Question 1

What is a dual-user product and why does it matter for product roadmaps?

Accepted Answer

A dual-user product is a software application designed to serve two fundamentally different types of users simultaneously: human users who interact through a graphical user interface and AI agents that interact programmatically through APIs, webhooks, or protocol-level integrations like MCP. The distinction matters for product roadmaps because the requirements of these two user types are often in conflict. Human UX design optimizes for discoverability — clear navigation, visual hierarchy, and progressive disclosure. Agent-accessible design optimizes for machine readability — structured outputs, deterministic behavior, narrow API endpoints, and predictable error handling. A roadmap that plans only for human users will systematically ship features that are difficult or impossible for AI agents to consume, ceding the emerging agent-driven workflow market to competitors who build for both. By end of 2026, Gartner projects 40 percent of enterprise applications will embed at least one task-specific AI agent, up from fewer than 5 percent today.

Question 2

How do AI agents actually access and use software products?

Accepted Answer

AI agents access software products through three main surfaces, in order of prevalence: REST APIs with structured JSON responses, webhook-based event streams, and protocol-level integrations including Model Context Protocol (MCP) servers that expose tool definitions an orchestrating LLM can call. Agents do not browse a product's UI the way a human does — they call specific endpoints, parse structured outputs, and chain multiple product actions together as part of a larger workflow. An AI agent automating a sales workflow might call a CRM's contact API, a calendar's scheduling API, and a messaging API in sequence without any human opening a browser. The implication for product teams is that API surface quality — documentation clarity, response structure, rate limit design, and error message specificity — is now a first-class product metric, not an engineering afterthought. Products with poorly documented or inconsistently structured APIs are invisible to the agents that are rapidly becoming a primary distribution surface for enterprise software.

Question 3

What is MCP and why does it matter for building dual-user products?

Accepted Answer

Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024 and adopted widely in 2025, that defines how AI agents discover, authenticate with, and invoke tools in external software products. An MCP server is a thin integration layer that wraps a product's existing API and exposes its capabilities as typed tool definitions that an orchestrating LLM can discover and call. For product teams, publishing an MCP server is the agent-compatibility equivalent of being listed in an app store: it makes your product discoverable to any AI agent that uses an MCP-compatible orchestration layer, including Claude, and many third-party agent frameworks. Products that ship MCP servers in 2026 gain immediate distribution to the growing ecosystem of enterprise AI agent workflows. Products that do not ship MCP servers are invisible to those workflows and must rely on custom integration work by each customer, which in practice means they are not integrated at all. For more on Claude's MCP marketplace, see [Anthropic Claude Skills Marketplace: A New AEO Surface for B2B SaaS](/article/anthropic-claude-skills-marketplace-aeo-impact-2026).

Question 4

Does building for AI agents hurt human user experience?

Accepted Answer

Building for AI agents does not inherently hurt human UX, but it requires deliberate architectural separation that many product teams skip. The mistake that creates conflict is trying to serve both user types from the same surface — forcing API responses to match UI state, or designing UI flows that happen to be API-callable as a side effect. The better approach is to treat the agent surface as a distinct product layer: a clean API and MCP integration that exposes the product's core data and actions in a structured, deterministic way, built independently of (and in parallel with) the human-facing UI. The API layer can be richer, more composable, and more permissive than the UI because agents can handle complexity and ambiguity that human users cannot. The human UI can remain optimized for discoverability and guided flows. The separation is what allows both user types to be served without compromise. Teams that conflate the two surfaces — trying to make one design serve both — end up with a human UI that feels like an API console and an API that inherits the limitations of a UI interaction model.

Question 5

How should product teams prioritize AI agent features on their roadmap?

Accepted Answer

Prioritize agent features using the same framework you would apply to any B2B integration: who is the user, what is the job-to-be-done, what is the effort-to-impact ratio, and what happens competitively if you do not ship it? In 2026, the prioritization calculus has shifted because agent-mediated access to enterprise software is growing faster than human-mediated access in several categories. For any product in project management, CRM, data analytics, communication, or developer tooling, audit your top 20 accounts for the percentage of their workflow that is now being orchestrated by AI agents rather than human clicks. If that percentage is above 10 percent, agent features should be on the roadmap within the next two quarters. If it is approaching 30 percent, agent API surface quality should be treated as a P0 concern. The specific features that matter most are: documented REST endpoints with typed schemas, webhook support for event-driven agent workflows, OAuth 2.0 with scoped agent permissions, an MCP server if you are in the Anthropic ecosystem, and rate limits designed for burst agent workloads rather than human session patterns.