forked from cardosofelipe/fast-next-template
docs: add architecture spikes and deep analysis documentation
Add comprehensive spike research documents: - SPIKE-002: Agent Orchestration Pattern (LangGraph + Temporal hybrid) - SPIKE-006: Knowledge Base pgvector (RAG with hybrid search) - SPIKE-007: Agent Communication Protocol (JSON-RPC + Redis Streams) - SPIKE-008: Workflow State Machine (transitions lib + event sourcing) - SPIKE-009: Issue Synchronization (bi-directional sync with conflict resolution) - SPIKE-010: Cost Tracking (LiteLLM callbacks + budget enforcement) - SPIKE-011: Audit Logging (structured event sourcing) - SPIKE-012: Client Approval Flow (checkpoint-based approvals) Add architecture documentation: - ARCHITECTURE_DEEP_ANALYSIS.md: Memory management, security, testing strategy - IMPLEMENTATION_ROADMAP.md: 6-phase, 24-week implementation plan Closes #2, #6, #7, #8, #9, #10, #11, #12 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
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docs/architecture/ARCHITECTURE_DEEP_ANALYSIS.md
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docs/architecture/ARCHITECTURE_DEEP_ANALYSIS.md
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# Syndarix Architecture Deep Analysis
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**Version:** 1.0
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**Date:** 2025-12-29
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**Status:** Draft - Architectural Thinking
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---
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## Executive Summary
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This document captures deep architectural thinking about Syndarix beyond the immediate spikes. It addresses complex challenges that arise when building a truly autonomous multi-agent system and proposes solutions based on first principles.
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---
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## 1. Agent Memory and Context Management
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### The Challenge
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Agents in Syndarix may work on projects for weeks or months. LLM context windows are finite (128K-200K tokens), but project context grows unboundedly. How do we maintain coherent agent "memory" over time?
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### Analysis
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**Context Window Constraints:**
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| Model | Context Window | Practical Limit (with tools) |
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|-------|---------------|------------------------------|
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| Claude 3.5 Sonnet | 200K tokens | ~150K usable |
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| GPT-4 Turbo | 128K tokens | ~100K usable |
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| Llama 3 (70B) | 8K-128K tokens | ~80K usable |
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**Memory Types Needed:**
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1. **Working Memory** - Current task context (fits in context window)
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2. **Short-term Memory** - Recent conversation history (RAG-retrievable)
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3. **Long-term Memory** - Project knowledge, past decisions (RAG + summarization)
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4. **Episodic Memory** - Specific past events/mistakes to learn from
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### Proposed Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Agent Memory System │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ │ Working │ │ Short-term │ │ Long-term │ │
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│ │ Memory │ │ Memory │ │ Memory │ │
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│ │ (Context) │ │ (Redis) │ │ (pgvector) │ │
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│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
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│ │ │ │ │
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│ └───────────────────┼──────────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌──────────────────────────────────────────────────────────┐ │
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│ │ Context Assembler │ │
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│ │ │ │
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│ │ 1. System prompt (agent personality, role) │ │
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│ │ 2. Project context (from long-term memory) │ │
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│ │ 3. Task context (current issue, requirements) │ │
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│ │ 4. Relevant history (from short-term memory) │ │
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│ │ 5. User message │ │
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│ │ │ │
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│ │ Total: Fit within context window limits │ │
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│ └──────────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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**Context Compression Strategy:**
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```python
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class ContextManager:
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"""Manages agent context to fit within LLM limits."""
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MAX_CONTEXT_TOKENS = 100_000 # Leave room for response
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async def build_context(
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self,
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agent: AgentInstance,
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task: Task,
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user_message: str
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) -> list[Message]:
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# Fixed costs
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system_prompt = self._get_system_prompt(agent) # ~2K tokens
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task_context = self._get_task_context(task) # ~1K tokens
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# Variable budget
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remaining = self.MAX_CONTEXT_TOKENS - token_count(system_prompt, task_context, user_message)
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# Allocate remaining to memories
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long_term = await self._query_long_term(agent, task, budget=remaining * 0.4)
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short_term = await self._get_short_term(agent, budget=remaining * 0.4)
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episodic = await self._get_relevant_episodes(agent, task, budget=remaining * 0.2)
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return self._assemble_messages(
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system_prompt, task_context, long_term, short_term, episodic, user_message
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)
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```
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**Conversation Summarization:**
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- After every N turns (e.g., 10), summarize conversation and archive
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- Use smaller/cheaper model for summarization
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- Store summaries in pgvector for semantic retrieval
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### Recommendation
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Implement a **tiered memory system** with automatic context compression and semantic retrieval. Use Redis for hot short-term memory, pgvector for cold long-term memory, and automatic summarization to prevent context overflow.
