- Closed Context Is a Dead End
- Open Context Travels With You
- Data Sovereignty In Contextual AI
- Importance of Contextual Data In the Protocol Layer
- What is the Open Context Layer (OCL)?
- Why Is Data Portability A Killer Move In Agentic Web?
- How To Overcome Data Siloes With Contextual Data?
- Context At The Core Of Digital Identity Management in Agentic Web Ecosystem
- Where Is The Context Leading Us To?
- Frequently Asked Questions
Open vs. Closed Contexts: Building an Open Context Layer for the Internet
By Alev • July 31, 2025
Every time you open a new chat in ChatGPT or interact with any other AI agent, it feels like you are introducing yourself all over again. The system greets you with no memory of your past interactions, no sense of your preferences, and no understanding of the journey that brought you there. It is as if the slate is wiped clean every single time.
This exhausting cycle of repetitive self-introduction is more than a user’s frustration. It’s a direct extension of the cold start problem. When no shared context carries over, every interaction begins at zero. The system knows nothing of past conversations, preferences, or goals, leaving users to restate their needs and businesses unable to deliver meaningful personalization.
Instead of building momentum, the experience stalls at the starting line, turning what could be intelligent, adaptive engagement into a tedious reset with every touchpoint. What we need is not just more advanced algorithms but a shared memory. A way for our context, including our history, intent, and identity, to travel with us across the internet.
Closed Context Is a Dead End
Closed contexts live inside walled gardens. Each application layer or LLM instance either operates without long-term memory, relying only on a short-lived context window, or depends on a centralized external memory. In both cases, the result is fragmented and siloed knowledge that limits continuity and personalization. That means every new session starts fresh, and every insight gets lost the moment you switch platforms. These systems limit continuity, personalization, and real-time adaptability.
For example, you start a chat on ChatGPT and tell it what you are going through. But when you come back for counselling, after a break, the context disappears, and it asks you, “I don’t have the context from our earlier conversation. Can you share a bit about what you’re going through so I can support you in the best way possible?” And you can manage your memory by only deleting a few hints and preferences that it has saved. But export or edit? No way!
Is it a nightmare for developers only? Absolutely not!
Users and developers are both unhappy with closed contexts. For developers, it is like creating redundancies and limiting multi-agent collaboration. For users, it feels like talking to someone who forgets your name every five minutes. It breaks trust.
Open Context Travels With You
Open context means your personal preferences, history, goals, and metadata aren’t locked inside a single application layer or model. Instead, they move with you, across services, agents, and platforms. This includes behavioral signals, chat history, selected tools, memory graphs, emotional tone, and more.
Plurality Network has created the open context layer as a portable memory substrate, built at the protocol layer of the agentic web. This context layer works on your terms; it is permissioned, encrypted, and future-proof. The experiences not only get personalized but also remain personal.
Data Sovereignty In Contextual AI
Data sovereignty means you, not platforms, own and control your data. This principle becomes critical in Contextual AI systems, where agents learn and adapt based on your data signals. Without sovereignty, contextual data becomes a liability, vulnerable to misuse or loss.
Open context flips the model. With encrypted, permissioned data control, your context becomes an asset you command. You decide who gets access, how it’s used, when it expires, and the ability to monetize it. This is essential not just for trust, but for scale, especially in decentralized ecosystems where collaboration is key.
Importance of Contextual Data In the Protocol Layer
Contextual information isn’t just useful; rather, it’s foundational. At the protocol layer, it enables meaningful communication between apps, agents, and networks. Without contextual interoperability, AI systems remain isolated and ineffective.
Embedding contextual intelligence at the infrastructure layer means every agent or app can access shared, permissioned context. That’s how we scale intelligence without fragmenting the experience.
Key Benefits of Contextual Data at the Protocol Level:
- Supports multi-agent collaboration across apps and chains
- Enables persistent personalization with privacy by design
- Allows shared memory and cross-platform continuity
- Powers real-time adaptation based on evolving user needs
- Prevents repetition and redundant data capture
What is the Open Context Layer (OCL)?
