docs(arch): autonomous evolution of agentic and microkernel blueprints

architectures/agentic-architecture/data-flow.md

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+---
+technology: Agentic Architecture
+domain: Architecture
+level: Senior/Architect
+version: Agnostic
+tags: [ai-agents, data-flow, orchestration, multi-agent-systems]
+ai_role: Senior Software Architect
+last_updated: 2026-04-17
+---
+
+# 🌊 Agentic Architecture Data Flow
+
+## Request Lifecycle
+
+```mermaid
+sequenceDiagram
+    participant User
+    participant Orchestrator
+    participant Planner
+    participant Coder
+    participant Reviewer
+    participant DB
+
+    User->>Orchestrator: Send Request
+    activate Orchestrator
+
+    Orchestrator->>Planner: Request Task Breakdown
+    Planner-->>Orchestrator: Execution Plan
+
+    Orchestrator->>Coder: Execute Coding Step
+    Coder-->>Reviewer: Code Output
+
+    Reviewer-->>Orchestrator: Code Verification
+
+    Orchestrator->>DB: Persist Approved State
+    Orchestrator-->>User: Final Result
+    deactivate Orchestrator
+```
+
+## Core Principles
+1. **Delegation:** Orchestrator decomposes tasks and delegates.
+2. **Handoff Validation:** Structured data must be validated between agent handoffs.

architectures/agentic-architecture/folder-structure.md

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+---
+technology: Agentic Architecture
+domain: Architecture
+level: Senior/Architect
+version: Agnostic
+tags: [ai-agents, folder-structure, orchestration]
+ai_role: Senior Software Architect
+last_updated: 2026-04-17
+---
+
+# 📁 Agentic Architecture Folder Structure
+
+```mermaid
+graph TD
+    User[User Request] --> Orchestrator[Orchestrator Agent]
+    Orchestrator --> |Decomposes task| Planner[Planner Agent]
+    Planner -.-> |Plan| Orchestrator
+    Orchestrator --> |Delegates| Coder[Coder Agent]
+    Orchestrator --> |Delegates| Reviewer[Reviewer Agent]
+    Coder -.-> |Code output| Reviewer
+    Reviewer -.-> |Verification| Orchestrator
+    Orchestrator --> DB[(Shared Context / Memory)]
+
+    %% Added Design Token Styles for Mermaid Diagrams
+    classDef default fill:#e1f5fe,stroke:#03a9f4,stroke-width:2px,color:#000;
+    classDef component fill:#e8f5e9,stroke:#4caf50,stroke-width:2px,color:#000;
+    classDef layout fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px,color:#000;
+
+    class User component;
+    class Orchestrator layout;
+    class Planner component;
+    class Coder component;
+    class Reviewer component;
+    class DB default;
+```
+
+## Directory Blueprint
+```text
+src/
+├── 📁 orchestrator/     # Main coordinator agent
+├── 📁 workers/          # Specialized worker agents (Planner, Coder, Reviewer)
+└── 📁 memory/           # Shared context and validation schemas
+```

architectures/agentic-architecture/implementation-guide.md

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+---
+technology: Agentic Architecture
+domain: Architecture
+level: Senior/Architect
+version: Agnostic
+tags: [ai-agents, implementation-guide, orchestration]
+ai_role: Senior Software Architect
+last_updated: 2026-04-17
+---
+
+# 🛠️ Agentic Architecture Implementation Guide
+
+## 1. Monolithic Agent State Management
+
+### ❌ Bad Practice
+```typescript
+class MonolithicAIAgent {
+  constructor(private readonly llm: LLMClient) {}
+
+  async handleRequest(userPrompt: string) {
+    // Agent attempts to plan, code, and review all at once with unbounded context
+    const fullContext = await this.gatherAllSystemContext();
+    const prompt = `Plan this out, write the code, and review it.
+                    Here is the entire database context: ${fullContext}
+                    Task: ${userPrompt}`;
+
+    const response = await this.llm.generate(prompt);
+    // Unsafe execution of non-deterministic output
+    eval(response.code);
+    return response;
+  }
+}
+```
+
+### ⚠️ Problem
+Loading a monolithic agent with unbounded context leads to "Context Explosion", resulting in non-deterministic hallucinations, excessive token costs, and security risks (like arbitrary code execution). A single LLM call attempting multiple distinct personas (Planner, Coder, Reviewer) fundamentally degrades reasoning quality.
+
+### ✅ Best Practice
+```typescript
+interface AgentTask {
+  goal: string;
+  context: unknown;
+}
+
+class OrchestratorAgent {
+  constructor(
+    private readonly planner: PlannerAgent,
+    private readonly coder: CoderAgent,
+    private readonly reviewer: ReviewerAgent
+  ) {}
+
+  async processTask(userPrompt: string) {
+    // 1. Specialized Planner Agent isolates the task roadmap
+    const executionPlan = await this.planner.plan({ goal: userPrompt, context: {} });
+
+    // 2. Specialized Coder Agent executes ONLY the specific sub-tasks
+    const codePayload = await this.coder.execute({ goal: executionPlan.codingSteps, context: executionPlan.schema });
+
+    // 3. Specialized Reviewer Agent deterministically validates the output
+    const isApproved = await this.reviewer.validate({ goal: 'Verify code matches schema', context: codePayload });
+
+    if (!isApproved) {
+        throw new Error("Validation failed in Review phase");
+    }
+
+    return codePayload;
+  }
+}
+```
+
+### 🚀 Solution
+Implementing an **Orchestrator-Worker pattern** strictly isolates responsibilities. Each specialized agent receives only the exact context required for its task (O(1) relevant context per agent), lowering token overhead and drastically increasing deterministic reliability. Validating structured data at every handoff ensures resilient, secure, and predictable Multi-Agent execution.