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---
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## 2. Cross-Project Knowledge Sharing
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### The Challenge
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Each project has isolated knowledge, but agents could benefit from cross-project learnings:
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- Common patterns (authentication, testing, CI/CD)
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- Technology expertise (how to configure Kubernetes)
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- Anti-patterns (what didn't work before)
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### Analysis
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**Privacy Considerations:**
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- Client data must remain isolated (contractual, legal)
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- Technical patterns are generally shareable
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- Need clear data classification
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**Knowledge Categories:**
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| Category | Scope | Examples |
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|----------|-------|----------|
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| **Client Data** | Project-only | Requirements, business logic, code |
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| **Technical Patterns** | Global | Best practices, configurations |
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| **Agent Learnings** | Global | What approaches worked/failed |
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| **Anti-patterns** | Global | Common mistakes to avoid |
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### Proposed Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Knowledge Graph │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌─────────────────────────────────────────────────────────┐ │
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│ │ GLOBAL KNOWLEDGE │ │
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│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
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│ │ │ Patterns │ │ Anti-patterns│ │ Expertise │ │ │
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│ │ │ Library │ │ Library │ │ Index │ │ │
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│ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │
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│ └─────────────────────────────────────────────────────────┘ │
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│ ▲ │
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│ │ Curated extraction │
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│ │ │
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│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
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│ │ Project A │ │ Project B │ │ Project C │ │
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│ │ Knowledge │ │ Knowledge │ │ Knowledge │ │
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│ │ (Isolated) │ │ (Isolated) │ │ (Isolated) │ │
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│ └─────────────┘ └─────────────┘ └─────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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**Knowledge Extraction Pipeline:**
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```python
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class KnowledgeExtractor:
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"""Extracts shareable learnings from project work."""
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async def extract_learnings(self, project_id: str) -> list[Learning]:
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"""
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Run periodically or after sprints to extract learnings.
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Human review required before promoting to global.
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"""
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# Get completed work
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completed_issues = await self.get_completed_issues(project_id)
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# Extract patterns using LLM
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patterns = await self.llm.extract_patterns(
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completed_issues,
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categories=["architecture", "testing", "deployment", "security"]
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)
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# Classify privacy
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for pattern in patterns:
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pattern.privacy_level = await self.llm.classify_privacy(pattern)
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# Return only shareable patterns for review
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return [p for p in patterns if p.privacy_level == "public"]
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```
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### Recommendation
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Implement **privacy-aware knowledge extraction** with human review gate. Project knowledge stays isolated by default; only explicitly approved patterns flow to global knowledge.
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---
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## 3. Agent Specialization vs Generalization Trade-offs
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### The Challenge
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Should each agent type be highly specialized (depth) or have overlapping capabilities (breadth)?
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### Analysis
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**Specialization Benefits:**
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- Deeper expertise in domain
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- Cleaner system prompts
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- Less confusion about responsibilities
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- Easier to optimize prompts per role
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**Generalization Benefits:**
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- Fewer agent types to maintain
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- Smoother handoffs (shared context)
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- More flexible team composition
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- Graceful degradation if agent unavailable
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**Current Agent Types (10):**
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| Role | Primary Domain | Potential Overlap |
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|------|---------------|-------------------|
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| Product Owner | Requirements | Business Analyst |
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| Business Analyst | Documentation | Product Owner |
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| Project Manager | Planning | Product Owner |
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| Software Architect | Design | Senior Engineer |
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| Software Engineer | Coding | Architect, QA |
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| UI/UX Designer | Interface | Frontend Engineer |
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| QA Engineer | Testing | Software Engineer |
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| DevOps Engineer | Infrastructure | Senior Engineer |
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| AI/ML Engineer | ML/AI | Software Engineer |
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| Security Expert | Security | All |
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### Proposed Approach: Layered Specialization
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Agent Capability Layers │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ Layer 3: Role-Specific Expertise │
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│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
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│ │ Product │ │ Architect│ │Engineer │ │ QA │ │
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│ │ Owner │ │ │ │ │ │ │ │
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│ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │
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│ │ │ │ │ │
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│ Layer 2: Shared Professional Skills │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ Technical Communication | Code Understanding | Git │ │
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│ │ Documentation | Research | Problem Decomposition │ │
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│ └──────────────────────────────────────────────────────┘ │
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│ │ │
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│ Layer 1: Foundation Model Capabilities │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ Reasoning | Analysis | Writing | Coding (LLM Base) │ │
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│ └──────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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**Capability Inheritance:**
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```python
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class AgentTypeBuilder:
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"""Builds agent types with layered capabilities."""
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BASE_CAPABILITIES = [
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"reasoning", "analysis", "writing", "coding_assist"
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]
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PROFESSIONAL_SKILLS = [
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"technical_communication", "code_understanding",
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"git_operations", "documentation", "research"
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]
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ROLE_SPECIFIC = {
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"ENGINEER": ["code_generation", "code_review", "testing", "debugging"],
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"ARCHITECT": ["system_design", "adr_writing", "tech_selection"],
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"QA": ["test_planning", "test_automation", "bug_reporting"],
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# ...
