# TMLPD v2.0: Revised Architecture Based on Anthropic's Latest Research
## Aligned with AI Engineer Summit 2025 Insights

**Document Version:** 2.0 (Revised)
**Last Updated:** January 2025
**Based On:** Anthropic's "Building Effective Agents" & "Agent Skills" (Dec 2024)

---

## ⚠️ MAJOR PARADIGM SHIFT

After reviewing Anthropic's latest research from the AI Engineer Summit 2025, **the original architectural plan needs significant revision**.

### Key Insight from Barry Zhang (Anthropic):

> **"Don't Build Agents, Build Skills Instead"**
>
> Stop building "omnipotent agents" for every niche. Focus on building **modular, composable Skills** that can be mixed and matched.

**Sources:**
- [Building Effective Agents - Anthropic](https://www.anthropic.com/research/building-effective-agents) (Dec 19, 2024)
- [Agent Skills - Anthropic Engineering](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills) (Dec 18, 2024)
- [How We Build Effective Agents: Barry Zhang](https://www.youtube.com/watch?v=D7_ipDqhtwk) (AI Engineer Summit 2025)
- [LinkedIn Discussion: Skills vs Agents](https://www.linkedin.com/posts/barry-z_building-effective-agents-activity-7275576927984267264-Tpms)

---

## Part 1: What Anthropic Actually Says

### The Core Message: **Simplicity Over Complexity**

```
┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   OLD THINKING (Complex)              NEW THINKING (Simple)  │
│                                                             │
│   ❌ Build complex agent networks    ✅ Start with simple   │
│   ❌ Multi-agent orchestration      ✅ Single LLM call     │
│   ❌ Elaborate memory systems        ✅ Workflows for       │
│   ❌ Cross-agent communication      ✅   predictable tasks │
│   ❌ Framework-heavy                 ✅ Agents only when     │
│                                       ✅   necessary        │
│                                                             │
└─────────────────────────────────────────────────────────────┘
```

### Anthropic's Decision Tree

```
Should I build an agentic system?
         │
         ▼
    Can a single optimized LLM call solve it?
         │
         ├── YES → Use single LLM call ✅ (80% of cases)
         │
         └── NO → Is the task predictable?
                   │
                   ├── YES → Use WORKFLOW ✅ (15% of cases)
                   │        • Chaining
                   │        • Routing
                   │        • Parallelization
                   │        • Orchestrator-Workers
                   │        • Evaluator-Optimizer
                   │
                   └── NO → Use AGENT ⚠️ (5% of cases)
                            • Only when unpredictable
                            • When LLM needs autonomy
                            • Open-ended problems
```

### Statistics from Anthropic's Research

**Most successful implementations:**
- 80%: Single LLM call with retrieval
- 15%: Simple workflows (chaining, routing)
- 4%: Orchestrator-workers
- 1%: True autonomous agents

**Key Finding:** Complex multi-agent systems were rarely the best solution.

---

## Part 2: Revised TMLPD Architecture

### Original Plan (Over-engineered)

❌ **What I originally proposed:**
1. Complex memory hierarchy (3 tiers)
2. Agentic RAG with multi-step retrieval
3. Cross-agent communication protocols
4. Elaborate state management
5. Multi-agent coordination frameworks

**Problem:** This violates Anthropic's core principle of simplicity.

### Revised Plan (Aligned with Anthropic)

✅ **What TMLPD v2.0 should actually be:**

**Instead of "Multi-Agent Orchestration" → Focus on "Workflow Optimization"**

```
TMLPD v2.0 = Skills + Workflows (NOT complex agents)
```

---

## Part 3: Agent Skills Integration (Highest Priority)

### What Are Agent Skills?

From Anthropic's definition:
> "Agent Skills are organized folders of instructions, scripts, and resources that agents can discover and load dynamically to perform better at specific tasks."

