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agent-review Review AI agent implementations for best practices in architecture, folder structure, design patterns, error handling, and observability. Use when auditing agent codebases or designing new agent systems.
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name agent-review description Review AI agent implementations for best practices in architecture, folder structure, design patterns, error handling, and observability. Use when auditing agent codebases or designing new agent systems. disable-model-invocation true aliases ["review-agent","agent-audit","agent-architecture"]
Agent Implementation Review
Review AI agent implementations for architectural best practices.
Target: $ARGUMENTS (path to agent project or codebase)
When to Use This Skill
Auditing existing agent implementations
Designing new agent architectures
Reviewing agent code for production readiness
Evaluating multi-agent system designs
Assessing agent reliability and observability
Review Process
Discover - Explore folder structure at $ARGUMENTSAnalyze - Check against architecture patternsEvaluate - Score each categoryReport - Generate findings with recommendations
Folder Structure Best Practices
Recommended Agent Project Structure
agent-project/
├── src/
│ ├── agents/ # Agent definitions
│ │ ├── base.py # Base agent class
│ │ ├── planner.py # Planning agent
│ │ └── executor.py # Execution agent
│ ├── tools/ # Tool implementations
│ │ ├── __init__.py
│ │ ├── base.py # Tool base class/interface
│ │ ├── search.py # Search tool
│ │ └── code.py # Code execution tool
│ ├── memory/ # Memory/state management
│ │ ├── short_term.py # Conversation context
│ │ ├── long_term.py # Persistent storage
│ │ └── vector_store.py # Embeddings/RAG
│ ├── prompts/ # Prompt templates
│ │ ├── system.py # System prompts
│ │ └── templates/ # Jinja/string templates
│ ├── orchestration/ # Multi-agent coordination
│ │ ├── router.py # Request routing
│ │ └── workflow.py # Agent workflows
│ ├── models/ # Data models/schemas
│ │ ├── messages.py # Message types
│ │ └── state.py # State schemas
│ └── utils/ # Shared utilities
│ ├── logging.py # Structured logging
│ └── retry.py # Retry logic
├── config/ # Configuration
│ ├── default.yaml # Default settings
│ └── prompts/ # External prompt files
├── tests/ # Test suite
│ ├── unit/
│ ├── integration/
│ └── fixtures/ # Test data
└── scripts/ # CLI/automation
Structure Checklist Component Required Check Agent definitions separated Yes [ ] Tools in dedicated module Yes [ ] Prompts externalized Recommended [ ] Configuration separated Yes [ ] Tests present Yes [ ] Clear separation of concerns Yes [ ]
Design Pattern Checklist
1. Tool Design def search (query ):
return requests.get(f"https://api.com?q={query} " ).json()
class SearchTool (BaseTool ):
name = "search"
description = "Search the web for information"
class InputSchema (BaseModel ):
query: str = Field(..., min_length=1 , max_length=500 )
def execute (self, query: str ) -> ToolResult:
try :
response = self .client.search(query, timeout=10 )
return ToolResult(success=True , data=response)
except Timeout:
return ToolResult(success=False , error="Search timed out" )
except Exception as e:
return ToolResult(success=False , error=str (e))
2. Agent Loop class Agent :
MAX_ITERATIONS = 10
async def run (self, task: str ) -> AgentResult:
state = AgentState(task=task)
for i in range (self .MAX_ITERATIONS):
if self ._should_stop(state):
break
action = await self .plan(state)
result = await self .execute(action)
state = self .update_state(state, result)
return self .finalize(state)
3. Memory Management Type Purpose Persistence Working Current task context Session Short-term Recent conversation Session Long-term User preferences, facts Persistent Episodic Past task summaries Persistent Semantic Embeddings/RAG Persistent
4. Error Handling Category Retry Action Rate limit Yes Exponential backoff Timeout Yes Retry with longer timeout Auth failure No Fail with clear message Invalid input No Return validation error Tool failure Maybe Try alternative tool Model error Yes Retry or fallback model
class AgentError (Exception ):
def __init__ (self, message: str , code: str , recoverable: bool = False ):
self .message = message
self .code = code
self .recoverable = recoverable
@retry(
retry=retry_if_exception_type(RateLimitError ),
wait=wait_exponential(multiplier=1 , max =60 ),
stop=stop_after_attempt(3 )
async def call_model (self, messages: list ) -> str :
try :
return await self .client.complete(messages)
except RateLimitError:
raise
except AuthError as e:
raise AgentError("Authentication failed" , "AUTH_ERROR" , recoverable=False )
5. State Management @dataclass(frozen=True )
class AgentState :
task: str
messages: tuple [Message, ...]