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}
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def build_capabilities(self, role: AgentRole) -> list[str]:
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return (
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self.BASE_CAPABILITIES +
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self.PROFESSIONAL_SKILLS +
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self.ROLE_SPECIFIC[role]
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)
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```
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### Recommendation
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Adopt **layered specialization** where all agents share foundational and professional capabilities, with role-specific expertise on top. This enables smooth collaboration while maintaining clear responsibilities.
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---
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## 4. Human-Agent Collaboration Model
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### The Challenge
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Beyond approval gates, how do humans effectively collaborate with autonomous agents during active work?
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### Interaction Patterns
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| Pattern | Use Case | Frequency |
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|---------|----------|-----------|
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| **Approval** | Confirm before action | Per checkpoint |
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| **Guidance** | Steer direction | On-demand |
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| **Override** | Correct mistake | Rare |
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| **Pair Working** | Work together | Optional |
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| **Review** | Evaluate output | Post-completion |
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### Proposed Collaboration Interface
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Human-Agent Collaboration Dashboard │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌─────────────────────────────────────────────────────────┐ │
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│ │ Activity Stream │ │
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│ │ ────────────────────────────────────────────────────── │ │
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│ │ [10:23] Dave (Engineer) is implementing login API │ │
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│ │ [10:24] Dave created auth/service.py │ │
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│ │ [10:25] Dave is writing unit tests │ │
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│ │ [LIVE] Dave: "I'm adding JWT validation. Using HS256..." │ │
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│ └─────────────────────────────────────────────────────────┘ │
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│ │
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│ ┌─────────────────────────────────────────────────────────┐ │
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│ │ Intervention Panel │ │
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│ │ │ │
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│ │ [💬 Chat] [⏸️ Pause] [↩️ Undo Last] [📝 Guide] │ │
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│ │ │ │
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│ │ Quick Guidance: │ │
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│ │ ┌─────────────────────────────────────────────────┐ │ │
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│ │ │ "Use RS256 instead of HS256 for JWT signing" │ │ │
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│ │ │ [Send] 📤 │ │ │
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│ │ └─────────────────────────────────────────────────┘ │ │
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│ └─────────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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**Intervention API:**
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```python
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@router.post("/agents/{agent_id}/intervene")
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async def intervene(
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agent_id: UUID,
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intervention: InterventionRequest,
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current_user: User = Depends(get_current_user)
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):
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"""Allow human to intervene in agent work."""
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match intervention.type:
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case "pause":
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await orchestrator.pause_agent(agent_id)
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case "resume":
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await orchestrator.resume_agent(agent_id)
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case "guide":
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await orchestrator.send_guidance(agent_id, intervention.message)
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case "undo":
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await orchestrator.undo_last_action(agent_id)
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case "override":
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await orchestrator.override_decision(agent_id, intervention.decision)
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```
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### Recommendation
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Build a **real-time collaboration dashboard** with intervention capabilities. Humans should be able to observe, guide, pause, and correct agents without stopping the entire workflow.
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---
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## 5. Testing Strategy for Autonomous AI Systems
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### The Challenge
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Traditional testing (unit, integration, E2E) doesn't capture autonomous agent behavior. How do we ensure quality?
|
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|
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### Testing Pyramid for AI Agents
|
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|
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```
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▲
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╱ ╲
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||||
╱ ╲
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╱ E2E ╲ Agent Scenarios
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╱ Agent ╲ (Full workflows)
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╱─────────╲
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╱ Integration╲ Tool + LLM Integration
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╱ (with mocks) ╲ (Deterministic responses)
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╱─────────────────╲
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╱ Unit Tests ╲ Orchestrator, Services
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╱ (no LLM needed) ╲ (Pure logic)
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╱───────────────────────╲
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╱ Prompt Testing ╲ System prompt evaluation
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╱ (LLM evals) ╲(Quality metrics)
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╱─────────────────────────────╲
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```
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### Test Categories
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**1. Prompt Testing (Eval Framework):**
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```python
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class PromptEvaluator:
|
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"""Evaluate system prompt quality."""