architectures/agentic-architecture/readme.md

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 This architecture defines the operational boundaries for multi-agent workflows, specifically optimizing for context windows, token efficiency, and deterministic output.
 
-- 🌊 **Data Flow:** Orchestrator-to-Worker execution paths.
-- 📁 **Folder Structure:** Modular isolation of Prompts, Skills, and Contexts.
-- ⚖️ **Trade-offs:** Latency vs. Reasoning depth.
-- 🛠️ **Implementation Guide:** Rules for defining strict agent personas and constraints.
-
-```mermaid
-graph TD
-    User[User Request] --> Orchestrator[Orchestrator Agent]
-    Orchestrator --> |Decomposes task| Planner[Planner Agent]
-    Planner -.-> |Plan| Orchestrator
-    Orchestrator --> |Delegates| Coder[Coder Agent]
-    Orchestrator --> |Delegates| Reviewer[Reviewer Agent]
-    Coder -.-> |Code output| Reviewer
-    Reviewer -.-> |Verification| Orchestrator
-    Orchestrator --> DB[(Shared Context / Memory)]
-
-    %% Added Design Token Styles for Mermaid Diagrams
-    classDef default fill:#e1f5fe,stroke:#03a9f4,stroke-width:2px,color:#000;
-    classDef component fill:#e8f5e9,stroke:#4caf50,stroke-width:2px,color:#000;
-    classDef layout fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px,color:#000;
-
-    class User component;
-    class Orchestrator layout;
-    class Planner component;
-    class Coder component;
-    class Reviewer component;
-    class DB default;
-```
+- 🌊 [**Data Flow:** Orchestrator-to-Worker execution paths](./data-flow.md)
+- 📁 [**Folder Structure:** Modular isolation of Prompts, Skills, and Contexts](./folder-structure.md)
+- ⚖️ [**Trade-offs:** Latency vs. Reasoning depth](./trade-offs.md)
+- 🛠️ [**Implementation Guide:** Rules for defining strict agent personas and constraints](./implementation-guide.md)
 
 ## 🚀 The Core Philosophy
 
 Agentic Architecture emphasizes the decomposition of monolithic tasks into granular, specialized agent workloads managed by a central Orchestrator. This resolves massive context window pollution and isolates functional logic.
 
