Teams are independently adopting 3rd-party MCP servers without a governed approach to discovery, onboarding, and authentication.

Security teams must evaluate and approve MCP server usage within developer IDEs before enterprise-wide adoption can proceed.

HTTP streaming and SSE connections required by MCP and AI services conflict with existing corporate security policies and infrastructure.

Identity and authorization tokens must be properly propagated when AI agents call other agents or services in multi-hop scenarios.

Organizations need to understand and control the total cost of ownership across AI models, MCP servers, and third-party services.

Organizations need to understand how MCP fits into existing API infrastructure and governance tooling they have invested in over the past decade.

Governance rules need to be available directly in developers' coding assistants and AI agents, not just in standalone tools and pipelines.

Enterprise security restrictions prevent using standard distribution mechanisms to deliver governance rules to all API designers.

Morgan needs to track and report on API governance reviews across the portfolio.

Morgan needs teams to obtain API tokens and keys from an internal gateway rather than directly from 3rd-party providers.

Morgan needs to ensure AI coding assistants follow security policies when generating code, with attestation of compliance.

Developers perceive governance as friction and avoid it unless compliance is the easiest path.

API governance must be embedded seamlessly into the developer workflow so that the compliant way of building APIs is also the easiest and most secure way.

Organizations can enforce coding standards and CI gates but cannot enforce equivalent governance for the Markdown files that shape AI agent behavior.

Repositories need to explicitly declare what AI agents are allowed to change and what is off-limits.

Beyond MCP, protocols like A2A, ACP, AP2, and x402 are proliferating — enterprises need unified governance across all agent communication protocols, not just one.

Agentic browsers and AI workspaces will autonomously discover, evaluate, and transact via APIs — organizations need APIs that are not just discoverable but transactable by agents with proper governance guardrails.

Enterprises need an internal registry of approved MCP servers that surfaces inside the developer IDE, acting as an allow-list so developers discover only vetted servers.

The MCP discovery registry (which servers are approved) and the package registry (where the binary actually lives) are two different systems with different governance requirements.

Enterprise contracts only cover generally-available features, so AI tooling rollouts at scale must wait for GA even when developers are already asking for preview capabilities.

Iris sees the patient's data sit in adjacent organizations that legally cannot share it — region vs municipality, primary vs secondary purpose, country vs country — even though all of them care for the same person. She needs a patient-centered data fabric that the law can actually permit.

Iris waits months to years for ethical approvals and data extractions before she can touch real patient data. She needs a synthetic-data pipeline that's data-driven, schema-conformant, and privacy-preserving — so research can move while the legal track runs in parallel.

Iris has watched well-designed healthcare data standards stall for years until regulation made them mandatory — FHIR via 21st Century Cures, openEHR via the European Health Data Space. She needs the regulation-to-implementation path to be cheap, demonstrable, and reusable.

Iris's project pulls in a vendor, a consultancy, a healthcare provider, and a university — each cares about a different slice of the same patient. She needs an interoperability fabric that lets every stakeholder see only what they should and contribute only what they own.