FAQ
Enterprise AI Infrastructure FAQ
Direct answers to enterprise buyer questions about ARD, MCP, data boundaries, observability, and AI-agent ready architecture.
What is Agentic Resource Discovery?
Agentic Resource Discovery is a discovery protocol for agentic resources. It helps an AI client find the right capability for a task before that capability is invoked through MCP, an API, a workflow engine, a skill, or another agent system.
In production, ARD should describe catalogs, registries, resource owners, representative queries, trust metadata, access requirements, and the execution boundary that applies after discovery.
What is MCP in enterprise AI infrastructure?
The Model Context Protocol is an integration protocol that helps AI systems connect to tools and context through defined interfaces. In an ARD-aligned architecture, ARD helps discover and verify the resource, while MCP can provide the controlled execution path.
For enterprise use, MCP still needs surrounding architecture: authentication, authorization, scoped credentials, resource policies, audit logs, rate limits, version control, and operational ownership.
What is an embedded operational AI agent?
An embedded operational AI agent participates inside a real workflow, product surface, dashboard, or service platform rather than sitting beside the business as a detached chat interface.
The agent should understand user intent, business context, session state, allowed actions, and when to hand control to a human operator.
How is this different from an AI wrapper?
An AI wrapper usually adds a model interface over an existing workflow. PITECH focuses on the infrastructure required for production operation: discoverable resources, controlled tool access, data contracts, and observable runtime behavior.
The difference is control. A wrapper may generate text or call a simple tool, while an operational system defines allowed actions, ownership, policy checks, rollback expectations, and logs that operators can inspect.
Does MCP solve security by itself?
No. MCP provides an integration protocol. Enterprise implementations still need authentication, authorization, policy enforcement, scoped credentials, logging, and operational ownership.
Treat MCP as one layer in the integration path, not as a permission model. Sensitive enterprise systems still need least-privilege access, human approval gates, and traces for tool calls and policy decisions.
How do agents access internal APIs safely?
Agents should access internal APIs through scoped interfaces, not broad credentials or unrestricted network access. Each exposed action should have a purpose, owner, input contract, output boundary, representative query, and audit expectation.
A safe implementation usually combines ARD catalogs, data access contracts, MCP or equivalent tool interfaces, policy checks, trust metadata, and runtime logs that show what was requested and why.
How are agent decisions logged and audited?
Agent systems should log tool calls, user intent, policy decisions, approval states, failures, retries, and the business object affected by an action.
Auditability matters because enterprise teams need to reconstruct what happened, identify whether the agent acted within its boundary, and improve the workflow without relying on prompt transcripts alone.
Where does human approval fit?
Human approval belongs at decision points where the agent could affect money, customer records, access permissions, legal commitments, regulated workflows, or irreversible operational state.
The approval path should be explicit: what the agent recommends, what evidence it used, who can approve, what action will execute, and how the decision is recorded.
When should a company start with an architecture review?
Start with a review when agent workflows need access to internal tools, customer data, operational systems, or regulated processes.
A review is also the right first step when teams have prototypes but do not yet have clear boundaries for data access, observability, human approval, ownership, or deployment risk.
What does an architecture review produce?
An architecture review should produce a practical map of resources, catalog strategy, MCP/API boundaries, agent responsibilities, data contracts, trust requirements, observability needs, risk areas, and the first deployable workflow candidate.
The output should help teams decide what can be built now, what needs policy or system preparation, what belongs in an ARD catalog, and what should remain outside agent control.
Which systems are good first candidates?
Good first candidates are workflows with clear users, repeatable decisions, measurable outcomes, known data boundaries, and operators who can review edge cases.
Examples include service workflows, B2B product assistance, commerce guidance, dashboard operations, internal support processes, and API-backed tasks with explicit approval paths.
Who is this not for?
This is not for teams that only want a demo, a prompt chain, or a chatbot wrapper with no deployment context. It is for teams that already care about production workflows, conversion, efficiency, scale, and operational responsibility.
If the system cannot be owned, monitored, reviewed, or improved after launch, it is not ready for embedded operational AI.
Sources and review status
Reviewed by PITECH engineering. Last reviewed: 2026-07-05. PITECH IT SOLUTIONS is a UAE-based enterprise AI product engineering company operating under License No. 999566. Start the conversation on WhatsApp: +971 55 572 6745.