How Agentic AI Will Transform Business Models for Service Providers, Resellers, and Distributors

Two decades ago, the rise of APIs quietly rewrote the rules for service providers. What began as a tool for developers to connect disparate systems became the foundation for a new kind of partner value, one that extended far beyond “rack and stack” of hardware or basic installation services. Partners who embraced APIs could integrate solutions, automate processes, and embed themselves deeper in their customers’ operations. Those who didn’t often saw their margins erode and their relevance fade as the value moved up the stack.

Today, agentic AI represents a similar but far more disruptive inflection point. Like an API, it connects, but instead of linking systems, agentic AI connects intent to action. It can plan, reason, and execute without step-by-step human guidance. This shift will not only enable new services; it will redefine the economics of the channel. Just as APIs created a new layer of integration-focused value, agentic AI will create a new layer of autonomous, outcome-focused value. Only this time, the pace of change will be much faster.

Unlike the API revolution, where creating value often required teams of skilled developers, the agentic era will be more accessible. Low-code and no-code tools are emerging that allow partners to design, test, and deploy agents without an army of expensive coders. In practice, this lowers the barrier to entry: success will come not from deep engineering resources but from how quickly partners can apply agents to real business challenges. The differentiator shifts from technical capability to business imagination and execution.

For partners, the stakes are clear: seize this moment, or risk being locked out of the next decade’s most valuable service opportunities.

From assistant to operator: capabilities and market impact

Agentic AI differs fundamentally from today’s chat-based interfaces or ML-driven anomaly detection. Earlier automation and AI/ML systems were reactive. They waited for prompts, triggered alerts, or executed predefined actions. Agentic AI is proactive. A multi-agent system perceives goals, evaluates options, takes action, and learns from the results.

In practical terms, that shift moves AI from the role of assistant to operator. Here are just a few ideas on how an AI agent, or team of agents, might change a customer’s operations:

• A healthcare provider could deploy agents to coordinate patient follow-ups, schedule tests, and order supplies.
• A bank could use agents to monitor compliance, reconcile transactions, and investigate fraud in real time.
• A manufacturer might orchestrate a network of agents to balance production schedules, adjust supply orders, and pre-empt equipment failures.

For the channel, this goes beyond efficiency. Customers will increasingly expect partners to deliver AI-powered outcomes, not just deploy technology. That means the traditional “project and support” cycle will evolve toward ongoing, autonomous value delivery, a shift which could disrupt existing contracts, pricing models, and service definitions.

Business model transformation and marketplaces

This shift enables a range of new business models. One of the most promising is “Agent-as-a-Service,” where providers package and deliver prebuilt, domain-specific agents such as an IT-ops troubleshooter, logistics optimizers, or customer onboarding assistants. Instead of charging for hours or licenses, partners can charge for the value the agent produces, whether measured in problems resolved, sales processed, or downtime avoided.

A major enabler of this model will be Agentic “App Stores”. These curated marketplaces allow ISVs, distributors, and resellers to publish certified agents. Similar to today’s SaaS marketplaces but focused on autonomous agents, these platforms will allow customers to browse, deploy, and integrate agents across vendors. Emerging standards such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication protocols will make cross-platform compatibility practical, while certification programs can assure security and governance compliance.

The commercial potential is significant. Partners who can bridge the gap between vendor-created agents and customer-specific needs through integration, customization, and orchestration will have a competitive advantage in this new economy. Distributors in particular may evolve into gatekeepers, curating agent marketplaces, handling billing, and offering trust and validation services across the ecosystem.

The new service provider stack: today vs. future

One way to view this transformation as a four-layer value stack:

• Infrastructure
• Today: Cloud hosting, managed compute, networking, and storage.
• Future: Agent-optimized fabrics, designed to adapt to shifting demand autonomously.
• API / Integration
• Today: Middleware, ETL pipelines, point-to-point connectors.
• Future: MCP or A2A-enabled orchestration, allowing agents to invoke tools, exchange context, and act across systems.
• Agentic AI Platforms
• Today: Task-specific “copilots” embedded in applications.
• Future: Autonomous vertical agents that operate across applications, making proactive decisions and adapting to changing goals.
• Outcome-Based Services
• Today: Project-based fees, SLAs tied to uptime or delivery milestones.
• Future: Contracts tied to measurable outcomes such as cost savings, customer retention, or productivity gains, often with usage or performance-based pricing.

