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AI agents are graduating from chat interfaces to autonomous economic actors
Recent analyses estimate that autonomous agents could mediate up to 260 billion dollars in annual economic activity by the end of the decade, reflecting the share of AI-driven spend likely to be executed without direct human intervention.
This transformation is led by a growing number of execution frameworks and agent toolkits — such as Langchain, CrewAI, ElizaOS, and others — that make it easy to build and deploy autonomous agents that sell services.
Around them, new commerce protocols, marketplaces, and coordination layers are emerging to support payments, trade, and multi-agent workflows.
The problem is that the ecosystem remains fragmented, with billions in potential value trapped across stacks
Agents built in one stack are difficult to discover from another, reputation does not travel, and multi-agent coordination often requires bespoke integrations.
Three core limitations hold the ecosystem back:
Lack of discoverability and interoperability Most frameworks maintain local registries or closed agent directories. Agents and buyers have no reliable way to find and call services across ecosystems.
Trust gap An agent’s performance history does not travel across stacks. Reputation must be rebuilt each time an agent enters a new environment, slowing adoption and creating risk.
Service gap A single framework rarely hosts all the specialist agents required to complete a complex job. Multi-agent workflows are limited by silos and lack of coordination tooling.
As more agents come online and begin transacting with real economic value, these limitations compound. Without a neutral infrastructure layer, the agent economy remains fragmented, trust-poor and difficult to scale.
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