The agent economy is the emerging market in which AI agents transact directly with businesses and with each other: discovering services, negotiating terms, placing orders and paying for outcomes, with contracts and reputations attached.
No jargon, no math
Today most agents work inside one company. The agent economy starts when agents cross company walls. The mechanism has four layers. Discovery: agents find each other through directories and open protocols, the way websites are found through search. Negotiation: they exchange structured offers, price, quantity, deadline, and settle terms within limits you set. Execution: orders flow through APIs, and payment rails built for machines settle the transaction. Reputation: every completed deal feeds a track record that the next counterparty checks before trusting. What is missing still matters: liability when an agent commits you to a bad deal, payment standards, and trust frameworks. The boundary is your mandate: an agent should never commit money or sign terms beyond the envelope you gave it. Everything inside the envelope happens without you; the envelope itself is the human's design.
An AI agent is software that pursues a goal on your behalf: it reads the situation, plans the next step, acts through tools (email, calendars, databases, APIs) and checks the result before continuing, instead of answering one question and stopping.
ReadModel Context Protocol (MCP) is an open standard that lets an AI agent connect to your tools and data (files, databases, CRMs, APIs) through one common plug, instead of a custom integration for every tool.
ReadA multi-agent system is a team of specialised AI agents working on one goal: each agent has its own role and tools, and a coordinator divides the work, collects the results and resolves conflicts, instead of one model trying to do everything alone.
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We design and ship AI systems with permissions, logs and human approval. It starts with a 30-minute scoping call.