|
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TEST_CASES = [
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EvalCase(
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name="requirement_extraction",
|
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input="Client wants a mobile app for food delivery",
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expected_behaviors=[
|
||||
"asks clarifying questions",
|
||||
"identifies stakeholders",
|
||||
"considers non-functional requirements"
|
||||
]
|
||||
),
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||||
EvalCase(
|
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name="code_review_thoroughness",
|
||||
input="Review this PR: [vulnerable SQL code]",
|
||||
expected_behaviors=[
|
||||
"identifies SQL injection",
|
||||
"suggests parameterized queries",
|
||||
"mentions security best practices"
|
||||
]
|
||||
)
|
||||
]
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||||
|
||||
async def evaluate(self, agent_type: AgentType) -> EvalReport:
|
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results = []
|
||||
for case in self.TEST_CASES:
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response = await self.llm.complete(
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system=agent_type.system_prompt,
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user=case.input
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)
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score = await self.judge_response(response, case.expected_behaviors)
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results.append(score)
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return EvalReport(results)
|
||||
```
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|
||||
**2. Integration Testing (Mock LLM):**
|
||||
```python
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@pytest.fixture
|
||||
def mock_llm():
|
||||
"""Deterministic LLM responses for integration tests."""
|
||||
responses = {
|
||||
"analyze requirements": "...",
|
||||
"generate code": "def hello(): return 'world'",
|
||||
"review code": "LGTM"
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||||
}
|
||||
return MockLLM(responses)
|
||||
|
||||
async def test_story_implementation_workflow(mock_llm):
|
||||
"""Test full workflow with predictable responses."""
|
||||
orchestrator = AgentOrchestrator(llm=mock_llm)
|
||||
|
||||
result = await orchestrator.execute_workflow(
|
||||
workflow="implement_story",
|
||||
inputs={"story_id": "TEST-123"}
|
||||
)
|
||||
|
||||
assert result.status == "completed"
|
||||
assert "hello" in result.artifacts["code"]
|
||||
```
|
||||
|
||||
**3. Agent Scenario Testing:**
|
||||
```python
|
||||
class AgentScenarioTest:
|
||||
"""End-to-end agent behavior testing."""
|
||||
|
||||
@scenario("engineer_handles_bug_report")
|
||||
async def test_bug_resolution(self):
|
||||
"""Engineer agent should fix bugs correctly."""
|
||||
# Setup
|
||||
project = await create_test_project()
|
||||
engineer = await spawn_agent("engineer", project)
|
||||
|
||||
# Act
|
||||
bug = await create_issue(
|
||||
project,
|
||||
title="Login button not working",
|
||||
type="bug"
|
||||
)
|
||||
result = await engineer.handle(bug)
|
||||
|
||||
# Assert
|
||||
assert result.pr_created
|
||||
assert result.tests_pass
|
||||
assert "button" in result.changes_summary.lower()
|
||||
```
|
||||
|
||||
### Recommendation
|
||||
|
||||
Implement a **multi-layer testing strategy** with prompt evals, deterministic integration tests, and scenario-based agent testing. Use LLM-as-judge for evaluating open-ended responses.
|
||||
|
||||
---
|
||||
|
||||
## 6. Rollback and Recovery
|
||||
|
||||
### The Challenge
|
||||
|
||||
Autonomous agents will make mistakes. How do we recover gracefully?
|
||||
|
||||
### Error Categories
|
||||
|
||||
| Category | Example | Recovery Strategy |
|
||||
|----------|---------|-------------------|
|
||||
| **Reversible** | Wrong code generated | Revert commit, regenerate |
|
||||
| **Partially Reversible** | Merged bad PR | Revert PR, fix, re-merge |
|
||||
| **Non-reversible** | Deployed to production | Forward-fix or rollback deploy |
|
||||
| **External Side Effects** | Email sent to client | Apology + correction |
|
||||
|
||||
### Recovery Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ Recovery System │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ ┌─────────────────────────────────────────────────────────┐ │
|
||||
│ │ Action Log │ │
|
||||
│ │ ┌──────────────────────────────────────────────────┐ │ │
|
||||
│ │ │ Action ID | Agent | Type | Reversible | State │ │ │
|
||||
│ │ ├──────────────────────────────────────────────────┤ │ │
|
||||
│ │ │ a-001 | Dave | commit | Yes | completed │ │ │
|
||||
│ │ │ a-002 | Dave | push | Yes | completed │ │ │
|
||||
│ │ │ a-003 | Dave | create_pr | Yes | completed │ │ │
|
||||
│ │ │ a-004 | Kate | merge_pr | Partial | completed │ │ │
|
||||
│ │ └──────────────────────────────────────────────────┘ │ │
|
||||
│ └─────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
│ ┌─────────────────────────────────────────────────────────┐ │
|
||||
│ │ Rollback Engine │ │
|
||||
│ │ │ │
|
||||
│ │ rollback_to(action_id) -> Reverses all actions after │ │
|
||||
│ │ undo_action(action_id) -> Reverses single action │ │
|
||||
│ │ compensate(action_id) -> Creates compensating action │ │
|
||||
│ │ │ │
|
||||
│ └─────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**Action Logging:**
|
||||
```python
|
||||
class ActionLog:
|
||||
"""Immutable log of all agent actions for recovery."""