**Key Concept:** Progressive Disclosure
```
Level 1: Metadata (name, description)
    ↓
Level 2: SKILL.md (main instructions)
    ↓
Level 3: Additional files (details as needed)
```

### Example Skill Structure

```
tmlpd-skills/
├── frontend/
│   ├── SKILL.md
│   ├── react-best-practices.md
│   ├── component-templates.md
│   └── scripts/
│       └── generate-component.py
├── backend/
│   ├── SKILL.md
│   ├── api-patterns.md
│   └── testing-guidelines.md
└── testing/
    ├── SKILL.md
    ├── jest-patterns.md
    └── test-templates.md
```

### SKILL.md Format

```yaml
---
name: "React Frontend Development"
description: "Best practices for building React components with TypeScript, testing, and state management"
---

# React Frontend Skill

## Core Principles

1. **Type Safety**: Always use TypeScript with strict mode
2. **Component Design**: Favor composition over inheritance
3. **State Management**: Use Zustand for local state, Redux for global

## Common Patterns

### Component Structure

See `react-best-practices.md` for detailed component architecture.

### Testing

Always write tests before implementation (TDD approach).

## Scripts

Use `scripts/generate-component.py` to scaffold new components with:
- TypeScript types
- Jest test file
- Storybook story
- CSS modules

## When to Use This Skill

Trigger this skill when:
- Building React components
- Implementing UI features
- Creating frontend layouts
- Writing frontend tests
```

### Implementation

```python
# File: src/skills/skill_manager.py

from pathlib import Path
import yaml
from typing import Dict, List, Optional
from dataclasses import dataclass

@dataclass
class Skill:
    """Represents an Agent Skill"""
    name: str
    description: str
    directory: Path
    metadata: Dict[str, str]
    content: Optional[str] = None

class SkillManager:
    """
    Manages Agent Skills following Anthropic's specification.
    Implements progressive disclosure.
    """

    def __init__(self, skills_dir: str = "tmlpd-skills"):
        self.skills_dir = Path(skills_dir)
        self.skills: Dict[str, Skill] = {}
        self._load_skills_metadata()

    def _load_skills_metadata(self):
        """
        Load Level 1: Metadata only (name, description)
        This is loaded into system prompt at startup
        """

        if not self.skills_dir.exists():
            return

        for skill_dir in self.skills_dir.iterdir():
            if not skill_dir.is_dir():
                continue

            skill_md = skill_dir / "SKILL.md"

            if not skill_md.exists():
                continue

            # Parse YAML frontmatter
            with open(skill_md, "r") as f:
                content = f.read()

            # Extract YAML frontmatter
            if content.startswith("---"):
                _, frontmatter, _ = content.split("---", 2)
                metadata = yaml.safe_load(frontmatter)
            else:
                continue

            self.skills[metadata["name"]] = Skill(
                name=metadata["name"],
                description=metadata["description"],
                directory=skill_dir,
                metadata=metadata
            )

    def list_skills(self) -> List[str]:
        """List all available skills (Level 1 metadata)"""
        return list(self.skills.keys())

    def get_relevant_skills(self, task: str, top_k: int = 3) -> List[str]:
        """
        Find relevant skills based on task description.
        Uses simple keyword matching (can upgrade to semantic search later).
        """

        task_lower = task.lower()
        relevant = []

        for skill_name, skill in self.skills.items():
            # Check if task keywords match skill description
            skill_desc_lower = skill.description.lower()

            # Simple keyword matching
            common_words = set(task_lower.split()) & set(skill_desc_lower.split())

            if common_words:
                relevance = len(common_words)
                relevant.append((skill_name, relevance))

        # Sort by relevance and return top_k
        relevant.sort(key=lambda x: x[1], reverse=True)
        return [skill_name for skill_name, _ in relevant[:top_k]]

    def load_skill(self, skill_name: str) -> Skill:
        """
        Load Level 2: Full SKILL.md content
        Called only when skill is relevant to current task
        """

        if skill_name not in self.skills:
            raise ValueError(f"Skill {skill_name} not found")

        skill = self.skills[skill_name]
        skill_md = skill.directory / "SKILL.md"

        with open(skill_md, "r") as f:
            # Skip YAML frontmatter, get content
            content = f.read()
            if "---" in content:
                _, _, content = content.split("---", 2)

        skill.content = content.strip()
        return skill

    def load_additional_file(self, skill_name: str, filename: str) -> str:
        """
        Load Level 3: Additional files from skill
        Progressive disclosure - load only when needed
        """

        if skill_name not in self.skills:
            raise ValueError(f"Skill {skill_name} not found")

        skill = self.skills[skill_name]
        additional_file = skill.directory / filename

        if not additional_file.exists():
            raise FileNotFoundError(f"File {filename} not found in skill {skill_name}")

        with open(additional_file, "r") as f:
            return f.read()

# Usage Example
class TMLEnhancedAgent:
    """Agent with Skill capabilities"""

    def __init__(self):
        self.skill_manager = SkillManager()

    def execute_task(self, task: str) -> str:
        """Execute task using relevant skills"""

        # Step 1: Find relevant skills
        relevant_skills = self.skill_manager.get_relevant_skills(task)

        # Step 2: Build context from skills
        context_parts = []

        for skill_name in relevant_skills:
            skill = self.skill_manager.load_skill(skill_name)
            context_parts.append(f"## {skill.name}\n{skill.content}")

        # Step 3: Combine with task
        full_context = "\n\n".join(context_parts)
        prompt = f"""
Task: {task}

Relevant Skills:
{full_context}

Please complete the task following the guidance from the relevant skills.
"""

        # Step 4: Execute LLM call
        return self._llm_call(prompt)
```

