{
  "id": 12257395,
  "title": "From Agent Protocols to Semantic Agent Runtimes",
  "url": "https://urgent.news/2026/10/05/from-agent-protocols-to-semantic-agent-runtimes",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-05T23:48:45.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/fullagenticstack/from-agent-protocols-to-semantic-agent-runtimes-479m"
  },
  "original_language": "en",
  "account": "Agentic systems have advanced significantly, enabling tools, communication, sandboxed execution, and trace management. Yet, there remains a gap in providing a unified semantic model to answer the core question: \"What exactly is being executed?\" This article aims to clarify this issue and define the foundational architecture needed to address it.\n\nToday's agentic applications typically involve a user interacting with an LLM agent, which then calls various tools, APIs, databases, and potentially other agents. While modern frameworks incorporate sophisticated elements like tool registries, memory management, planners, retries, workflow engines, tracing, authorization, guardrails, human approval, durable execution, and sandboxed code execution, the industry faces a challenge. These capabilities are often described at different architectural levels, creating a gap in understanding the actual user intention, selected behavior, authorized actor, executed action, state transitions, acceptance criteria, and evidence of results.\n\nOne analogy is the evolution of the Web. HTTP defines how to communicate but does not specify business semantics like authorization, transfer validity, account state changes, ledger transitions, or proof of execution. Similarly, in agentic systems, MCP (Model Context Protocol) and A2A (Agent2Agent Protocol) serve as communication mechanisms, while tracing and policy enforcement provide additional layers of functionality. However, they do not offer a comprehensive semantic model capturing the complete lifecycle of an agent's execution.\n\nConsider a simple request: \"Book me a table tomorrow at 8 PM.\" An MCP-enabled agent may discover tools for searching restaurants, creating reservations, and canceling them. While MCP facilitates communication, it does not define the semantic lifecycle of the intent, decision-making, authorization, action execution, side effects, acceptance, or evidence of the outcome. This distinction becomes even clearer when considering MCP Tasks, which provide durable task state machines for request execution but still lack a semantic definition of the user's intention.\n\nAllasCode is addressing this gap by introducing a layer above MCP and A2A. It differentiates between transport/runtime abstractions (MCP Task) and semantic execution abstractions (AllasCode Execution). MCP Task represents the protocol request being executed, while AllasCode Execution defines the actual semantic behavior being performed for the user's intent, by the responsible actor, under specific policies. A2A, on the other hand, enables interoperability between different agent frameworks, allowing them to discover capabilities, communicate, delegate tasks, and collaborate without exposing internal memory or implementation details.\n\nIn summary, the current agentic architecture effectively solves numerous technical challenges but falls short in providing a unified semantic model. By establishing a distinct layer that captures the complete lifecycle of an agent's execution, from intent to evidence, the industry can move towards more robust and transparent agentic systems.",
  "summary": "A simpler way to understand where agentic architecture is going The current generation of agentic systems is already solving several difficult problems. Agents can call tools through the Model Context Protocol (MCP). They can communicate with other agents through the Agent2Agent Protocol (A2A). They can execute isolated workloads through sandboxes and increasingly portable WebAssembly components.…",
  "key_points": [
    "Agentic systems enable tools, communication, sandboxed execution, trace management.",
    "Current agentic architecture lacks unified semantic model for execution.",
    "AllasCode introduces layer above MCP and A2A to define semantic behavior."
  ],
  "editors_take": "AllasCode's introduction of a semantic execution layer fills a longstanding gap in agentic systems, enabling a unified understanding of an agent's execution lifecycle and paving the way for more robust and transparent systems.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}