The Agentic Web is upon us!
If you want to learn about the Agentic Web, look no further.
This new report is a banger!
It presents a detailed framework to understand and build the agentic web.
Here is everything you need to know:
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.
It's defined by a triplet of core dimensions (intelligence, interaction, and economics) and involves fundamental architectural and commercial transitions.
From static browsing to agentic delegation
The Agentic 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.
A Cross-Era Comparison
They compare the PC, Mobile, and Agentic Web eras across dimensions like user behavior, technology, commercial models, and attention focus, framing the Agentic Web as a shift to action-driven, agent-mediated interaction and economics.
Web Systems Evolution
The architectural evolution of the Web highlights a shift from static content and manual interaction (PC era), to algorithm-curated feeds (Mobile era), and now to agentic automation where AI agents handle tasks via goal-driven orchestration.
This marks a transition from human operators to agents as outcome-driven executors.
Algorithmic Transitions for the Agentic Web
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 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.
Interaction Process
The example shows how high-level user intents are processed via three core components: the User Client, the Intelligent Agent, and Backend 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.
Implementations of autonomous web agents include ChatGPT Agent, Anthropic Computer Use, Google Project Mariner, and Genspark Super Agent.
Agentic Browsers
The authors list early AI-augmented browsers (Agent-as-Interface) applications, like Opera Neon, Perplexity Comet, and Microsoft NLWeb, highlighting how agents augment browsing via orchestration, summarization, and conversational UIs.
Taxonomy of Agentic Web Challenges
The authors present a taxonomy of open challenges in building the Agentic Web, spanning foundational cognition, learning, coordination, alignment, security, and socio-economic impact.
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).
Paper: arxiv.org/abs/2507.21206
GitHub:
github.com/SafeRL-Lab/age…
--
Want to take the next steps?
Learn everything you need to know about building with AI Agents in my academy: dair-ai.thinkific.com
Share this Scrolly Tale with your friends.
A Scrolly Tale is a new way to read Twitter threads with a more visually immersive experience.
Discover more beautiful Scrolly Tales like this.