---
name: architecture-synthesis
description: Generate a reference architecture specification from analyzed frameworks. Use when (1) designing a new agent framework based on prior art, (2) defining core primitives (Message, State, Tool types), (3) specifying interface protocols, (4) creating execution loop pseudocode, or (5) producing architecture diagrams and implementation roadmaps.
---

# Architecture Synthesis

Generates a reference architecture specification for a new framework.

## Process

1. **Define primitives** — Message, State, Result, Tool types
2. **Specify interfaces** — Protocols for LLM, Tool, Memory
3. **Design the loop** — Core execution algorithm
4. **Create diagrams** — Visual architecture representation
5. **Produce roadmap** — Implementation phases

## Prerequisites

Before synthesis, ensure you have:
- [ ] Comparative matrix with decisions per dimension
- [ ] Anti-pattern catalog with "Do Not Repeat" list
- [ ] Design requirements document

## Core Primitives Definition

### Message Type

```python
from typing import Literal
from pydantic import BaseModel

class Message(BaseModel):
    """Immutable message in the conversation."""
    role: Literal["system", "user", "assistant", "tool"]
    content: str
    name: str | None = None  # For tool messages
    tool_call_id: str | None = None
    
    class Config:
        frozen = True  # Immutable
```

### State Type

```python
from dataclasses import dataclass, field
from typing import Any

@dataclass(frozen=True)
class AgentState:
    """Immutable agent state - copy-on-write pattern."""
    messages: tuple[Message, ...]
    tool_results: tuple[ToolResult, ...] = ()
    metadata: dict[str, Any] = field(default_factory=dict)
    step_count: int = 0
    
    def with_message(self, msg: Message) -> "AgentState":
        """Return new state with message added."""
        return AgentState(
            messages=(*self.messages, msg),
            tool_results=self.tool_results,
            metadata=self.metadata,
            step_count=self.step_count
        )
```

### Result Types

```python
from typing import Union

@dataclass(frozen=True)
class ToolResult:
    """Result from tool execution."""
    tool_name: str
    success: bool
    output: str | None = None
    error: str | None = None
    
@dataclass(frozen=True)
class AgentFinish:
    """Agent completed its task."""
    output: str
    
@dataclass(frozen=True)
class AgentContinue:
    """Agent needs another step."""
    tool_calls: tuple[ToolCall, ...]

StepResult = Union[AgentFinish, AgentContinue]
```

## Interface Protocols

### LLM Protocol

```python
from typing import Protocol, Iterator

class LLM(Protocol):
    """Minimal LLM interface."""
    
    def generate(self, messages: list[Message]) -> LLMResponse:
        """Generate a response."""
        ...
    
    def stream(self, messages: list[Message]) -> Iterator[str]:
        """Stream response tokens."""
        ...

@dataclass
class LLMResponse:
    """Full LLM response with metadata."""
    content: str
    tool_calls: list[ToolCall] | None
    usage: TokenUsage
    model: str
    raw: Any  # Original API response
```

### Tool Protocol

```python
class Tool(Protocol):
    """Minimal tool interface."""
    
    @property
    def name(self) -> str:
        """Tool identifier."""
        ...
    
    @property
    def description(self) -> str:
        """Human-readable description."""
        ...
    
    @property
    def schema(self) -> dict:
        """JSON Schema for parameters."""
        ...
    
    def execute(self, **kwargs) -> str:
        """Execute the tool."""
        ...
```

### Memory Protocol

```python
class Memory(Protocol):
    """Memory/context management interface."""
    
    def add(self, message: Message) -> None:
        """Add a message to memory."""
        ...
    
    def get_context(self, query: str, max_tokens: int) -> list[Message]:
        """Retrieve relevant context."""
        ...
    
    def clear(self) -> None:
        """Clear memory."""
        ...
```

## Execution Loop Design

### Algorithm Pseudocode

```
FUNCTION run_agent(input: str, max_steps: int) -> str:
    state = initial_state(input)
    
    FOR step IN range(max_steps):
        # 1. Build context
        messages = build_messages(state)
        
        # 2. Call LLM
        response = llm.generate(messages)
        
        # 3. Parse and decide
        result = parse_response(response)
        
        # 4. Handle result
        IF result IS AgentFinish:
            RETURN result.output
        
        IF result IS AgentContinue:
            # Execute tools
            FOR tool_call IN result.tool_calls:
                tool_result = execute_tool(tool_call)
                state = state.with_tool_result(tool_result)
            
            # Feed back to LLM
            state = state.with_message(format_observations(state))
        
        # 5. Emit events
        emit("step_complete", state)
    
    # Max steps reached
    RAISE MaxStepsExceeded(state)
```

