AI agents represent a new generation of intelligent systems capable of autonomous decision-making and action. This article explores their architecture, design principles, and practical implementations based on large language models.
Basic Principles of AI Agent Architecture¶
AI agents represent the next evolutionary step from static chatbots to autonomous systems capable of complex decision-making and action. A properly designed agent combines several key components: reasoning engine, memory system, tool integration, and execution layer.
The basic architecture of a modern AI agent is based on the ReAct (Reasoning + Acting) pattern, where the agent progressively analyzes the situation, plans steps, and executes actions. This cycle repeats until the goal is achieved or resources are exhausted.
class AIAgent:
def __init__(self, llm_client, tools, memory):
self.llm = llm_client
self.tools = tools
self.memory = memory
self.max_iterations = 10
async def execute(self, task):
for iteration in range(self.max_iterations):
# Reasoning phase
context = self.memory.get_context()
response = await self.llm.generate(
prompt=f"Task: {task}\nContext: {context}\n"
f"Available tools: {list(self.tools.keys())}"
)
# Acting phase
if self.should_use_tool(response):
result = await self.execute_tool(response)
self.memory.add_step(response, result)
else:
return response
Memory Management¶
A critical element is the agent’s memory management. We distinguish three types of memory: working memory for current context, episodic memory for specific events, and semantic memory for general knowledge.
class AgentMemory:
def __init__(self, vector_store, max_working_memory=4096):
self.vector_store = vector_store
self.working_memory = []
self.max_tokens = max_working_memory
def add_step(self, thought, action, result):
step = {
"timestamp": datetime.now(),
"thought": thought,
"action": action,
"result": result,
"embedding": self.get_embedding(f"{thought} {result}")
}
# Store in long-term memory
self.vector_store.store(step)
# Add to working memory with size management
self.working_memory.append(step)
self.manage_working_memory_size()
def retrieve_relevant_memories(self, query, top_k=3):
query_embedding = self.get_embedding(query)
return self.vector_store.similarity_search(
query_embedding, top_k
)
Tool Integration and Function Calling¶
Modern AI agents use function calling to interact with external systems. The key is to define clear interfaces and error handling for each tool.
class ToolRegistry:
def __init__(self):
self.tools = {}
def register_tool(self, name, func, schema):
self.tools[name] = {
"function": func,
"schema": schema,
"retry_config": {"max_retries": 3, "backoff": 2}
}
async def execute_tool(self, tool_name, parameters):
if tool_name not in self.tools:
raise ValueError(f"Tool {tool_name} not found")
tool = self.tools[tool_name]
try:
# Validate parameters against schema
self.validate_parameters(parameters, tool["schema"])
# Execute with retry logic
return await self.execute_with_retry(
tool["function"],
parameters,
tool["retry_config"]
)
except Exception as e:
return {"error": str(e), "tool": tool_name}
# Example tool registration
@tools.register("web_search", {
"type": "function",
"parameters": {
"query": {"type": "string"},
"max_results": {"type": "integer", "default": 5}
}
})
async def web_search(query, max_results=5):
# Implementation
pass
Planning and Multi-step Execution¶
For more complex tasks, the agent needs planning capabilities. We implement hierarchical planning with the ability to adapt during execution.
class TaskPlanner:
def __init__(self, llm_client):
self.llm = llm_client
async def create_plan(self, goal, available_tools):
planning_prompt = f"""
Goal: {goal}
Available tools: {list(available_tools.keys())}
Create a step-by-step plan. Each step should:
1. Be atomic and executable
2. Specify required tool if needed
3. Include success criteria
Format as JSON array of steps.
"""
response = await self.llm.generate(planning_prompt)
return self.parse_plan(response)
async def adapt_plan(self, current_plan, executed_steps, error_info):
adaptation_prompt = f"""
Original plan: {current_plan}
Executed steps: {executed_steps}
Error encountered: {error_info}
Adapt the remaining plan to handle the error and achieve the goal.
"""
response = await self.llm.generate(adaptation_prompt)
return self.parse_plan(response)
Monitoring and Observability¶
In production deployment, monitoring agent performance and behavior is crucial. We implement comprehensive logging and metrics for debugging and optimization.
class AgentObserver:
def __init__(self, logger, metrics_client):
self.logger = logger
self.metrics = metrics_client
def log_agent_step(self, agent_id, step_type, details, duration):
log_entry = {
"agent_id": agent_id,
"step_type": step_type,
"details": details,
"duration_ms": duration,
"timestamp": datetime.now().isoformat()
}
self.logger.info("agent_step", extra=log_entry)
# Track metrics
self.metrics.histogram("agent.step.duration",
duration, tags={"type": step_type})
self.metrics.increment("agent.steps.total",
tags={"type": step_type})
def track_tool_usage(self, tool_name, success, error_type=None):
tags = {"tool": tool_name, "success": success}
if error_type:
tags["error_type"] = error_type
self.metrics.increment("agent.tool.calls", tags=tags)
def track_token_usage(self, prompt_tokens, completion_tokens):
self.metrics.gauge("agent.tokens.prompt", prompt_tokens)
self.metrics.gauge("agent.tokens.completion", completion_tokens)
Error Handling and Recovery¶
Robust agents must handle failures gracefully. We implement multi-level error recovery with fallback strategies.
class ErrorRecoveryManager:
def __init__(self, max_recovery_attempts=3):
self.max_attempts = max_recovery_attempts
self.recovery_strategies = {
"tool_error": self.handle_tool_error,
"llm_error": self.handle_llm_error,
"timeout_error": self.handle_timeout_error
}
async def handle_error(self, error, context):
error_type = self.classify_error(error)
if error_type in self.recovery_strategies:
return await self.recovery_strategies[error_type](
error, context
)
else:
# Generic fallback
return await self.generic_recovery(error, context)
async def handle_tool_error(self, error, context):
# Try alternative tool or simplified approach
if hasattr(error, 'tool_name'):
alternative_tools = self.find_alternative_tools(
error.tool_name, context
)
if alternative_tools:
return {"strategy": "retry_with_alternative",
"tools": alternative_tools}
return {"strategy": "simplify_task"}
Security and Rate Limiting¶
AI agent security requires a multi-layered approach including input sanitization, output validation, and resource management.
class AgentSecurityManager:
def __init__(self, rate_limiter, content_filter):
self.rate_limiter = rate_limiter
self.content_filter = content_filter
self.dangerous_patterns = [
r'rm\s+-rf',
r'DROP\s+TABLE',
r'eval\s*\(',
]
async def validate_input(self, user_input, user_id):
# Rate limiting
if not await self.rate_limiter.allow(user_id):
raise SecurityError("Rate limit exceeded")
# Content filtering
if self.content_filter.is_malicious(user_input):
raise SecurityError("Malicious content detected")
# Pattern matching for dangerous commands
for pattern in self.dangerous_patterns:
if re.search(pattern, user_input, re.IGNORECASE):
raise SecurityError(f"Dangerous pattern detected: {pattern}")
def sanitize_tool_parameters(self, tool_name, parameters):
# Tool-specific sanitization
if tool_name == "file_system":
parameters = self.sanitize_file_paths(parameters)
elif tool_name == "database":
parameters = self.sanitize_sql_inputs(parameters)
return parameters
Summary¶
Successful AI agent architecture combines modular design with robust mechanisms for memory management, tool integration, and error recovery. The key is finding the balance between agent autonomy and security measures. When implementing, focus on observability and gradual testing of individual components. Modern agents are not just chatbots with tools - they are sophisticated systems requiring careful architectural decisions and continuous monitoring in production.