Non-Functional Market Requirements
The cross-cutting requirements that any solution must meet to be viable at enterprise scale—from sub-second discovery and air-gapped distribution to 5,000+ interface support and multi-protocol visibility. These aren't aspirational; they're the minimum bar set by real enterprise environments.
Performance
When integration latency becomes unpredictable, downstream services fail and user trust erodes. AI agents are especially sensitive — long tool lists degrade model accuracy, and...
3 requirements
View DetailsReliability
Integration failures cascade. If teams can't tell whether a failure is upstream, internal, or configuration-related, mean time to recovery climbs and incidents repeat. Graceful degradation...
3 requirements
View DetailsSecurity
MCP servers, AI agents, and shadow gateways introduce attack surfaces that traditional security reviews weren't designed to assess. Without auditability of network calls, identity propagation...
6 requirements
View DetailsCompliance
Regulatory auditors need evidence, not assurance. If governance reviews aren't tracked, streaming connections bypass proxy infrastructure, and deployment gates don't enforce compliance status, organizations are...
4 requirements
View DetailsAuditability & Traceability
When something goes wrong — a cost spike, a security incident, a governance violation — the first question is 'who did what, when, and under...
5 requirements
View DetailsInteroperability
Enterprises don't get to choose one tool. They have Apigee and Kong, Backstage and internal portals, Entra and Okta, REST and Kafka. Any solution that...
5 requirements
View DetailsDistributability
Banks can't use public Git. Regulated industries run air-gapped environments. If governance rules, policies, and tooling can't be distributed through whatever mechanism a restricted enterprise...
4 requirements
View DetailsUsability
Small teams operating at enterprise scale don't have time for standalone tools, heavy IDE plugins, or keyword-based discovery that doesn't match how they think. If...
4 requirements
View DetailsMaintainability & Freshness
Stale catalogs, outdated metadata, and unversioned rules undermine everything built on top of them — SDKs, MCP servers, AI agent behavior, governance enforcement. If data...
4 requirements
View DetailsDiscoverability
Teams rebuild what they can't find. If discovery requires knowing the right keyword, navigating fragmented documentation, or manually checking catalogs that don't include third-party APIs,...
5 requirements
View DetailsCost Transparency
AI costs are multi-dimensional — tokens, model calls, upstream API fees, caching — and no single vendor provides cross-platform visibility. Without budget controls, cost attribution,...
5 requirements
View DetailsObservability
You can't govern what you can't see. Without visibility into MCP and API usage patterns — what's being called, by whom, how often, and at...
4 requirements
View DetailsPortability & Standards Alignment
Vendor lock-in is the silent tax on every integration decision. If configuration isn't declarative, version-controlled, and built on open standards like OpenAPI, AsyncAPI, and JSON...
4 requirements
View DetailsBusiness Alignment
APIs that can't be mapped to products and business capabilities are invisible to leadership. Without business context, reuse decisions are based on technical similarity instead...
4 requirements
View DetailsData Consistency
Inconsistent schemas across APIs create integration friction for humans and make AI agents unreliable. If core business entities don't have single-source-of-truth definitions and schema reuse...
4 requirements
View DetailsDeveloper-Native Experience
Developers don't log into governance tools. If security notifications, compliance feedback, and governance guidance aren't delivered where developers already work — Slack, Teams, PR comments,...
4 requirements
View DetailsAI-First Documentation Design
AI agents consume documentation differently than humans. If developer docs aren't available as markdown endpoints, explicit about required context, and supplemented with task-oriented guidance, MCP...
4 requirements
View DetailsContext Efficiency
Every unnecessary field in a tool schema, every irrelevant option exposed to an agent, wastes context window tokens and degrades model performance. Right-sizing context —...
4 requirements
View DetailsAgent Evaluation Precision
Checking whether an AI agent's final output 'looks right' is not evaluation. Without verification of actual API calls made, parameter correctness, call ordering, and upstream...
4 requirements
View DetailsAttestation & Audit Trail for AI-Generated Code
When AI generates code, nobody knows which security rules it followed, whether dependencies are license-compliant, or what version of governance policies applied. Without SBOM-like attestation...
4 requirements
View DetailsRisk Appetite Customization
A fintech startup and a bank have different risk tolerances. A prototype and a production service need different rule strictness. If governance rules can't be...
4 requirements
View DetailsExtreme Scale Support
Most integration platforms are designed for 100–500 interfaces. Organizations operating at 5,000+ interfaces across 7,500+ applications — effectively '10 companies worth of work' — break...
4 requirements
View DetailsMulti-Protocol Landscape Visibility
APIs are often a 'small fraction' of the integration landscape. SAP, iPaaS, Kafka, Solace, EDI, GraphQL, and custom protocols all carry business-critical data. Any visibility...
4 requirements
View DetailsTechnology Lifecycle Intelligence
Getting into a technology is easy; getting out is expensive. Without signals for when technologies are declining — vendor pricing shifts, community activity drops, support...
4 requirements
View DetailsConsumer Traceability Post-Handoff
After an API is handed off to consuming teams, it gets 'used all over the place' — and nobody can trace who's calling what through...
4 requirements
View Details