Multi-Agent AI Is Coming Fast. Here’s How to Prepare

Within enterprises,AI agents have moved from prototype to production - answering customer queries, generating code, and tracking inventory, among other tasks.

But single agents are just the beginning. The next phase will see the orchestration of multiple agents to perform complex, automated tasks both inside companies and across organizational boundaries with customers and partners. That shift creates new governance challenges around visibility, coordination, accountability, and trust.

Legal battles over the effects even single agents can have on a business or business model are already brewing, because when agents start acting on behalf of customers, companies lose the ability to monetize those relationships. In November, for example, Amazon sued Perplexity over its shopping agent, alleging the startup covertly accessed customer accounts and disguised AI activity as human browsing. But single agents are just the first wave. Multi-agent systems, in which specialized agents with different objectives interact with each other, raise far more complex governance challenges. The Amazon lawsuit is an early signal of the coming battles over customer relationships, platform control, and the rules governing agent-to-agent commerce.

When multiple agents interact - when a procurement agent for one company negotiates with a sales agent for another, say, or an agent for a logistics provider coordinates deliveries with multiple vendors - it's easy to see how conflicts can escalate. If one agent misrepresents pricing data during negotiations, the resulting agreement can't be trusted. Or if a delivery coordination between two agents exposes proprietary shipping patterns with competitors, who is liable?

The payoff for successfully navigating the shift to multi-agent systems, however, will be enormous. Multi-agent systems will fundamentally restructure how enterprises operate, enabling new levels of coordination across departments and across different businesses that humans would find challenging or impossible. Enterprises that invest now in elements like robust governance frameworks, data harmonization, and orchestration capabilities will be positioned to lead. Those that delay risk operating under rules and infrastructure established by first movers, and they may find themselves relegated to commodity status in ecosystems they no longer control.

Slowly and Then All at Once

Companies are currently deploying agents with ambitious goals, but most implementations remain focused on specific use cases. Right now, the demand for multi-agent architecture may not seem pressing to companies that are still rolling out an employee help desk agent.

Even companies with single agent deployments can get mired in what The Verge's Nilay Patel calls the "DoorDash problem." When AI agents insert themselves between businesses and customers, companies risk losing the ability to monetize through ads, loyalty programs, upsells, and partnerships. They become commodity providers competing on price alone. But single-agent disruption is just the opening act. When agents start negotiating with other agents on behalf of competing or even partner organizations, the governance challenges become far more acute.

Because AI models are trained to be accommodating, two agents interacting can fall into a feedback loop of endless agreement that undermines the objectives of their owners.

These technological inflection points follow a long-established pattern, but each cycle moves faster than the last. Mobile was a curiosity until it became so central that business models needed to be rethought. Cloud computing proved no different; resisters who insisted on-premise forever were suddenly scrambling to catch up. Multi-agent systems are still emerging, but the transition to widespread adoption could compress the timeline further. The window to prepare deliberately rather than react desperately is likely shorter than most executives assume.

The Trust Bubble

Before enterprises can prepare, they need to understand what they're actually preparing for. Developers initially assumed a single, sufficiently capable agent could handle everything within an organization. But in practice, performance degrades when one system tries to juggle too much specialized knowledge and too many competing priorities. Multi-agent systems solve this through specialization. A marketing agent negotiates resource allocation with sales and product development agents or a finance agent evaluates proposals from the operations agent. Or agents represent different organizations entirely.

Data infrastructure work is unglamorous, but it's foundational.

When organizations deploy multi-agent systems with potentially competing objectives, they'll hit a fundamental problem: the "trust bubble." An organization may assume agents will remain aligned with its objectives and that interactions between agents will be straightforward. But in reality, competing parties haggle over prices, dispute contract clauses, and balance short-term gains against long-term relationships. How does one agent verify another agent's claims? How do agents exercise the judgment and intuition that human negotiators bring to complex negotiations? How do you establish credentials when agents negotiate without human oversight? How will an agent know when to hold firm in a negotiation and when to compromise - without getting exploited in the process?

Researchers have already identified unexpected problems, like "echoing." Because AI models are trained to be accommodating, two agents interacting can fall into a feedback loop of endless agreement that undermines the objectives of their owners. One case study from Salesforce AI Research examined what happens when a customer's agent seeks to return ill-fitting shoes to a retailer's service agent. The exchange ended with both agents agreeing that the customer would keep the shoes, pay a 25% restocking fee, and consider buying a second pair out of appreciation for the retailer's "fair policies." When agents represent opposing interests, ensuring they remain loyal to the organizations they serve cannot be assumed - it must be engineered into the system.

That infrastructure barely exists today. How do you ensure agents remain loyal to their owners when they're also trained to be cooperative? How do you balance the need for agents to reach agreements with the imperative to advocate forcefully? How do you verify that an agent didn't abandon its owner's interests mid-negotiation? AI developers are still working through these fundamental questions about verification and accountability when agents represent competing interests.

How to Prepare

While technologists build out this infrastructure, enterprises can't afford to wait. The transition to multi-agent systems requires deliberate preparation across three critical dimensions.

Integration foundation: In a multi-vendor ecosystem, integration is no longer just about moving data; it is the fundamental "connective tissue" of the Agentic Enterprise. Successful multi-agent systems require an API-driven architecture that allows agents to fluidly access and take action across a sprawling landscape of nearly 900 different enterprise applications. Without this seamless connectivity, agents remain trapped in silos, unable to share context or collaborate on end-to-end complex tasks.

Governance frameworks: While researchers work on the technical challenges of agent loyalty and verification, enterprises must establish their own governance frameworks that define boundaries and accountability before agents begin making autonomous decisions. Which decisions require human approval and which can proceed autonomously? What can agents negotiate on the organization's behalf and what remains off-limits? Equally critical: Which external agents will your systems trust? How will you audit agent-to-agent transactions? What credentials and validation processes will you require? Building these frameworks now while stakes are relatively low is easier than retrofitting them later when agent-to-agent commerce is routine.

Orchestration. Multi-agent orchestration means coordinating how specialized agents interact and hand off tasks to achieve business goals.It means optimizing across the enterprise rather than within departments. Organizing around the stages of the customer journey created vertical silos with separate budgets, tech stacks, and incentives. Multi-agent systems enable horizontal optimization, making trade-offs across functions to achieve business outcomes rather than departmental metrics. This is the real transformation: not just efficiency but systemic coordination.

Data harmonization. Years of vertical optimization produced isolated functions, incompatible systems, and customer data that doesn't align. The same customer may appear as a "premium member" in service records, "Account #47382" in billing, and an email address in marketing - three disconnected identities that agents can't reconcile without harmonized data. Multi-agent coordination can't function without shared understanding across domains. Data infrastructure work is unglamorous, but it's foundational.

The Window Is Open Now

The technical challenges around multi-agent systems remain partially unsolved, but enterprises that invest now in working through these problems - building governance frameworks, solving for data harmonization, developing orchestration capabilities - will write the rules for agent-to-agent commerce. The window for deliberate preparation is open now. Done right, multi-agent systems will enable enterprises to collaborate and integrate with partners, suppliers, and customers in ways that today's manual processes and rigid APIs never could. The organizations building these capabilities now are creating the foundation for that future.

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