Give AI governed access to real business capabilities
MCP-native tool boundaries and versionable CLI Skills turn business capabilities into machine-readable, callable functions — so agents can act through governed interfaces, not hardcoded API glue.
Agents are only as useful as the capabilities they can safely call
An agent that can reason but not act is a demo. The moment it has to build an audience, issue a voucher or send a message, it must call real business capabilities — and how it calls them decides whether the enterprise stays safe. Hardcoded API glue cannot express who may do what, within which limits, or what happens on failure; it turns every integration into bespoke, ungoverned plumbing. This is why the hardest part of agentic AI is not the model — it is the integration and governance around execution.
The industry is standardizing on exactly this layer. Open protocols like MCP (Model Context Protocol) describe a capability's inputs, outputs, constraints and failure behavior in machine-readable form, so agents discover and invoke it safely instead of through brittle, hand-wired calls — and so the same capability stays governable no matter which agent, yours or a vendor's, calls it.
- By 2027, over 50% of enterprise AI agents will rely on standardized frameworks such as MCP or A2A for secure interoperability. — Gartner, via K2view (2025) ↗
- More than 60% of early agentic AI orchestration efforts are forecast to miss performance or cost targets by 2030 — integration, not models, is the gap. — Gartner, via StackOne (2025) ↗
- 40% of enterprise applications will feature task-specific AI agents by end of 2026, up from less than 5%. — Gartner, Aug 2025 ↗
Why this stays unsolved today
Hardcoded integrations can't carry governance
A raw API call has no notion of authority, limits or failure policy. Wire an agent to your systems that way and every integration becomes bespoke, opaque and ungoverned — impossible to reason about as the number of agents and tools grows.
Judgment without safe execution is worthless
Reasoning has no enterprise value if it cannot call real capabilities through stable, governed interfaces. Where teams underestimate that execution layer, agentic projects stall — the integration, not the intelligence, is what fails.
Over 60% of early agentic orchestration implementations are forecast to fall short by 2030, largely on integration and governance. — Gartner, via StackOne (2025)↗Black-box connectors don't compose or version
Glue code can't be assembled into workflows or versioned like software, so every capability is a one-off and every change is a risk. There is no clean way to build, test and roll back what an agent can do.
Lock-in to a single AI ecosystem
Without open standards, capabilities can't be invoked safely by Microsoft Copilot, custom agents or partner systems under one governance model — betting the roadmap on a single vendor's proprietary glue.
MCP-native tools and composable, versionable CLI Skills
Every callable capability is described in machine-readable form — inputs, outputs, constraints, failure behavior and its governance boundary — across four domains: identity and entitlement, economic incentive, content and reach, and service and responsibility. Agents discover and call capabilities through MCP, so misuse is bounded and every call is governable, instead of relying on brittle hardcoded API integrations.
CLI Skills package those capabilities so they can be composed into workflows and versioned like software. Audiences and campaigns are built from live behavioral data via governed tool calls — no external data vendor in the path — and the platform plugs into broader enterprise AI ecosystems, from Microsoft Copilot to custom agents, as a governed execution node rather than a black box.
How it works
The mechanics behind ai frontier: mcp & skills.
MCP-native tool boundaries
Each capability is described in machine-readable form — inputs, outputs, constraints, failure behavior and governance boundary. Agents call it through MCP, which reduces misuse and makes every call governable.
Composable, versionable Skills
CLI Skills package business capabilities so they can be composed into workflows and versioned like software. Audiences and campaigns are built from live behavioral data via governed tool calls, not external data vendors.
Governed execution node
The platform plugs into broader enterprise AI ecosystems — Microsoft Copilot, custom agents — as a governed node. External orchestrators can invoke capabilities, but only within the boundaries MCP and the workflow layer enforce.
What good looks like
Directional outcomes grounded in the mechanism above and independent benchmarks — a target to design toward, not a guaranteed result.
Execution you can govern per call
Every capability carries its own boundary, limits and failure policy, so agents act within constraints rather than around them — and each call is inspectable and auditable instead of buried in glue code.
Composable, versioned capabilities
Business functions become building blocks you assemble into workflows and roll back like software, so you can build, test and evolve what agents can do without a bespoke rewrite each time.
One governance model across ecosystems
The same MCP-described capability stays safe whether your agent or Microsoft Copilot invokes it — aligning you with the open standard analysts expect most enterprise agents to adopt.
By 2027, over 50% of enterprise AI agents will rely on standards like MCP or A2A for interoperability. — Gartner, via K2view (2025)↗Frequently asked
What is MCP and why does it matter to a CIO?
MCP (Model Context Protocol) is how agents discover and use capabilities safely. Instead of hardcoded API integrations, it describes tool boundaries, parameters, constraints and expected outcomes in a machine-readable format — reducing misuse, enabling governance, and letting the platform act as a governed node in a broader AI ecosystem.
Can this connect into Microsoft Copilot or our own agents?
Yes. The platform is API-first and exposes MCP-native capabilities, so it connects into broader AI ecosystems — including Microsoft Copilot and custom agents — as a governed execution node, with tool boundaries enforced on every call.
Do we need an external data vendor to build audiences?
No. Audiences are built internally through CLI-driven, governed tool calls, with each one derived from live behavioral data — no external data vendor in the path.
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