Tech & FAQ

An AI agent is software that can perceive its environment, make decisions, and take actions autonomously — without step-by-step human instruction. Unlike a chatbot that answers questions, an agent can browse the web, write and run code, place orders, send emails, and interact with other systems. Think of it as a digital employee that works continuously, never sleeps, and can operate across multiple systems simultaneously.

Agent-mediated commerce is economic activity where AI agents act as buyers and sellers — placing orders, negotiating terms, and executing transactions on behalf of humans or businesses. McKinsey estimates this market will reach $3–5 trillion by 2030. Instead of a human logging into a supplier portal, an AI procurement agent places the order automatically based on inventory signals, price rules, and business logic.

Two forces converge in 2026. First, the EU AI Act's high-risk enforcement deadline arrives in August 2026 — autonomous AI in manufacturing is classified as high-risk, requiring audit trails, human oversight, and governance documentation. Companies that haven't deployed compliant infrastructure by then face fines of up to €30M or 6% of global turnover. Second, enterprise procurement cycles run 6–18 months, meaning companies that want compliant agentic AI running before the deadline need to start the procurement process now.

MCP is an open protocol developed by Anthropic that standardizes how AI models connect to external data sources and tools. Think of it as USB-C for AI — a universal connector that lets any AI model work with any tool or data source that speaks the same protocol. Instead of building a custom integration for every AI model and every data source, you build one MCP connector and it works across the ecosystem.

Virtuals ACP (Agent Commerce Protocol) is a standard for AI agent commerce developed by Virtuals Protocol — enabling agents to discover, negotiate, and transact with each other.

Google AP2 (Agent-to-Agent Protocol 2) is Google's proposed standard for agent-to-agent communication and task delegation.

A2A (Agent-to-Agent) is a broader category of protocols enabling direct agent communication.

The problem: each of these protocols is incompatible with the others. A company that implements Virtuals ACP can only trade with ACP-compatible agents. Agentegra's Salesman API abstracts all of them.

Competing protocols are a natural result of rapid innovation — multiple companies are racing to define the standard for how AI agents communicate. This is similar to the early internet when TCP/IP, IPX, and AppleTalk competed before TCP/IP won. The difference is that enterprises can't afford to bet on the wrong protocol. Agentegra's Salesman API lets you implement once and let us handle the protocol translation — so your investment is protected regardless of which standard eventually dominates.

The Salesman API is Agentegra's protocol-agnostic commerce bridge. It's a single API endpoint that normalizes orders from any agent commerce standard (Virtuals ACP, Google AP2, A2A, MCP) and routes them to your fulfillment system. Human buyers and AI agent buyers use the same endpoint — your backend doesn't need to know or care which type of buyer placed the order.

You connect your product or service to the Salesman API once. You define your products, prices, and fulfillment logic in one place. The Salesman API then accepts orders from any agent commerce protocol — current and future. When a new protocol emerges, Agentegra adds support for it. Your implementation doesn't change. Your product automatically becomes available to every agent that speaks any of the supported protocols.

Anyone or anything that can make an HTTP request: human customers via standard web checkout, AI agents using Virtuals ACP, procurement bots using MCP, enterprise systems using REST, and any future protocol Agentegra adds. The Salesman API is commerce-blind — it processes valid orders regardless of the buyer type, logs everything for the audit trail, and routes to your fulfillment system.

Sovereign AI infrastructure means you control where your AI runs, where your data lives, and who has access to it. In practice: your AI models run on your servers (on-premise or your private cloud), your data never leaves your facility, and you're not dependent on any hyperscaler (AWS, Azure, GCP, OpenAI) for availability, pricing, or compliance. For European manufacturers handling production data, IP, and customer information, this is a legal and competitive necessity.

Legal — EU AI Act requires you to know where high-risk AI decisions are made and to maintain full audit trails. Sending production data to a US cloud provider creates GDPR exposure and makes compliance harder.

Competitive — production recipes, process parameters, and supplier relationships are IP. Sending them to a third-party model is a risk.

Operational — cloud-dependent AI goes down when connectivity goes down. A factory that stops because the internet is slow is not acceptable.

Agentegra's platform is compliant by architecture, not retrofitted. Every AI decision generates an audit log entry — what was the input, what was the output, what action was taken. Human oversight is built into the workflow: the Human Switchboard reviews decisions above a configurable risk threshold. Governance documentation is generated automatically. The deployment runs on your infrastructure — there's no third-party to explain to a regulator. We handle August 2026 by design.