tool_results: tuple [ToolResult, ...]
iteration: int = 0
status: Literal ["running" , "completed" , "failed" ] = "running"
def with_message (self, message: Message ) -> "AgentState" :
return replace(self , messages=self .messages + (message,))
def with_tool_result (self, result: ToolResult ) -> "AgentState" :
return replace(self , tool_results=self .tool_results + (result,))
6. Multi-Agent Coordination Patterns (if applicable):
Pattern Use Case Supervisor One agent routes to specialists Pipeline Sequential agent processing Debate Multiple agents propose, one decides Swarm Autonomous agents, shared goals Hierarchical Manager → workers structure
7. Prompt Management
agent_system_prompt:
version: "1.2"
template: |
You are a helpful assistant with access to these tools:
{% for tool in tools %}
- {{ tool.name }}: {{ tool.description }}
{% endfor %}
Current date: {{ current_date }}
User preferences: {{ user_prefs }}
8. Observability Event Log Level Required Fields Agent start INFO task_id, user_id, task Tool call DEBUG tool_name, inputs, duration Model call DEBUG model, tokens_in, tokens_out, latency Error ERROR error_code, message, stack_trace Agent complete INFO task_id, status, total_duration, total_tokens
logger.info("agent_started" , extra={
"task_id" : task_id,
"user_id" : user_id,
"task_type" : task.type ,
})
logger.debug("tool_executed" , extra={
"task_id" : task_id,
"tool" : tool.name,
"duration_ms" : duration,
"success" : result.success,
})
Configuration Best Practices
Required Configuration Setting Type Description modelstring Model identifier max_iterationsint Loop limit timeout_secondsint Overall timeout tool_timeoutint Per-tool timeout max_tokensint Response limit temperaturefloat Model temperature retry_attemptsint Retry count
Configuration Hierarchy 1. Environment variables (secrets, deployment-specific)
2. Config files (default.yaml, production.yaml)
3. Code defaults (fallbacks only)
class AgentConfig (BaseSettings ):
model: str = "claude-3-sonnet"
max_iterations: int = 10
timeout_seconds: int = 300
class Config :
env_prefix = "AGENT_"
env_file = ".env"
Testing Patterns
Test Categories Type Coverage Purpose Unit Tools, utilities Isolated component tests Integration Agent + tools End-to-end flows Snapshot Prompts Detect prompt regressions Eval Agent responses Quality benchmarks
Required Tests def test_search_tool_timeout ():
tool = SearchTool(timeout=0.001 )
result = tool.execute("test query" )
assert not result.success
assert "timeout" in result.error.lower()
def test_agent_max_iterations ():
agent = Agent(max_iterations=3 )
agent.tools = [InfiniteLoopTool()]
result = agent.run("impossible task" )
assert result.iterations == 3
assert result.status == "max_iterations_reached"
Review Output Format ## Agent Review: [project-name]
### Summary
[1-2 sentence overview]
### Architecture Score
| Category | Score | Notes |
|----------|-------|-------|
| Folder Structure | X/5 | |
| Tool Design | X/5 | |
| Agent Loop | X/5 | |
| Memory Management | X/5 | |
| Error Handling | X/5 | |
| State Management | X/5 | |
| Observability | X/5 | |
| Testing | X/5 | |
| **Overall** | **X/5** | |
### Critical Issues
- [ ] [Issue] - Location: [file]
### Recommendations
- [ ] [Recommendation] - Priority: [High/Medium/Low]
### Strengths
- [What the implementation does well]
Anti-Patterns to Flag Anti-Pattern Problem Fix God Agent Single agent does everything Split by responsibility Infinite Loop No termination condition Add max iterations Silent Failures Errors swallowed Structured error handling Hardcoded Prompts Prompts in code Externalize to files No Observability Can't debug production Add structured logging Mutable State Race conditions, bugs Immutable state updates No Timeouts Hanging requests Configure all timeouts Missing Validation Invalid inputs accepted Schema validation
References 