Agentic Web
This paper introduces the concept of the Agentic Web, a transformative vision of the internet where autonomous AI agents, powered by LLMs, act on behalf of users to plan, coordinate, and execute tasks. It proposes a structured framework for understanding this shift, situating it as a successor to the PC and Mobile Web eras. The Agentic Web is defined by a triplet of core dimensions, intelligence, interaction, and economics, and involves fundamental architectural and commercial transitions.
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From static browsing to agentic delegation: The Web transitions from human-led navigation (PC era) and feed-based content discovery (Mobile era) to agent-driven action execution. Here, users delegate intents like “plan a trip” or “summarize recent research,” and agents autonomously orchestrate multi-step workflows across services and platforms.
Three dimensions of the Agentic Web: Intelligence: Agents must support contextual understanding, planning, tool use, and self-monitoring across modalities. Interaction: Agents communicate via semantic protocols (e.g., MCP, A2A), enabling persistent, asynchronous coordination with tools and other agents. Economics: Autonomous agents form new machine-native economies, shifting focus from human attention to agent invocation and task completion.
Algorithmic transitions: Traditional paradigms like keyword search, recommender systems, and single-agent MDPs are replaced by agentic retrieval, goal-driven planning, and multi-agent orchestration. This includes systems like ReAct, WebAgent, and AutoGen, which blend LLM reasoning with external tool invocation, memory, and planning modules.
Protocols and infrastructure: To enable agent-agent and agent-tool communication, the paper details protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent), along with system components such as semantic registries, task routers, and billing ledgers. These redefine APIs as semantically rich, discoverable services.
Applications and use cases: From transactional automation (e.g., booking, purchasing), to deep research and inter-agent collaboration, the Agentic Web supports persistent agent-driven workflows. Early implementations include ChatGPT Agent, Anthropic Computer Use, Opera Neon, and Genspark Super Agent.
Risks and governance: The shift to autonomous agents introduces new safety threats, such as goal drift, context poisoning, and coordinated market manipulation. The paper proposes multi-layered defenses including red teaming (human and automated), agentic guardrails, and secure protocols, while highlighting gaps in evaluation (e.g., lack of robust benchmarks for agent safety).
Intelligence: Agents must support contextual understanding, planning, tool use, and self-monitoring across modalities.
Interaction: Agents communicate via semantic protocols (e.g., MCP, A2A), enabling persistent, asynchronous coordination with tools and other agents.
Economics: Autonomous agents form new machine-native economies, shifting focus from human attention to agent invocation and task completion.