The Open Context Layer (OCL) is a decentralized memory protocol that lets users carry their preferences, goals, history, and behavior across apps, agents, and chains. It sits at the infrastructure layer of the Agentic Web and decouples memory from any single model or interface. By design, it is permissioned, portable, and encrypted, turning ephemeral context into something structured and user-controlled.
With the OCL, developers can access a composable, agent-readable layer of contextual data through APIs and SDKs. It does not store raw data but instead captures structured signals like recent tool use, emotional tone, past actions, and preference patterns. These signals are defined and shared on the user’s terms. Apps and agents can read from or write to this layer only with explicit permission.
OCL makes context durable and scalable. It ensures your AI agents do more than just respond. They remember, learn over time, and maintain continuity across touchpoints. Whether you are interacting with a co-pilot, a chatbot, or a decentralized service, OCL keeps your identity and memory intact. This unlocks truly personalized experiences in the Agentic Web.
For more detailed information, visit docs.plurality.network.
Why Is Data Portability A Killer Move In Agentic Web?
In the Agentic Web, portability isn’t a nice-to-have; it’s the foundation for continuity. Without it, agents cannot coordinate, users are stuck in loops, and intelligence remains static. Data portability enables a dynamic context that evolves with you.
a. Interoperability Between Agents
Portability enables agents to access personalized context backpacks, each tailored to the user’s specific needs within a given domain. For example, a health advisor and fitness trainer could draw from the same health-focused backpack, ensuring seamless continuity without redundancy. This way, every agent interaction builds on the user’s own context rather than starting from scratch.
b. Real-Time Personalization
Data portability allows agents to adapt in real time, offering better recommendations, fewer mistakes, and stronger alignment across workflows.
c. Future-Proofing Your Digital Self
Portability makes your context resilient. If a tool shuts down, your memory stays intact. If you switch chains or protocols, your identity remains coherent.
How To Overcome Data Siloes With Contextual Data?
Developers working with AI agents often hit the wall of data silos, context trapped within one model, app, or cloud instance. This leads to repetitive prompts, inconsistent behavior, and wasted compute.
Open context layer solves this by creating a shared, user-permissioned context layer at the network layer. Developers can build applications that plug into this layer, ensuring seamless memory flow across tools. No more rebuilding the same context stack over and over.
Context At The Core Of Digital Identity Management in Agentic Web Ecosystem
In the Agentic Web, identity isn’t just about your wallet or login. It’s a composite of your preferences, history, tone, and behavior. That’s what makes digital identity management more nuanced and more valuable.
By linking identity to open context, we create persistent, evolving user profiles that agents can reference securely. This contextual identity enhances personalization, improves agent alignment, and allows users to remain coherent across platforms and protocols.
The agent’s web is now all about contextual memory. If the context thrives, the users feel connected, and ultimately, it boosts retention. Any identity management system without context is now just a social venture running in the dark.
Where Is The Context Leading Us To?
The future of AI is contextual. And the future of context is open. In a decentralized internet, we can’t afford to lose our memory at every app border. The Open Context Layer gives us a way forward, a way to preserve our preferences, carry our signals, and stay sovereign across the Agentic Web.
If you’re building agents, LLM apps, or decentralized identity systems, now’s the time to rethink how you manage context, not as a feature, but as infrastructure.
“With Open Context Layer, your AI doesn’t just get smarter, it finally remembers who you are.”
Frequently Asked Questions
What is the Open Context Layer?
The Open Context Layer is an infrastructure protocol that enables users to carry their contextual preferences, memory, and goals across agents and platforms securely and permissioned.
Why is context important for AI?
Without context, AI agents must relearn everything each time. Context allows for personalization, continuity, and efficiency, especially in multi-agent or cross-app environments.
What are data silos on the internet?
Data silos occur when contextual information is locked within specific tools or models. This prevents agents from collaborating or offering consistent support.
How does Open Context support digital identity management?
Open Context links dynamic, evolving context with identity, allowing users to maintain continuity across apps while keeping full control over what’s shared.
What makes data portability essential in the Agentic Web?
Portability ensures your context can move freely and securely across ecosystems. It enables better agent cooperation, personalization, and future-proofing.
Is Open Context compatible with existing AI systems?
Yes. Open Context can be integrated at the application and network layer, allowing existing AI agents and apps to access contextual data through secure APIs and SDKs.
