The agentic web envisions a structured global network designed for machine-to-machine interactions rather than human browsing. It builds on the semantic web and aims at enabling autonomous AI agents to act as well as reason.
How does the agentic web work?
The agentic web – as currently envisioned – is organised around workflows, APIs, protocols, large language models (LLMs) and frameworks such as knowledge graphs that enable AI agents to operate autonomously. Agents follow structured processes for decision-making and executing tasks.
In an agentic future, these processes would likely be governed by ontologies (high level frameworks) that are the semantic backbone for interoperability and explainable reasoning. Among other things, ontologies can be used to impose rules and constraints on what AI agents decide, do or use.
AI agents need to collaborate among themselves for the agentic web to scale, suggesting that web standards would be used to enhance interoperability. Standards could also provide the necessary traceability (digital footprint) and structure for agent transactions and communication.
Is the agentic web different to the semantic web?
The semantic web‘s main function is enabling intelligent systems to reason deductively. This means that an AI can arrive at a logical conclusion by itself, based on general principles or known facts. The semantic web is highly structured for this job, enabling machines to process, query and interpret knowledge more efficiently.
The agentic web’s main difference is that it adds an execution layer to the semantic web, with a focus on operational workflows, agent autonomy and multi-agent collaboration. In theory this should enable AI agents to act as well as reason. The agentic model also integrates LLMs which weren’t practical a few years ago.
A 2025 paper sets out the technological foundations that support the shift from human browsing to machine-to-machine interaction.
Is the agentic web happening any time soon?
The semantic web focuses on consistency and reusability. This allows it to expand more efficiently by linking up with new systems using common standards. However, the semantic web’s complexity has often slowed its adoption, a drawback which could also apply to its agentic offspring.
There is no universal protocol for agent-to-agent discovery, negotiation and trust in the way that HTTP worked for the original web. Scaling reasoning, and getting domain experts to build consensus around common ontologies, are enormous technical and collaborative challenges. Encoding real-world norms into agent behaviours is also very complex; these can be both legal, ethical and organisational.
On the ground, data is still fragmented across the web. It’s often unstructured or locked in silos, or both. This limits how far and how fast the agentic web can spread, especially when businesses are incentivised to protect their proprietary knowledge.
Trust, safety and security are the other major obstacles. Delegating tasks to AI agents carries multiple risks, such as spam, fraud and hacking. These have costs that are difficult to quantify for a potential investor, especially at scale.
All of these challenges suggest that a global version of the agentic web is still a long way off.
Can vertical AI advance the agentic web?
The commercial case for investing in a global agent-to-agent ecosystem certainly carries enormous risks, due to technical hurdles, regulatory barriers and its inherent fragility as a collaborative model. But a more gradualist approach to the agentic web is also possible, focused on niche markets and applications known as verticals, that are described in a 2025 agentic AI study.
This would suggest that commercial potential lies in sectors where the semantic web is already firmly embedded, such as bioinformatics and advanced manufacturing. This could provide the base for an agentic ecosystem to expand across adjacent sectors that are also data-heavy.
Ultimately, it all depends on whether the agentic web’s promise of widespread automation can persuade big players to adopt open, interoperable standards and sink large sums of money into a high risk, high return bet on an agentic future.