JTk3.c0m

🔮 The absolute state of things...

          
state: () => ({
  sessionId: `23342219-4c1b-8f3d-2e5f6a7b8c9d`, 
  userIntent: 'Provide context to LLM powered agents',
  modelContext: {
    userId: null, // could be anon or stored elsewhere
    agentName: 'ingentx',
    "goals": ["summarize news", "respond in dry humor"],
    "preferences": {
      "verbosity": "low",
      "format": "markdown"
    },
    "memory": {
      "known_topics": ["AI ethics", "Vue 3", "Model Context Protocol"],
      "personality_notes": [
        "likes concise answers", 
        "hates buzzwords"]
    },
    "tools": ["web", "python", "vector_db"]
    tone: {
      desired: Tone.NEUTRAL,
      observed: Tone.CONVERSATIONAL,
      history: []
    },
    topicMap: {
      "ai_agents": {
        id: "ai_agents",
        title: "AI Agents and Context",
        summary: "Discussion about context fragility and persistent memory in LLM-powered agents.",
        keywords: ["context", "memory", "LLMs", "agents"],
        createdAt: 1717432340000,
        updatedAt: 1717432400000,
        turns: [
          {
            timestamp: 1717432340000,
            user: "What is context fragility?",
            assistant: "How easily a model loses track of important information in a conversation."
          },
          {
            timestamp: 1717432400000,
            user: "Can we fix that with Model Context Protocol?",
            assistant: "MCP can help by standardizing memory handoff and session framing."
          }
        ]
      },

      "vue_pinia_session": {
        id: "vue_pinia_session",
        title: "Pinia Store Design in Vue",
        summary: "Session and tone management using Pinia with Vue 3.",
        keywords: ["Vue", "Pinia", "state management", "tone tracking"],
        createdAt: 1717435000000,
        updatedAt: 1717435050000,
        turns: [
          {
            timestamp: 1717435010000,
            user: "Pinia",
            assistant: "Implementing an MCP structure in a Vue Pina Store."
          }
        ]
      }
    },
    facts: [],
    openQuestions: []
  }
}),
          
        

⚠️ Context Fragility

I've been working on the tendency of large language models and AI agents to lose track of conversational, situational, or task-specific context over time, especially in nuanced or long-running interactions.

Large Language Models (LLMs) are prone to context fragility when they:

• Misunderstand shifts in tone or topic
• Forget prior facts or introduce contradictions
• Respond inconsistently due to prompt misalignment

It's not that models have no context — it’s that their hold on it is *brittle*. A minor shock, and it shatters. That’s very different from “stateless,” “forgetful,” or even “non-deterministic.” Fragility implies something more: the illusion of robustness that can fail without warning.

It's a real issue in AI-driven systems, especially in longer or more abstract conversations, where the model appears to forget the thread or misinterpret key ideas a few interactions in. Even small deviations in phrasing, tone, or metaphor can cause the model to “drop the thread,” and suddenly it's asking you to re-explain things you thought were settled.

🧠 Model Context Protocol

The Model Context Protocol ( MCP ) is a proposed standard for managing context in AI agents, particularly those powered by large language models (LLMs). It aims to provide a structured way to handle context, memory, and user intent across sessions, enabling more robust and persistent interactions.

Combining MCP with a state management system like Pinia in Vue 3 allows developers to create AI agents that can maintain context over time, improving the user experience and making interactions more meaningful. If that state is distributed across a peer-to-peer network, it becomes more robust against censorship and context loss.

🛠 How MCP Helps

MCP helps reduce context fragility by:

• Structuring context as data, not just a long prompt string
• Enabling agent-to-model contracts with explicit fields
• Supporting multi-turn planning via structured memory (e.g. goals, tone, facts)
• Allowing middleware or UIs to inspect, edit, or enrich context before passing it to the model

I Believe Code Should Be:

🔧 Open Source

Closed source code benefits a very small sub section of people. Open source benefits humanity. Open source allows anyone to inspect, improve, and share code, fostering collaboration and innovation. It ensures transparency, trust, and rapid progress, making technology accessible to all rather than a privileged few.

🔄 Distributed

The last few years have proven that centralization makes censorship trivial. Code running on decentralized peer-to-peer networks however, as opposed to typical applications where the backend code is running on centralized servers, has proven much more difficult to censor.

📊 Standardized

The Model Context Protocol (MCP) is a strong starting structure to standardize interactions between agents and models. It enables:

• Modularity Pass consistent state between agents, UIs, memory layers, and models.

• Serialization Save, log, or debug the full context easily at any point in a conversation.

• Interpretability Clear separation of elements like user intent, tone, memory, facts, and goals.

📦 Real-World Example: Vue Agent + MCP

I've been working with Pinia stores aligned with MCP principles:

session: {
  modelContext: {
    memory: {
      knownTopics: [],
      personalityNotes: ["likes concise answers", "hates buzzwords"]
    },
    tone: {
      desired,
      observed,
      history
    },
    userIntent: "...",
    facts: [...],
    goals: [...],
    openQuestions: [...]
  }
}

This structure enables:

• Context injection into prompt templates
• Session-to-prompt hygiene, with no accidental leaks or overlaps
• Dynamic UI behavior — e.g. changing tone color if there's a tone mismatch

MCP is not a rigid format — it’s more like a living scaffold or a "proto-buffer for cognition".

You can extend it with:

• Recursive topic threads
• Temporally tagged facts or beliefs
• Function call memories
• Modular exports for agents-to-agents communication

Even at its most basic, MCP is:

• Explicit
• Portable
• Inspectable

😼 which provides context — which is exactly what LLM-based agents need most.

💬 Contact