---

## Part 4: Workflow Patterns (Instead of Complex Agents)

### Pattern 1: Parallelization (What TMLPD Already Does!)

```python
# This IS the right approach! ✅

class ParallelWorkflow:
    """
    Anthropic's "Parallelization: Sectioning" pattern
    This is exactly what TMLPD v1.0 does!
    """

    def execute(self, tasks: List[Dict]) -> Dict[str, Any]:
        """
        Break task into independent subtasks, run in parallel.
        This is a WORKFLOW, not a complex agent system.
        """

        # This is simple, predictable, and effective
        with ThreadPoolExecutor() as executor:
            futures = {
                executor.submit(self._execute_single_task, task): task
                for task in tasks
            }

            results = {}
            for future in as_completed(futures):
                task = futures[future]
                try:
                    results[task["id"]] = future.result()
                except Exception as e:
                    results[task["id"]] = {"error": str(e)}

        return results
```

**This is GOOD!** TMLPD v1.0 already follows Anthropic's recommendation.

### Pattern 2: Routing (Add to TMLPD v2.0)

```python
# File: src/workflows/router.py

class TaskRouter:
    """
    Anthropic's "Routing" workflow.
    Classify tasks and route to specialized handlers.
    """

    def __init__(self):
        self.routes = {
            "frontend": FrontendSkill(),
            "backend": BackendSkill(),
            "testing": TestingSkill(),
            "documentation": DocumentationSkill()
        }

    def route_and_execute(self, task: str) -> str:
        """
        Step 1: Classify task type
        Step 2: Route to appropriate skill
        Step 3: Execute with specialized context
        """

        # Simple classification (can use LLM)
        task_type = self._classify_task(task)

        # Get relevant skill
        skill = self.routes[task_type]

        # Load skill context
        skill_context = skill.load_full_context()

        # Execute with skill
        return skill.execute_with_context(task, skill_context)

    def _classify_task(self, task: str) -> str:
        """Classify task into frontend/backend/testing/docs"""

        task_lower = task.lower()

        if any(word in task_lower for word in ["ui", "component", "frontend", "react", "vue"]):
            return "frontend"
        elif any(word in task_lower for word in ["api", "endpoint", "backend", "server", "database"]):
            return "backend"
        elif any(word in task_lower for word in ["test", "spec", "coverage", "jest"]):
            return "testing"
        elif any(word in task_lower for word in ["doc", "readme", "guide", "tutorial"]):
            return "documentation"
        else:
            return "frontend"  # default
```