|
||||
|
||||
async def record(
|
||||
self,
|
||||
agent_id: UUID,
|
||||
action_type: str,
|
||||
inputs: dict,
|
||||
outputs: dict,
|
||||
reversible: bool,
|
||||
reverse_action: str | None = None
|
||||
) -> ActionRecord:
|
||||
record = ActionRecord(
|
||||
id=uuid4(),
|
||||
agent_id=agent_id,
|
||||
action_type=action_type,
|
||||
inputs=inputs,
|
||||
outputs=outputs,
|
||||
reversible=reversible,
|
||||
reverse_action=reverse_action,
|
||||
timestamp=datetime.utcnow()
|
||||
)
|
||||
await self.db.add(record)
|
||||
return record
|
||||
|
||||
async def rollback_to(self, action_id: UUID) -> RollbackResult:
|
||||
"""Rollback all actions after the given action."""
|
||||
actions = await self.get_actions_after(action_id)
|
||||
|
||||
results = []
|
||||
for action in reversed(actions):
|
||||
if action.reversible:
|
||||
result = await self._execute_reverse(action)
|
||||
results.append(result)
|
||||
else:
|
||||
results.append(RollbackSkipped(action, reason="non-reversible"))
|
||||
|
||||
return RollbackResult(results)
|
||||
```
|
||||
|
||||
**Compensation Pattern:**
|
||||
```python
|
||||
class CompensationEngine:
|
||||
"""Handles compensating actions for non-reversible operations."""
|
||||
|
||||
COMPENSATIONS = {
|
||||
"email_sent": "send_correction_email",
|
||||
"deployment": "rollback_deployment",
|
||||
"external_api_call": "create_reversal_request"
|
||||
}
|
||||
|
||||
async def compensate(self, action: ActionRecord) -> CompensationResult:
|
||||
if action.action_type in self.COMPENSATIONS:
|
||||
compensation = self.COMPENSATIONS[action.action_type]
|
||||
return await self._execute_compensation(compensation, action)
|
||||
else:
|
||||
return CompensationResult(
|
||||
status="manual_required",
|
||||
message=f"No automatic compensation for {action.action_type}"
|
||||
)
|
||||
```
|
||||
|
||||
### Recommendation
|
||||
|
||||
Implement **comprehensive action logging** with rollback capabilities. Define compensation strategies for non-reversible actions. Enable point-in-time recovery for project state.
|
||||
|
||||
---
|
||||
|
||||
## 7. Security Considerations for Autonomous Agents
|
||||
|
||||
### Threat Model
|
||||
|
||||
| Threat | Risk | Mitigation |
|
||||
|--------|------|------------|
|
||||
| Agent executes malicious code | High | Sandboxed execution, code review gates |
|
||||
| Agent exfiltrates data | High | Network isolation, output filtering |
|
||||
| Prompt injection via user input | Medium | Input sanitization, prompt hardening |
|
||||
| Agent credential abuse | Medium | Least-privilege tokens, short TTL |
|
||||
| Agent collusion | Low | Independent agent instances, monitoring |
|
||||
|
||||
### Security Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ Security Layers │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ Layer 4: Output Filtering │
|
||||
│ ┌─────────────────────────────────────────────────────────┐ │
|
||||
│ │ - Code scan before commit │ │
|
||||
│ │ - Secrets detection │ │
|
||||
│ │ - Policy compliance check │ │
|
||||
│ └─────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
│ Layer 3: Action Authorization │
|
||||
│ ┌─────────────────────────────────────────────────────────┐ │
|
||||
│ │ - Role-based permissions │ │
|
||||
│ │ - Project scope enforcement │ │
|
||||
│ │ - Sensitive action approval │ │
|
||||
│ └─────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
│ Layer 2: Input Sanitization │
|
||||
│ ┌─────────────────────────────────────────────────────────┐ │
|
||||
│ │ - Prompt injection detection │ │
|
||||
│ │ - Content filtering │ │
|
||||
│ │ - Schema validation │ │
|
||||
│ └─────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
│ Layer 1: Infrastructure Isolation │
|
||||
│ ┌─────────────────────────────────────────────────────────┐ │
|
||||
│ │ - Container sandboxing │ │
|
||||
│ │ - Network segmentation │ │
|
||||
│ │ - File system restrictions │ │
|
||||
│ └─────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Recommendation
|
||||
|
||||
Implement **defense-in-depth** with multiple security layers. Assume agents can be compromised and design for containment.
