Credit: Unsplash For decades, enterprise software has been designed primarily to record what businesses already know: customer information, financial transactions, employee activity, inventory, sales pipelines, and operational decisions. These platforms became the systems of record that organizations relied on to maintain a consistent version of the truth. Artificial intelligence is beginning to challenge that model. The emergence of multi-agent AI systems could push enterprise software toward something more dynamic: systems of action that do not simply store information but interpret it, coordinate decisions, and initiate tasks.
The shift is significant because enterprise workflows rarely consist of a single question with a single answer. A customer-service issue, for example, may require reviewing account history, checking contractual terms, identifying a potential solution, updating a ticket, and communicating with another department. A single AI assistant can help with individual steps, but coordinating an entire workflow requires something closer to a team of specialized digital workers. That is where multi-agent orchestration enters the picture.
For Swaroop Borukar, a seasoned Product Manager building in the AI Infrastructure domain at Workday, this represents a fundamental change in how enterprise applications can be conceived. Swaroop says, “The real opportunity with multi-agent AI is moving beyond systems that simply answer questions. By giving specialized agents defined responsibilities and the ability to coordinate, enterprises can begin building software that understands a business objective and helps execute the workflow required to achieve it.” Swaroop Borukar, product manager at Workday — Credit: Workday Why single-agent AI isn’t enough for enterprise workflows The first generation of enterprise generative AI largely focused on the chatbot or copilot model. A user asks a question, the AI retrieves information or generates content, and the user decides what happens next.
That approach remains useful, but complex enterprise environments demand more. Modern businesses operate across dozens or even hundreds of interconnected applications. A workflow might span CRM platforms, financial systems, project-management tools, internal databases, and communication applications. Each system has its own permissions, data structures, and business rules.
A multi-agent architecture can divide these responsibilities among specialized agents. One agent might retrieve relevant information, another could analyze it, a third could determine what action should be taken, while another validates the proposed action against organizational policies. “A single general-purpose agent is not always the best architecture for complex enterprise environments,” Swaroop explains. “Specialized agents can be designed around specific functions, permissions, and business rules, making the overall system more controllable, observable, and reliable.” The objective is not simply to have multiple AI models running simultaneously. It is to create an architecture in which agents have defined responsibilities and controlled ways of interacting with one another and with enterprise systems. This distinction is important.
Without effective orchestration, adding more agents can create more complexity rather than greater efficiency. For technology leaders, the interesting question is therefore not whether enterprises can connect AI models to their software. It is how they can build an architecture in which autonomous components collaborate reliably enough to participate in real business processes. Inside the architecture of a multi-agent enterprise system A well-designed multi-agent system resembles a coordinated organization more than a conventional chatbot.
At its foundation is an orchestration layer responsible for determining which agent should handle a particular task, what information it should receive, and what should happen after its work is completed. Individual agents can then be optimized for narrower functions rather than being expected to solve every problem. This specialization can have practical advantages. An agent designed for financial reconciliation, for instance, can operate under different rules from an agent responsible for customer communications.
A compliance-focused agent might verify whether a proposed action is permitted before another agent executes it. The architecture can also incorporate tools and APIs, allowing agents to interact with existing enterprise systems rather than operating in isolation. For Swaroop, the engineering challenge is making those interactions predictable and observable. “The intelligence of an agent is only one part of the equation,” she says. “Enterprise systems also need strong orchestration, well-defined interfaces, secure access to data and tools, and observability across the entire workflow. Without those foundations, adding more agents can simply add more complexity.” However, orchestration introduces its own engineering challenges.
Developers must account for communication between agents, conflicting recommendations, incomplete information and situations in which an agent makes an incorrect assumption. The system needs mechanisms for detecting errors and determining when a task should be escalated to a human. This makes multi-agent AI as much a systems-engineering problem as an AI problem. The quality of the underlying language model matters, but so do the surrounding architecture, data access, observability, authentication, and workflow design.
Designing guardrails for autonomous AI Giving an AI system the ability to generate an answer is fundamentally different from giving it the ability to change something in an enterprise environment. An incorrect summary may be inconvenient. An incorrect payment, customer-account modification, or regulatory decision can have substantially greater consequences. Consequently, enterprise multi-agent systems require carefully designed guardrails.
Permissions are one important layer. Agents should only be able to access the information and tools necessary for their assigned responsibilities. A customer-support agent, for example, may need access to account information but not unrestricted access to financial systems. Human oversight is another.
Not every action needs manual approval, but organizations can establish thresholds where higher-risk decisions are automatically routed to a person. Auditability is equally important. Enterprises need to understand what an agent did, which information influenced its decision, and which other agents or systems were involved. Without that visibility, diagnosing failures becomes difficult.
For Swaroop, these safeguards need to be incorporated into the architecture from the outset rather than added after deployment. “Autonomy in an enterprise environment has to be engineered, not assumed,” says Swaroop, Product Manager at Workday According to her, agents need clearly defined permissions, auditability and escalation mechanisms so that organizations can benefit from automation while retaining human oversight over decisions where the consequences are significant. The architecture therefore needs to treat autonomy as a spectrum rather than an on/off switch. Low-risk, repetitive tasks can potentially be automated end-to-end. More consequential activities can require validation, additional agent review, or explicit human approval.
This approach creates a more realistic path toward enterprise adoption: increase autonomy where the risk is manageable while maintaining control where the consequences are significant. The infrastructure challenge behind agentic AI There is another challenge that receives less attention outside technical circles: economics. Running one AI interaction is relatively straightforward. Running a complex agentic workflow involving multiple agents, repeated model calls, retrieval systems, tool executions, and