Each layer presents opportunities. ISVs can publish vertical agents, distributors can run integration hubs and marketplaces, and resellers and MSPs can bundle agents with governance frameworks and managed services. The higher up the stack, the more differentiated (and margin-rich) the offering.

When the stack compresses, the value chain changes

For manufacturers, stack compression doesn’t just reshape the partner landscape, it rewires the entire route to market. Traditionally, manufacturers delivered products to distributors, who passed them through channels or MSP to customers. Each tier plays a distinct role: product creation, distribution logistics, service delivery, customer relationship. Agentic AI blurs those boundaries.

With AI, products are no longer static. They can become platforms enriched by a constantly evolving library of autonomous agents. Distributors may take on orchestration roles, running MCP-enabled hubs that certify, connect, and manage agents across multiple vendors, including the manufacturer’s own. In some cases, a distributor might even publish its own specialized agents back into the manufacturer’s marketplace, effectively stepping into the ISV role. The channel and MSP layer shifts, too. With many operational tasks now handled autonomously, these partners move higher up the value chain, designing outcome packages rather than performing repetitive configuration or support. A network services MSP might evolve into a business outcomes partner, bundling a manufacturer’s hardware, software, and AI with their own customer onboarding agents, billing by the number of successful deployments or the speed of new site activation.

Even the customer relationship becomes less linear. Where the manufacturer’s engagement once ended at the point of sale or renewal, agentic platforms create an always-on link between product and end-user environment. Autonomous agents can surface new features, detect opportunities for optimization, and even suggest add-on services, sometimes directly to the customer. Partners still deliver the holistic solution, but manufacturers now maintain a persistent, data-driven presence in the customer’s operations that extends far beyond current “phone home” solutions.

The result is a value chain that is more networked than linear, with overlapping roles and shared ownership of customer experience. For manufacturers, the strategic imperative is to design for openness, equip partners to work across layers, and build ecosystems where every participant can both consume and contribute value. In a compressed stack, success belongs to those who can flex across roles without losing trust.

Challenges and enablers

No transformation of this scale is without obstacles. The skills gap many partners face is acute, spanning governance, security, orchestration, and even ethics. Integration complexity can be daunting, especially in enterprises with fragmented data architectures. Data governance remains critical, since agents cannot be allowed to act on incomplete, biased, or unauthorized information.

Change management may be the hardest challenge of all. Agentic AI shifts roles and responsibilities across teams. It requires not only technical deployment but also organizational redesign, with clear policies for when AI can act autonomously and when humans must remain in the loop.

Early adopters are mitigating these risks by running contained pilots, often in low-stakes operational areas, before expanding to mission-critical workflows. They’re also leaning on interoperability standards like MCP to avoid lock-in and investing in certification programs to ensure agent trustworthiness.

The strategic imperative: roadmap for service providers

For partner leadership teams, the question is no longer whether agentic AI will reshape the market: it is how quickly. Competitive advantage will go to those who stake out their position in the agentic value chain early, before the ecosystem hardens around new leaders.

The most effective near-term steps are pragmatic: building or expanding an AI practice, launching pilot programs with a handful of high-impact agents in areas where results can be measured, while simultaneously building fluency in interoperability standards like MCP and A2A. At the same time, partners could begin positioning data readiness as a service, helping customers prepare clean, governed data pipelines that agents can trust.

Longer-term plays require a broader vision. Partners will need to secure a role in emerging agent marketplaces, whether as publishers of their own vertical agents, as integrators, or as curators providing trust and governance. They will also need to experiment with outcome-based contracting models, supported by analytics that prove value delivered rather than hours billed. And as agent ecosystems grow more complex, forging partnerships with ISVs and hyperscalers will become critical to ensuring ongoing access to the most advanced agents and orchestration protocols.

The business case for embracing agentic AI is strong. McKinsey estimates that autonomous AI could deliver $4.4 trillion in global annual productivity gains. In the channel, partners who can capture even a small fraction of that value and tie their revenue directly to customer outcomes will likely command premium pricing and long-term loyalty.

The next era of the channel will not be defined by who can provision the fastest, but by who can orchestrate the smartest. In the API era, integration mastery separated leaders from laggards. In the agentic era, it will be the ability to combine autonomy, interoperability, and measurable outcomes into a single, trusted offering. For service providers, resellers, and distributors, the moment to act is now: before someone else’s agent does it for you.