|
||||
|
||||
---
|
||||
|
||||
## Summary of Recommendations
|
||||
|
||||
| Area | Recommendation | Priority |
|
||||
|------|----------------|----------|
|
||||
| Memory | Tiered memory with context compression | High |
|
||||
| Knowledge | Privacy-aware extraction with human gate | Medium |
|
||||
| Specialization | Layered capabilities with role-specific top | Medium |
|
||||
| Collaboration | Real-time dashboard with intervention | High |
|
||||
| Testing | Multi-layer with prompt evals | High |
|
||||
| Recovery | Action logging with rollback engine | High |
|
||||
| Security | Defense-in-depth, assume compromise | High |
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Validate with spike research** - Update based on spike findings
|
||||
2. **Create detailed ADRs** - For memory, recovery, security
|
||||
3. **Prototype critical paths** - Memory system, rollback engine
|
||||
4. **Security review** - External audit before production
|
||||
|
||||
---
|
||||
|
||||
*This document captures architectural thinking to guide implementation. It should be updated as spikes complete and design evolves.*
|
||||
339
docs/architecture/IMPLEMENTATION_ROADMAP.md
Normal file
339
docs/architecture/IMPLEMENTATION_ROADMAP.md
Normal file
@@ -0,0 +1,339 @@
|
||||
# Syndarix Implementation Roadmap
|
||||
|
||||
**Version:** 1.0
|
||||
**Date:** 2025-12-29
|
||||
**Status:** Draft
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
This roadmap outlines the phased implementation approach for Syndarix, prioritizing foundational infrastructure before advanced features. Each phase builds upon the previous, with clear milestones and deliverables.
|
||||
|
||||
---
|
||||
|
||||
## Phase 0: Foundation (Weeks 1-2)
|
||||
**Goal:** Establish development infrastructure and basic platform
|
||||
|
||||
### 0.1 Repository Setup
|
||||
- [x] Fork PragmaStack to Syndarix
|
||||
- [x] Create spike backlog in Gitea
|
||||
- [x] Complete architecture documentation
|
||||
- [ ] Rebrand codebase (Issue #13 - in progress)
|
||||
- [ ] Configure CI/CD pipelines
|
||||
- [ ] Set up development environment documentation
|
||||
|
||||
### 0.2 Core Infrastructure
|
||||
- [ ] Configure Redis for cache + pub/sub
|
||||
- [ ] Set up Celery worker infrastructure
|
||||
- [ ] Configure pgvector extension
|
||||
- [ ] Create MCP server directory structure
|
||||
- [ ] Set up Docker Compose for local development
|
||||
|
||||
### Deliverables
|
||||
- Fully branded Syndarix repository
|
||||
- Working local development environment
|
||||
- CI/CD pipeline running tests
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Core Platform (Weeks 3-6)
|
||||
**Goal:** Basic project and agent management without LLM integration
|
||||
|
||||
### 1.1 Data Model
|
||||
- [ ] Create Project entity and CRUD
|
||||
- [ ] Create AgentType entity and CRUD
|
||||
- [ ] Create AgentInstance entity and CRUD
|
||||
- [ ] Create Issue entity with external tracker fields
|
||||
- [ ] Create Sprint entity and CRUD
|
||||
- [ ] Database migrations with Alembic
|
||||
|
||||
### 1.2 API Layer
|
||||
- [ ] Project management endpoints
|
||||
- [ ] Agent type configuration endpoints
|
||||
- [ ] Agent instance management endpoints
|
||||
- [ ] Issue CRUD endpoints
|
||||
- [ ] Sprint management endpoints
|
||||
|
||||
### 1.3 Real-time Infrastructure
|
||||
- [ ] Implement EventBus with Redis Pub/Sub
|
||||
- [ ] Create SSE endpoint for project events
|
||||
- [ ] Implement event types enum
|
||||
- [ ] Add keepalive mechanism
|
||||
- [ ] Client-side SSE handling
|
||||
|
||||
### 1.4 Frontend Foundation
|
||||
- [ ] Project dashboard page
|
||||
- [ ] Agent configuration UI
|
||||
- [ ] Issue list and detail views
|
||||
- [ ] Real-time activity feed component
|
||||
- [ ] Basic navigation and layout
|
||||
|
||||
### Deliverables
|
||||
- CRUD operations for all core entities
|
||||
- Real-time event streaming working
|
||||
- Basic admin UI for configuration
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: MCP Integration (Weeks 7-10)
|
||||
**Goal:** Build MCP servers for external integrations
|
||||
|
||||
### 2.1 MCP Client Infrastructure
|
||||
- [ ] Create MCPClientManager class
|
||||
- [ ] Implement server registry