### Implementation Template

```python
class Agent:
    def __init__(
        self,
        llm: LLM,
        tools: list[Tool],
        system_prompt: str,
        max_steps: int = 10
    ):
        self.llm = llm
        self.tools = {t.name: t for t in tools}
        self.system_prompt = system_prompt
        self.max_steps = max_steps
        self.callbacks: list[Callback] = []
    
    def run(self, input: str) -> str:
        state = AgentState(messages=(
            Message(role="system", content=self.system_prompt),
            Message(role="user", content=input)
        ))
        
        for step in range(self.max_steps):
            self._emit("step_start", step, state)
            
            # LLM call
            response = self.llm.generate(list(state.messages))
            self._emit("llm_response", response)
            
            # Parse
            result = self._parse_response(response)
            
            # Finish or continue
            if isinstance(result, AgentFinish):
                self._emit("agent_finish", result)
                return result.output
            
            # Execute tools
            for call in result.tool_calls:
                tool_result = self._execute_tool(call)
                state = state.with_tool_result(tool_result)
            
            # Update state
            state = state.with_message(
                Message(role="assistant", content=response.content)
            )
            for tr in state.tool_results[-len(result.tool_calls):]:
                state = state.with_message(
                    Message(role="tool", content=tr.output or tr.error, name=tr.tool_name)
                )
            
            self._emit("step_end", step, state)
        
        raise MaxStepsExceeded(f"Exceeded {self.max_steps} steps")
    
    def _execute_tool(self, call: ToolCall) -> ToolResult:
        tool = self.tools.get(call.name)
        if not tool:
            return ToolResult(call.name, success=False, error=f"Unknown tool: {call.name}")
        
        try:
            output = tool.execute(**call.arguments)
            return ToolResult(call.name, success=True, output=output)
        except Exception as e:
            return ToolResult(call.name, success=False, error=f"{type(e).__name__}: {e}")
```

## Architecture Diagram

```mermaid
graph TB
    subgraph "Core Layer"
        MSG[Message]
        STATE[AgentState]
        RESULT[StepResult]
    end
    
    subgraph "Protocol Layer"
        LLM_P[LLM Protocol]
        TOOL_P[Tool Protocol]
        MEM_P[Memory Protocol]
    end
    
    subgraph "Execution Layer"
        LOOP[Agent Loop]
        PARSER[Response Parser]
        EXECUTOR[Tool Executor]
    end
    
    subgraph "Integration Layer"
        OPENAI[OpenAI LLM]
        ANTHROPIC[Anthropic LLM]
        TOOLS[Built-in Tools]
        VECTOR[Vector Memory]
    end
    
    MSG --> STATE
    STATE --> LOOP
    LOOP --> LLM_P
    LOOP --> PARSER
    PARSER --> RESULT
    RESULT --> EXECUTOR
    EXECUTOR --> TOOL_P
    
    LLM_P -.-> OPENAI
    LLM_P -.-> ANTHROPIC
    TOOL_P -.-> TOOLS
    MEM_P -.-> VECTOR
```

## Implementation Roadmap

### Phase 1: Core (Week 1-2)
- [ ] Define Message, State, Result types
- [ ] Implement LLM Protocol with OpenAI
- [ ] Implement basic Tool Protocol
- [ ] Create minimal Agent loop
- [ ] Add step limit termination

### Phase 2: Robustness (Week 3-4)
- [ ] Add error handling and feedback
- [ ] Implement retry mechanisms
- [ ] Add comprehensive logging
- [ ] Create callback/event system
- [ ] Add token counting

### Phase 3: Extensibility (Week 5-6)
- [ ] Add Memory Protocol
- [ ] Implement vector store integration
- [ ] Create tool discovery/registry
- [ ] Add configuration system
- [ ] Write documentation

### Phase 4: Production (Week 7-8)
- [ ] Add tracing/observability
- [ ] Implement streaming
- [ ] Add rate limiting
- [ ] Create async version
- [ ] Performance optimization

## Output Artifacts

```
reference-architecture/
├── docs/
│   ├── ARCHITECTURE.md      # This document
│   ├── PRIMITIVES.md        # Type definitions
│   ├── PROTOCOLS.md         # Interface specs
│   └── LOOP.md              # Algorithm details
├── diagrams/
│   ├── architecture.mermaid
│   ├── flow.mermaid
│   └── types.mermaid
├── examples/
│   ├── simple_agent.py
│   ├── multi_tool_agent.py
│   └── custom_llm.py
└── ROADMAP.md               # Implementation plan
```

## Integration

- **Inputs from**: `comparative-matrix`, `antipattern-catalog`
- **Produces**: Reference architecture for implementation
- **Validates against**: Original protocol requirements