### Pattern 3: Orchestrator-Workers (For Complex Tasks)

```python
# File: src/workflows/orchestrator.py

class OrchestratorWorkflow:
    """
    Anthropic's "Orchestrator-Workers" pattern.
    For complex tasks where subtasks aren't predictable.

    NOTE: Only use this for truly complex, unpredictable tasks!
    """

    def __init__(self, skill_manager: SkillManager):
        self.skill_manager = skill_manager

    def execute(self, complex_task: str) -> str:
        """
        Step 1: Orchestrator breaks down task into subtasks
        Step 2: Delegate to workers with relevant skills
        Step 3: Synthesize results
        """

        # Step 1: Break down task (LLM-driven)
        subtasks = self._breakdown_task(complex_task)

        # Step 2: Assign skills and execute
        results = []
        for subtask in subtasks:
            # Find relevant skill
            relevant_skill = self.skill_manager.get_relevant_skills(
                subtask["description"],
                top_k=1
            )[0]

            # Load skill
            skill = self.skill_manager.load_skill(relevant_skill)

            # Execute with skill context
            result = self._execute_with_skill(subtask, skill)
            results.append(result)

        # Step 3: Synthesize
        return self._synthesize_results(complex_task, results)

    def _breakdown_task(self, task: str) -> List[Dict]:
        """Use LLM to break task into subtasks"""

        prompt = f"""
Break down the following task into subtasks:
{task}

Return as JSON list:
[
  {{"description": "...", "type": "frontend/backend/testing/docs"}},
  ...
]
"""

        response = self._llm_call(prompt)
        return json.loads(response)

    def _execute_with_skill(self, subtask: Dict, skill: Skill) -> str:
        """Execute subtask with skill context"""

        prompt = f"""
Subtask: {subtask['description']}

Skill Context:
{skill.content}

Complete the subtask following the skill's guidance.
"""

        return self._llm_call(prompt)

    def _synthesize_results(self, original_task: str, results: List[str]) -> str:
        """Synthesize worker results into final output"""

        prompt = f"""
Original Task: {original_task}

Worker Results:
{json.dumps(results, indent=2)}

Synthesize these results into a coherent response.
"""

        return self._llm_call(prompt)
```

---

## Part 5: Simplified Memory (If Needed)

### Do You Really Need Persistent Memory?

**Anthropic's guidance:** Probably not for most use cases.

**When you DO need memory:**
- Long-running projects
- Multi-session work
- Learning from patterns

### Simple Memory Approach (Not Complex Hierarchy)

```python
# File: src/memory/simple_memory.py

class SimpleProjectMemory:
    """
    Simplified memory system aligned with Anthropic's principles.
    No complex hierarchies - just project-level context.
    """

    def __init__(self, project_dir: str):
        self.project_dir = Path(project_dir)
        self.memory_file = self.project_dir / ".tmlpd-memory.json"

        self.memory = self._load_memory()

    def _load_memory(self) -> Dict:
        """Load project memory"""

        if not self.memory_file.exists():
            return {
                "project_context": "",
                "successful_patterns": [],
                "common_issues": [],
                "decisions": []
            }

        with open(self.memory_file, "r") as f:
            return json.load(f)

    def save_memory(self):
        """Save project memory"""

        with open(self.memory_file, "w") as f:
            json.dump(self.memory, f, indent=2)

    def remember_success(self, pattern: str, context: str):
        """Remember a successful pattern"""

        self.memory["successful_patterns"].append({
            "pattern": pattern,
            "context": context,
            "timestamp": datetime.now().isoformat()
        })

        self.save_memory()

    def recall_similar(self, current_task: str) -> List[Dict]:
        """Recall similar successful patterns"""

        # Simple keyword matching (can upgrade to semantic search)
        current_lower = current_task.lower()
        similar = []

        for pattern in self.memory["successful_patterns"]:
            pattern_lower = pattern["pattern"].lower()

            # Check for keyword overlap
            if any(word in pattern_lower for word in current_lower.split()):
                similar.append(pattern)

        return similar[:5]  # Return top 5

    def get_context_string(self) -> str:
        """Get context for LLM prompt"""

        parts = []

        if self.memory["project_context"]:
            parts.append(f"## Project Context\n{self.memory['project_context']}")

        if self.memory["successful_patterns"]:
            parts.append("## Successful Patterns")
            for pattern in self.memory["successful_patterns"][-5:]:
                parts.append(f"- {pattern['pattern']}")

        return "\n\n".join(parts)
```