|
||||
- [ ] Add connection management with reconnection
|
||||
- [ ] Create tool call routing
|
||||
|
||||
### 2.2 LLM Gateway MCP (Priority 1)
|
||||
- [ ] Create FastMCP server structure
|
||||
- [ ] Implement LiteLLM integration
|
||||
- [ ] Add model group routing
|
||||
- [ ] Implement failover chain
|
||||
- [ ] Add cost tracking callbacks
|
||||
- [ ] Create token usage logging
|
||||
|
||||
### 2.3 Knowledge Base MCP (Priority 2)
|
||||
- [ ] Create pgvector schema for embeddings
|
||||
- [ ] Implement document ingestion pipeline
|
||||
- [ ] Create chunking strategies (code, markdown, text)
|
||||
- [ ] Implement semantic search
|
||||
- [ ] Add hybrid search (vector + keyword)
|
||||
- [ ] Per-project collection isolation
|
||||
|
||||
### 2.4 Git MCP (Priority 3)
|
||||
- [ ] Create Git operations wrapper
|
||||
- [ ] Implement clone, commit, push operations
|
||||
- [ ] Add branch management
|
||||
- [ ] Create PR operations
|
||||
- [ ] Add Gitea API integration
|
||||
- [ ] Implement GitHub/GitLab adapters
|
||||
|
||||
### 2.5 Issues MCP (Priority 4)
|
||||
- [ ] Create issue sync service
|
||||
- [ ] Implement Gitea issue operations
|
||||
- [ ] Add GitHub issue adapter
|
||||
- [ ] Add GitLab issue adapter
|
||||
- [ ] Implement bi-directional sync
|
||||
- [ ] Create conflict resolution logic
|
||||
|
||||
### Deliverables
|
||||
- 4 working MCP servers
|
||||
- LLM calls routed through gateway
|
||||
- RAG search functional
|
||||
- Git operations working
|
||||
- Issue sync with external trackers
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Agent Orchestration (Weeks 11-14)
|
||||
**Goal:** Enable agents to perform autonomous work
|
||||
|
||||
### 3.1 Agent Runner
|
||||
- [ ] Create AgentRunner class
|
||||
- [ ] Implement context assembly
|
||||
- [ ] Add memory management (short-term, long-term)
|
||||
- [ ] Implement action execution
|
||||
- [ ] Add tool call handling
|
||||
- [ ] Create agent error handling
|
||||
|
||||
### 3.2 Agent Orchestrator
|
||||
- [ ] Implement spawn_agent method
|
||||
- [ ] Create terminate_agent method
|
||||
- [ ] Implement send_message routing
|
||||
- [ ] Add broadcast functionality
|
||||
- [ ] Create agent status tracking
|
||||
- [ ] Implement agent recovery
|
||||
|
||||
### 3.3 Inter-Agent Communication
|
||||
- [ ] Define message format schema
|
||||
- [ ] Implement message persistence
|
||||
- [ ] Create message routing logic
|
||||
- [ ] Add @mention parsing
|
||||
- [ ] Implement priority queues
|
||||
- [ ] Add conversation threading
|
||||
|
||||
### 3.4 Background Task Integration
|
||||
- [ ] Create Celery task wrappers
|
||||
- [ ] Implement progress reporting
|
||||
- [ ] Add task chaining for workflows
|
||||
- [ ] Create agent queue routing
|
||||
- [ ] Implement task retry logic
|
||||
|
||||
### Deliverables
|
||||
- Agents can be spawned and communicate
|
||||
- Agents can call MCP tools
|
||||
- Background tasks for long operations
|
||||
- Agent activity visible in real-time
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Workflow Engine (Weeks 15-18)
|
||||
**Goal:** Implement structured workflows for software delivery
|
||||
|
||||
### 4.1 State Machine Foundation
|
||||
- [ ] Create workflow state machine base
|
||||
- [ ] Implement state persistence
|
||||
- [ ] Add transition validation
|
||||
- [ ] Create state history logging
|
||||
- [ ] Implement compensation patterns
|
||||
|
||||
### 4.2 Core Workflows
|
||||
- [ ] Requirements Discovery workflow
|
||||
- [ ] Architecture Spike workflow
|
||||
- [ ] Sprint Planning workflow
|
||||
- [ ] Story Implementation workflow
|
||||
- [ ] Sprint Demo workflow
|
||||
|
||||
### 4.3 Approval Gates
|
||||
- [ ] Create approval checkpoint system
|
||||
- [ ] Implement approval UI components
|
||||
- [ ] Add notification triggers
|
||||
- [ ] Create timeout handling
|
||||
- [ ] Implement escalation logic
|
||||
|
||||
### 4.4 Autonomy Levels
|
||||
- [ ] Implement FULL_CONTROL mode
|
||||
- [ ] Implement MILESTONE mode
|
||||
- [ ] Implement AUTONOMOUS mode
|
||||
- [ ] Create autonomy configuration UI
|
||||
- [ ] Add per-action approval overrides
|
||||
|
||||
### Deliverables
|
||||
- Structured workflows executing
