, auditable, private, and secure. The platform chosen for agentic AI should not only support this at the domain level but at the enterprise-wide deployment level as well.

It is important to ensure responsible and auditable AI even while the tasks are being distributed to the domain-specific agents or when the output of each agent is being collated for fulfillment purposes through the universal agent.

It is imperative to have an AI agent that supports flexibility in building workflows that each task agent or the universal agent needs to execute. As agentic architecture scales in the organization, one should expect more fulfillment to happen through this system.

This requires that the creation of workflows should not just be simple and flexible but agentic by nature itself. This means that the agentic system should be able to create and maintain workflows with minimal conversational input from the team implementing the agents. Creating a workflow should be as easy as asking the platform in a conversational manner,  to create a workflow capturing, using data from one or more systems of records and performing certain tasks.

For example, if an organization wants to create an autonomous fulfillment workflow where “the AI agent picks up purchase requisitions from SAP, identifies purchase order quantities by checking existing stock in Oracle applications, checks for AP & Vendor policies in another system, creates a purchase order and sends it to the buyer for approval”, one should be able to tell the system just that and in a conversational manner.

The agentic AI system should be autonomous enough to understand this request and create and deploy the workflow with the necessary API calls and permissions applied. Similarly, the enterprise strategy should allow for multiple channels with omnichannel capabilities, so that the deployment strategy can adapt to human behavior and convenience (e.g. voice calls, chatbots, emails, messaging apps, etc.)

Agentic AI systems pave the path toward autonomous agents for your enterprise. This means that AI agents could be activated on demand (when a user requests a fulfillment) or these AI agents should proactively trigger workflows based on certain events. Ensuring that your agentic AI platform supports event-based triggering of workflows becomes a mandatory capability.

Deploying AI agents is usually a multi-phase approach. Organizations plan the deployment of task agents, universal agents, LLMs, and even use cases for a given task agent over a roadmap of a few months or quarters.

This needs your strategy and platform to not just be modular, flexible, and scalable, but also malleable enough to change and manage complex workflows as organizational learning improves over phases of deployment. For example, many workflows use voice channels when users are currently comfortable calling the service desk using telephones.

As users get comfortable with other low-cost digital channels, the enterprise strategy and platform should allow for easy modification of existing flows to replace voice with digital channels. This transition should be seamless for both the admins managing the workflows and the users and agents using the agentic AI system.

Continuous improvement with large language models (LLMs) through reinforcement learning is a critical aspect of enterprise agentic AI systems. It’s essential to analyze both successful and unsuccessful task agent deflections, as well as tickets resolved directly within the system of record, to continually identify opportunities for enhancing the agentic AI system.

The system should autonomously identify knowledge and content gaps, and auto-generate the same. This would make the agentic system more efficient and increase fulfillment rates, thus improving user satisfaction.

For example, an IT task agent can continuously keep track of resolutions provided by human agents for various IT incidents where no knowledge article exists. The system can then auto-generate the knowledge articles, which can be used by the IT task agent to resolve future tickets, thus reducing the workload of the service desk agents.

Another theme for continuous improvement is understanding the root cause of repetitive and major issues. A good AIOps platform ensures that cross-domain impacts are not ignored because of the distribution of domains across task agents. Just like the reasoning and disambiguation service, the AIOps functionality will be central to the enterprise deployment strategy and ensure enterprise-wide observability.

It is important to note that as fulfillments happen through and across domain-specific task agents, and as the universal agent orchestrates these fulfillments, agent AI systems should have built-in guardrails to ensure data privacy and governance.

This includes data minimization (collecting only necessary data), encryption, access control, audits, continuous monitoring, and incident retrospectives.

Deploying agentic AI at an enterprise scale has several significant benefits increased efficiency, cost reduction, enhanced decision-making, adaptability to changing business needs, time saving, scalability to handle increased workloads, and improved productivity at task and human levels

It is important though to ensure that the enterprise agentic AI strategy includes the components mentioned above in order to benefit your business processes. It is extremely important and urgent to ensure that a scalable enterprise deployment strategy is built on a holistic agentic AI platform.

Such a platform should support strategy components mentioned in this blog, and offer you a jumpstart with configurable and scalable features, pre-seeded domain-specific task agents, pre-seeded ontologies, support for multiple systems of records, and a huge variety of integration possibilities.

Scaling agentic AI across an enterprise brings transformative benefits such as enhanced efficiency, reduced costs, improved decision-making, and increased adaptability. However, achieving these results requires a well-planned strategy, a flexible platform, and domain-specific agents working seamlessly together.

By adopting a robust, scalable agentic AI platform, organizations can ensure continuous learning and improvement while maintaining trust, privacy, and governance.

Ready to experience how agentic AI can elevate your enterprise? Book a custom AI demo and discover how Aisera’s GenAI empowers your organization in a scalable, intelligent agentic AI system.