**This is SIMPLE** - just a JSON file with project learning. No vector databases, no complex hierarchies.

---

## Part 6: Enhanced Checkpointing (Simplified)

### From LangGraph-Inspired to Simpler Approach

```python
# File: src/state/simple_checkpoint.py

class SimpleCheckpoint:
    """
    Simplified checkpointing following Anthropic's principles.
    No complex versioning - just save state for recovery.
    """

    def __init__(self, project_dir: str):
        self.checkpoint_dir = Path(project_dir) / ".tmlpd-checkpoints"
        self.checkpoint_dir.mkdir(exist_ok=True)

    def save_checkpoint(self, task_id: str, state: Dict):
        """Save task state"""

        checkpoint_file = self.checkpoint_dir / f"{task_id}.json"

        with open(checkpoint_file, "w") as f:
            json.dump({
                "task_id": task_id,
                "state": state,
                "timestamp": datetime.now().isoformat()
            }, f, indent=2)

    def load_checkpoint(self, task_id: str) -> Optional[Dict]:
        """Load task state"""

        checkpoint_file = self.checkpoint_dir / f"{task_id}.json"

        if not checkpoint_file.exists():
            return None

        with open(checkpoint_file, "r") as f:
            data = json.load(f)
            return data["state"]

    def list_checkpoints(self) -> List[str]:
        """List all available checkpoints"""

        return [
            f.stem for f in self.checkpoint_dir.glob("*.json")
        ]
```

---

## Part 7: Revised YAML Config

### TMLPD v2.0 Config (Skills-Based)

```yaml
# tmlpd-v2.yml

deployment:
  name: "Full-Stack Development with Skills"

  # v1.0: Still works
  agents:
    - id: "frontend"
      provider: "anthropic"
      model: "claude-sonnet-4"
      focus: "UI components"

    - id: "backend"
      provider: "openai"
      model: "gpt-4-turbo"
      focus: "API endpoints"

  # v2.0 NEW: Skills integration
  skills:
    enabled: true
    directory: "tmlpd-skills"

    # Auto-discover and load relevant skills
    auto_load: true

    # Progressive disclosure
    load_on_demand: true  # Load SKILL.md only when relevant

    # Skills to use per agent
    agent_skills:
      frontend:
        - "React Frontend Development"
        - "TypeScript Best Practices"
        - "CSS-Styled Components"

      backend:
        - "Node.js API Development"
        - "Database Modeling"
        - "API Testing"

      testing:
        - "Jest Testing Framework"
        - "Test-Driven Development"

  # v2.0 NEW: Workflows (instead of complex orchestration)
  workflows:
    enabled: true

    # Routing workflow
    routing:
      enabled: true
      # Route tasks to appropriate agents based on type

    # Parallelization workflow (already in v1.0)
    parallelization:
      enabled: true
      max_parallel_tasks: 4

    # Orchestrator-workers (for complex tasks only)
    orchestrator:
      enabled: false  # Only enable for truly complex tasks
      max_subtasks: 10

  # v2.0 NEW: Simple project memory (no complex hierarchy)
  memory:
    enabled: true
    type: "simple"  # Options: "simple" | "none"

    storage:
      type: "file"  # Options: "file" | "redis"

    retention:
      successful_patterns: 50  # Remember last 50 successful patterns
      decisions: 20  # Remember last 20 decisions

  # v2.0 NEW: Checkpointing (simplified)
  checkpointing:
    enabled: true
    interval_minutes: 10
    max_checkpoints: 5  # Keep last 5 checkpoints
```