|
||||
- Approval gates working
|
||||
- Autonomy levels configurable
|
||||
- Full sprint cycle possible
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: Advanced Features (Weeks 19-22)
|
||||
**Goal:** Polish and production readiness
|
||||
|
||||
### 5.1 Cost Management
|
||||
- [ ] Real-time cost tracking dashboard
|
||||
- [ ] Budget configuration per project
|
||||
- [ ] Alert threshold system
|
||||
- [ ] Cost optimization recommendations
|
||||
- [ ] Historical cost analytics
|
||||
|
||||
### 5.2 Audit & Compliance
|
||||
- [ ] Comprehensive action logging
|
||||
- [ ] Audit trail viewer UI
|
||||
- [ ] Export functionality
|
||||
- [ ] Retention policy implementation
|
||||
- [ ] Compliance report generation
|
||||
|
||||
### 5.3 Human-Agent Collaboration
|
||||
- [ ] Live activity dashboard
|
||||
- [ ] Intervention panel (pause, guide, undo)
|
||||
- [ ] Agent chat interface
|
||||
- [ ] Context inspector
|
||||
- [ ] Decision explainer
|
||||
|
||||
### 5.4 Additional MCP Servers
|
||||
- [ ] File System MCP
|
||||
- [ ] Code Analysis MCP
|
||||
- [ ] CI/CD MCP
|
||||
|
||||
### Deliverables
|
||||
- Production-ready system
|
||||
- Full observability
|
||||
- Cost controls active
|
||||
- Audit compliance
|
||||
|
||||
---
|
||||
|
||||
## Phase 6: Polish & Launch (Weeks 23-24)
|
||||
**Goal:** Production deployment
|
||||
|
||||
### 6.1 Performance Optimization
|
||||
- [ ] Load testing
|
||||
- [ ] Query optimization
|
||||
- [ ] Caching optimization
|
||||
- [ ] Memory profiling
|
||||
|
||||
### 6.2 Security Hardening
|
||||
- [ ] Security audit
|
||||
- [ ] Penetration testing
|
||||
- [ ] Secrets management
|
||||
- [ ] Rate limiting tuning
|
||||
|
||||
### 6.3 Documentation
|
||||
- [ ] User documentation
|
||||
- [ ] API documentation
|
||||
- [ ] Deployment guide
|
||||
- [ ] Runbook
|
||||
|
||||
### 6.4 Deployment
|
||||
- [ ] Production environment setup
|
||||
- [ ] Monitoring & alerting
|
||||
- [ ] Backup & recovery
|
||||
- [ ] Launch checklist
|
||||
|
||||
---
|
||||
|
||||
## Risk Register
|
||||
|
||||
| Risk | Impact | Probability | Mitigation |
|
||||
|------|--------|-------------|------------|
|
||||
| LLM API outages | High | Medium | Multi-provider failover |
|
||||
| Cost overruns | High | Medium | Budget enforcement, local models |
|
||||
| Agent hallucinations | High | Medium | Approval gates, code review |
|
||||
| Performance bottlenecks | Medium | Medium | Load testing, caching |
|
||||
| Integration failures | Medium | Low | Contract testing, mocks |
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
| Metric | Target | Measurement |
|
||||
|--------|--------|-------------|
|
||||
| Agent task success rate | >90% | Completed tasks / total tasks |
|
||||
| Response time (P95) | <2s | API latency |
|
||||
| Cost per project | <$50/sprint | LLM + compute costs |
|
||||
| Time to first commit | <1 hour | From requirements to PR |
|
||||
| Client satisfaction | >4/5 | Post-sprint survey |
|
||||
|
||||
---
|
||||
|
||||
## Dependencies
|
||||
|
||||
```
|
||||
Phase 0 ─────▶ Phase 1 ─────▶ Phase 2 ─────▶ Phase 3 ─────▶ Phase 4 ─────▶ Phase 5 ─────▶ Phase 6
|
||||
Foundation Core Platform MCP Integration Agent Orch Workflows Advanced Launch
|
||||
│
|
||||
│
|
||||
Depends on:
|
||||
- LLM Gateway
|
||||
- Knowledge Base
|
||||
- Real-time events
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Resource Requirements
|
||||
|
||||
### Development Team
|
||||
- 1 Backend Engineer (Python/FastAPI)
|
||||
- 1 Frontend Engineer (React/Next.js)
|
||||
- 0.5 DevOps Engineer
|
||||
- 0.25 Product Manager
|
||||
|
||||
### Infrastructure
|
||||
- PostgreSQL (managed or self-hosted)
|
||||
- Redis (managed or self-hosted)
|
||||
- Celery workers (2-4 instances)
|
||||
- MCP servers (7 containers)
|
||||
- API server (2+ instances)
|
||||
- Frontend (static hosting or SSR)
|
||||
|
||||
### External Services
|
||||
- Anthropic API (primary LLM)
|
||||
- OpenAI API (fallback)
|
||||
- Ollama (local models, optional)
|
||||
- Gitea/GitHub/GitLab (issue tracking)
|
||||
|
||||
---
|
||||
|
||||
*This roadmap will be refined as spikes complete and requirements evolve.*
|
||||
Reference in New Issue
Block a user