Agentic artificial intelligence—systems capable of perceiving, reasoning in a functional sense, and acting in a goal‑oriented manner over time—has produced a proliferation of technical frameworks: Anthropic's Model Context Protocol (MCP), Google's Agent‑to‑Agent (A2A), and orchestration frameworks such as AutoGen and LangGraph. These implementation frameworks address problems of technical coordination. However, they are insufficient for executives and organizations that need to decide how to structure and scale these capabilities coherently. This paper proposes a bio‑inspired taxonomy—analogous to the biological hierarchy—as an organizational framework of five levels: from the elementary agent (cell) to networks of interoperating agentic ecosystems (society). The analogy has explanatory value for specialization, reuse, orchestration, and continuous improvement. The central claim is that feedback and bounded correction—within thresholds defined by design—are architectural conditions that must be incorporated from the earliest stage of the system, not as advanced features of mature systems. Widespread access to these technologies without a shared conceptual framework leads to fragmentation and mis‑coordination. Multiple actors make local decisions about tools, agents, and automations without a systemic view. The absence of a common language to describe levels, roles, and responsibilities produces fragmented systems that are difficult to govern and limited in their capacity to evolve coherently.
Patricio Cáceres Vásquez (Mon,) studied this question.