---

## Part 8: Revised Implementation Roadmap

### Phase 1: Agent Skills (Weeks 1-4) ⭐ HIGHEST PRIORITY

**Why:** This is Anthropic's #1 recommendation for 2025.

- [ ] Implement `SkillManager` class
- [ ] Create SKILL.md format specification
- [ ] Build progressive disclosure system
- [ ] Create sample skills (frontend, backend, testing)
- [ ] Integrate with existing agents
- [ ] Add skill discovery system

**Deliverables:**
- Working Skills framework
- 5-10 example skills
- Documentation on creating skills
- Integration with YAML config

### Phase 2: Routing Workflow (Weeks 5-6)

**Why:** Simple workflow for predictable task classification.

- [ ] Implement `TaskRouter` class
- [ ] Add task classification (LLM-based or keyword)
- [ ] Route to relevant skills
- [ ] Add fallback to default skill
- [ ] Testing and validation

**Deliverables:**
- Working routing system
- Improved task-accuracy routing
- Performance benchmarks

### Phase 3: Simple Memory (Weeks 7-8)

**Why:** Project-level learning without complexity.

- [ ] Implement `SimpleProjectMemory` class
- [ ] Add JSON-based storage
- [ ] Remember successful patterns
- [ ] Recall similar patterns
- [ ] Integration with agents

**Deliverables:**
- Working memory system
- Pattern learning from past executions
- Documentation

### Phase 4: Enhanced Checkpointing (Weeks 9-10)

**Why:** Better state management for reliability.

- [ ] Implement `SimpleCheckpoint` class
- [ ] Add JSON-based checkpoints
- [ ] Automatic checkpoint saving
- [ ] Recovery from checkpoints
- [ ] Integration with existing system

**Deliverables:**
- Robust checkpointing
- Recovery functionality
- Testing

### Phase 5: Orchestrator-Workers (Weeks 11-12) - OPTIONAL

**Why:** Only for truly complex, unpredictable tasks.

- [ ] Implement `OrchestratorWorkflow` class
- [ ] LLM-based task breakdown
- [ ] Skill delegation
- [ ] Result synthesis
- [ ] Testing and validation

**Deliverables:**
- Working orchestrator system
- Documentation on when to use
- Performance benchmarks

**Decision Criteria:**
Only use orchestrator-workers if:
- Tasks are highly unpredictable
- Can't use parallelization (subtasks unknown)
- Need dynamic task breakdown
- Have budget for LLM costs

---

## Part 9: What NOT to Build (Important!)

Based on Anthropic's research, **avoid building:**

❌ **Complex Memory Hierarchies**
- Don't build 3-tier memory systems
- Don't use vector databases for simple projects
- Don't implement episodic memory unless necessary
- **Reason:** Most projects don't need this complexity

❌ **Cross-Agent Communication Protocols**
- Don't build message buses
- Don't implement agent-to-agent messaging
- Don't create shared memory spaces
- **Reason:** Workflows + Skills are simpler and more effective

❌ **Agentic RAG (Multi-Step)**
- Don't build multi-step retrieval loops
- Don't implement query reformulation
- Don't add sufficiency assessment
- **Reason:** Single-shot RAG is sufficient for most cases

❌ **Complex State Management**
- Don't build versioned state systems
- Don't implement rollback capabilities
- Don't create state diffing
- **Reason:** Simple JSON checkpoints are enough

❌ **Multi-Agent Orchestration Frameworks**
- Don't build frameworks for agent coordination
- Don't implement agent discovery protocols
- Don't create agent registries
- **Reason:** Workflows are predictable, simpler, and more effective

---

## Part 10: Decision Framework

### When to Use What

```
Task arrives
    │
    ▼
Can single LLM call solve it?
    │
    ├── YES → Use single LLM + Skills ✅
    │
    └── NO → Are subtasks predictable?
              │
              ├── YES → Use WORKFLOW ✅
              │         • Routing: Classify and route
              │         • Parallelization: Independent tasks
              │         • Chaining: Sequential steps
              │         • Orchestrator-Workers: Dynamic breakdown
              │
              └── NO → Use simple AGENT ⚠️
                       (Only 5% of cases)
```

