Why multi-agent systems are an enterprise architecture challenge

Why AI vendors and organizations are pivoting toward MAS

With advanced capabilities in reasoning, planning, execution and tool integration, multi-agentic AI systems can be deployed for numerous real-world enterprise use cases across applications such as financial operations, risk management, IT, sales, marketing, supply chain management, human resources and customer service. The growing potential for real-world applicability shows why industry interest and momentum in MAS are rising steeply.

For example, Gartner reports that, during the 15-month period from Q1 2024 to Q2 2025, MAS inquiries from organizations surged by 1,445%. Gartner cites MAS as one of the top 10 strategic technology trends for 2026 -- a strategic imperative that will empower leaders to build resilient foundations, protect enterprise value and shape the business landscape over the next five years. Gartner's Tom Coshow and Kiumarse Zamanian, both VP analysts and agentic AI experts, reiterate that MAS adoption will continue to accelerate across sectors worldwide, having already demonstrated measurable value in many business-critical applications.

Monolithic, single-agent AI systems continue to deliver value for targeted, repeatable tasks like repetitive workflow automation, narrow analytics, knowledge retrieval and context summarization. However, they also have a significant drawback: They struggle to reason, plan and operate within complex business workflows. Additionally, no single AI agent can effectively manage every process, especially since different processes operate under various rules and constraints and require distinct data sources, permissions and expertise to produce high-quality results.

To help enterprise customers mitigate these limitations, leading enterprise software vendors are increasingly pivoting toward MAS. Unlike standalone AI assistants, chatbots and retrieval engines, MAS are coordinated systems of dozens, hundreds or thousands of specialized AI agents that work together to intelligently plan and execute large-scale, complex tasks. These programs can operate not only within specific applications but also across multiple enterprise workflows, systems, platforms, business processes and departments.

MAS are beginning to emerge across ERP, HR, CRM, procurement, finance, IT service management, customer experience (CX) and collaboration platforms. These tools increasingly include dedicated AI agents that participate in broader business processes, not by operating in silos but by coordinating activities, sharing responsibilities and seamlessly completing interconnected tasks.

For example, Sprinklr offers an AI-native CX management platform that unifies all customer-facing functions. Within this platform, multiple agents work together to help organizations resolve customer issues faster, personalize customer interactions and boost conversions.

Similarly, Aisera AI from Automation Anywhere is an intelligent, agentic AI service management platform. Its domain-specific AI agents help to automate change and incident management and to facilitate proactive incident detection and root cause analysis.

Other vendors that offer agentic AI-enabled enterprise software include the following:

• Amazon.
• Braze.
• Kore.ai.
• Microsoft.
• Salesforce.
• ServiceNow.

The limitations of orchestrator agents

MAS can automate and streamline complex business workflows by breaking them down into manageable steps. This is something that single-agent AI systems are simply not equipped to do.

MAS can automate and streamline complex business workflows by breaking them down into manageable steps. This is something that single-agent AI systems are simply not equipped to do. Each agent within the MAS performs a relatively narrow role, but the "team" of agents works together to improve reliability, efficiency and scalability across workflows.

MAS present a critical challenge that organizations often overlook: ensuring agents share consistent assumptions, policies, data and definitions. While communication and coordination are solved problems -- most MAS use an orchestrator to manage specialized agents working toward common goals -- orchestration alone is not enough. Without a shared understanding across agents, organizations cannot achieve true agent-business alignment or realize strong ROI from their MAS investment.

How MAS expose existing enterprise architecture problems

Many organizations looking to adopt MAS see it as a software engineering problem: Define agent roles and personas, choose an orchestration framework, set up the control flow and memory and equip agents with tools -- and deployment is good to go. These technical details do matter for successful MAS setup. However, the chief concern facing many organizations is not technical but architectural. These challenges include the following:

• Fragmented data. When data is spread across different systems and stored in different formats, AI agents lack a unified source of truth. This makes it harder for them to communicate with other agents. They also might make contradictory decisions, hallucinate context or produce unpredictable or unreliable outputs.
• Conflicting business rules, policies and decision criteria. Overlapping or contradictory instructions can cause agents to override one another, resulting in operational gridlock. Also, a single agent's flawed decision based on a single rule can lead to cascading errors and even major system errors, as downstream agents use that decision as an operational baseline for their own decisions.
• Inconsistent definitions. The lack of a standardized, shared language when setting up a MAS might cause agents to misinterpret inputs or fail to execute coordinated tasks properly. They might also reinforce each other's mistakes, leading to compounding errors, or return irrelevant output when using external tools like APIs or databases.
• Disconnected workflows. Without connected, synchronized workflows, agents must contend with fragmented data and context loss during handoffs, resulting in higher latency, fragmented memory or compounding errors across the execution pipeline. Also, multiple agents might attempt to access or modify a shared resource simultaneously, creating a race condition that can lead to incorrect downstream decision-making or unpredictable or biased output. And even with an orchestration framework, disconnected workflows can cause goal misalignment among agents, leading them to lose sight of the broader business objective.

The growing challenge of accountability

Accountability can also be very challenging when multiple agents participate in the same business process.

For example, consider an AI-enabled algorithmic trading platform. This platform uses multiple AI agents: one to estimate market sentiment (Analyst), one to evaluate investment portfolio exposure (Risk Assessor), one to execute stock trades (Executor), and an Orchestrator to monitor MAS performance and resolve bottlenecks.

But what if one of the agents makes a mistake that leads to financial loss for the organization? Maybe the Analyst misread a news event. Or perhaps the Risk Assessor failed to set a proper safety cap prior to the trade. Without strong governance structures, traceability protocols and restrictions on the levels of autonomy available to agents, it can be difficult to determine which agent is actually at fault. Consequently, the system's outcomes are likely to be flawed, potentially causing significant real-world damage and eroding trust in the MAS.

What enterprise buyers should ask before adopting MAS

To avoid the issues highlighted above, organizations need to proactively evaluate the system's governance controls and accountability mechanisms before deploying it. These controls and mechanisms might include the following actions:

• Logging and tracking how a task cascades through the MAS.
• Maintaining an up-to-date agent registry.
• Deploying specialized agents to enforce rule boundaries and detect misbehaviors of other agents.
• Ensuring clear handshakes and context preservation between agents.
• Enforcing contracts and resolving disputes among competing agent goals.
• Establishing escalation protocols so agents know when to pause, abort or escalate a task to a human mediator.
• Implementing human-in-the-loop guardrails to correct or override MAS decisions.

Organizations also need to ensure that agents are not operating within a black box. This means gaining and maintaining clarity on which processes should involve multiple agents, how agents should share data and how agents will share context across applications. It is equally important to determine early which agents will be authorized to make business decisions, who owns each agent and its outcomes, and which agents will communicate with external systems.

Enterprise leaders also need to plan how conflicts between agent recommendations will be resolved and how exceptions will be handled. Furthermore, it is critical to clarify when human intervention or approval is required for different activities and how responsibilities will be divided between humans and AI agents.

When all these crucial building blocks are in place, organizations can minimize the risk of creating fragmented collections of AI agents. And in doing so, they can maximize the likelihood that the MAS will produce consistent, high-quality outcomes and operate safely within ethical and legal boundaries. Ultimately, a well-governed, accountable MAS can reduce operational complexity and help the business achieve its stated goals.

Rahul Awati is a PMP-certified project manager with IT infrastructure experience spanning storage, compute and enterprise networking.