 > [!IMPORTANT]
 > **AI Constraint:** Agents MUST NOT mutate shared global state directly. They must return deterministic structured data (e.g., JSON schema) to the Orchestrator, which strictly validates the payload before persisting it.
-
----
-
-## 1. Monolithic Agent State Management
-
-### ❌ Bad Practice
-```typescript
-class MonolithicAIAgent {
-  constructor(private readonly llm: LLMClient) {}
-
-  async handleRequest(userPrompt: string) {
-    // Agent attempts to plan, code, and review all at once with unbounded context
-    const fullContext = await this.gatherAllSystemContext();
-    const prompt = `Plan this out, write the code, and review it.
-                    Here is the entire database context: ${fullContext}
-                    Task: ${userPrompt}`;
-
-    const response = await this.llm.generate(prompt);
-    // Unsafe execution of non-deterministic output
-    eval(response.code);
-    return response;
-  }
-}
-```
-
-### ⚠️ Problem
-Loading a monolithic agent with unbounded context leads to "Context Explosion", resulting in non-deterministic hallucinations, excessive token costs, and security risks (like arbitrary code execution). A single LLM call attempting multiple distinct personas (Planner, Coder, Reviewer) fundamentally degrades reasoning quality.
-
-### ✅ Best Practice
-```typescript
-interface AgentTask {
-  goal: string;
-  context: unknown;
-}
-
-class OrchestratorAgent {
-  constructor(
-    private readonly planner: PlannerAgent,
-    private readonly coder: CoderAgent,
-    private readonly reviewer: ReviewerAgent
-  ) {}
-
-  async processTask(userPrompt: string) {
-    // 1. Specialized Planner Agent isolates the task roadmap
-    const executionPlan = await this.planner.plan({ goal: userPrompt, context: {} });
-
-    // 2. Specialized Coder Agent executes ONLY the specific sub-tasks
-    const codePayload = await this.coder.execute({ goal: executionPlan.codingSteps, context: executionPlan.schema });
-
-    // 3. Specialized Reviewer Agent deterministically validates the output
-    const isApproved = await this.reviewer.validate({ goal: 'Verify code matches schema', context: codePayload });
-
-    if (!isApproved) {
-        throw new Error("Validation failed in Review phase");
-    }
-
-    return codePayload;
-  }
-}
-```
-
-### 🚀 Solution
-Implementing an **Orchestrator-Worker pattern** strictly isolates responsibilities. Each specialized agent receives only the exact context required for its task (O(1) relevant context per agent), lowering token overhead and drastically increasing deterministic reliability. Validating structured data at every handoff ensures resilient, secure, and predictable Multi-Agent execution.

architectures/agentic-architecture/trade-offs.md

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+---
+technology: Agentic Architecture
+domain: Architecture
+level: Senior/Architect
+version: Agnostic
+tags: [ai-agents, trade-offs, orchestration]
+ai_role: Senior Software Architect
+last_updated: 2026-04-17
+---
+
+# ⚖️ Agentic Architecture Trade-offs
+
+## Pros and Cons
+
+| Category | Factor | Description |
+| :--- | :--- | :--- |
+| ✅ **Advantage** | Determinism | Smaller, bounded context per agent leads to more reliable outputs. |
+| ✅ **Advantage** | Token Efficiency | Passing only necessary context reduces API costs. |
+| ❌ **Disadvantage** | Latency | Multiple agent hops take longer than a single monolithic request. |
+| ❌ **Disadvantage** | Complexity | Managing handoffs, retries, and state between multiple agents is difficult. |

architectures/microkernel-architecture/data-flow.md

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+---
+technology: Microkernel Architecture
+domain: Architecture
+level: Senior/Architect
+version: Agnostic
+tags: [plugin-architecture, data-flow, core-system]
+ai_role: Senior Software Architect
+last_updated: 2026-04-18
+---
+
+# 🌊 Microkernel Architecture Data Flow
+
+## Request Lifecycle
+
+```mermaid
+sequenceDiagram
+    participant User
+    participant Core
+    participant Registry
+    participant Plugin
+
+    User->>Core: Trigger Action
+    activate Core
+
+    Core->>Registry: Lookup applicable Plugin
+    Registry-->>Core: Return Plugin Reference
+
+    Core->>Plugin: Execute Plugin Method (passing data)
+    activate Plugin
+    Plugin-->>Core: Return Result
+    deactivate Plugin
+
+    Core-->>User: Final Response
+    deactivate Core
+```
+
+## Core Principles
+1. **Contract Enforcement:** All plugins must strictly adhere to the interfaces defined by the Core.
+2. **Registry Lookup:** Execution relies on a dynamic registry mapping at runtime.

architectures/microkernel-architecture/folder-structure.md

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+---
+technology: Microkernel Architecture
+domain: Architecture
+level: Senior/Architect
+version: Agnostic
+tags: [plugin-architecture, folder-structure, core-system]
+ai_role: Senior Software Architect
+last_updated: 2026-04-18
+---
+
+# 📁 Microkernel Architecture Folder Structure
+
+```mermaid
+graph TD
+    Core[Core System / Microkernel] --> Registry[Plugin Registry]
+    Registry --> PluginA[Payment Plugin]
+    Registry --> PluginB[Notification Plugin]
+    Registry --> PluginC[Analytics Plugin]
+
+    %% Added Design Token Styles for Mermaid Diagrams
+    classDef default fill:#e1f5fe,stroke:#03a9f4,stroke-width:2px,color:#000;
+    classDef component fill:#e8f5e9,stroke:#4caf50,stroke-width:2px,color:#000;
+
+    class Core default;
+    class Registry default;
+    class PluginA component;
+    class PluginB component;
+    class PluginC component;
+```
+
+## Directory Blueprint
+```text
+src/
+├── 📁 core/             # Core system orchestrator and registry interfaces
+├── 📁 plugins/          # Independent modules implementing core interfaces
+└── 📁 shared/           # Data types and common utilities
+```