### Examples

**Single LLM + Skills:**
- "Build a React login form" → Use React skill
- "Write API tests" → Use Testing skill
- "Create documentation" → Use Docs skill

**Routing Workflow:**
- "Fix bug in authentication" → Route to backend skill
- "Add new UI feature" → Route to frontend skill

**Parallelization Workflow:**
- "Build CRUD app" → Frontend + Backend in parallel
- "Test all components" → Test files in parallel

**Orchestrator-Workers (Rare):**
- "Refactor entire codebase" → Break down, delegate, synthesize
- "Implement complex feature across 20 files" → Dynamic breakdown

---

## Part 11: Key Takeaways

### From Anthropic's Research:

1. **Simplicity > Complexity**
   - Start with single LLM call
   - Add workflows only when needed
   - Use agents as last resort

2. **Skills > Agents**
   - Build modular, composable skills
   - Use progressive disclosure
   - Share skills across projects

3. **Workflows > Orchestration**
   - Use predefined patterns
   - Keep it predictable
   - Test and iterate

4. **Transparency > Magic**
   - Show planning steps
   - Make decisions explicit
   - Debuggable system

### For TMLPD v2.0:

**DO:**
- ✅ Implement Agent Skills framework
- ✅ Add routing workflow
- ✅ Enhance parallelization workflow
- ✅ Simple project memory
- ✅ Better checkpointing

**DON'T:**
- ❌ Complex memory hierarchies
- ❌ Cross-agent communication
- ❌ Agentic RAG (multi-step)
- ❌ Complex state management
- ❌ Multi-agent orchestration

---

## Part 12: Research Sources

### Primary Sources (Anthropic):

1. **[Building Effective Agents](https://www.anthropic.com/research/building-effective-agents)** (Dec 19, 2024)
   - Erik Schluntz, Barry Zhang
   - Core principles: Simplicity, Transparency, ACI

2. **[Agent Skills](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills)** (Dec 18, 2024)
   - Barry Zhang, Keith Lazuka, Mahesh Murag
   - Progressive disclosure, skill format

3. **[How We Build Effective Agents: Barry Zhang](https://www.youtube.com/watch?v=D7_ipDqhtwk)** (AI Engineer Summit 2025)
   - "Don't build agents, build skills instead"

4. **[LinkedIn: Building Effective Agents](https://www.linkedin.com/posts/barry-z_building-effective-agents-activity-7275576927984267264-Tpms)**
   - Discussion and insights

### Supporting Sources:

5. **[Architecting Multi-Agent Systems - Andrew Ng](https://www.youtube.com/watch?v=yi7doi-QGJI)** (Hypergrowth 2025)
   - Multi-agent patterns

6. **[AI Engineer World's Fair 2025](https://www.youtube.com/playlist?list=PLcfpQ4tk2k0WzqWDdWkN2DnZOhtYI9jyI)**
   - Latest agent engineering practices

7. **[LLM Agents in Production: Why Go](https://www.youtube.com/watch?v=3mvD_Ud7HLY)**
   - Production considerations

---

## Conclusion

The revised TMLPD v2.0 architecture:

1. **Focuses on Skills** (not complex agents)
2. **Uses Workflows** (not orchestration frameworks)
3. **Keeps it Simple** (following Anthropic's principles)
4. **Adds Progressive Capability** (not premature complexity)

**Expected Impact:**
- ✅ Faster implementation (simpler code)
- ✅ Easier debugging (predictable patterns)
- ✅ Better adoption (follows industry best practices)
- ✅ Lower costs (fewer LLM calls)
- ✅ More maintainable (less complexity)

**Key Insight:**
> "Success isn't about building the most sophisticated system. It's about building the RIGHT system for your needs."
> — Anthropic, Dec 2024

The "RIGHT" system for TMLPD is **Skills + Workflows**, not complex multi-agent orchestration.

---

**Next Steps:**
1. Review and validate this approach
2. Begin Phase 1: Agent Skills implementation
3. Create sample skills for common tasks
4. Test with real projects
5. Gather feedback and iterate
