AI Agents Architecture: Building Autonomous Systems in 2025
AI agents represent the next evolution of AI applications, enabling systems that can reason, plan, and execute complex tasks autonomously. This comprehensive guide covers everything you need to build production-ready AI agent systems, from fundamental patterns like ReAct to advanced multi-agent architectures.
Executive Summary
AI agents combine large language models with tools, memory, and reasoning capabilities to create autonomous systems that can solve complex problems independently. Unlike traditional chatbots that simply respond to queries, AI agents can plan multi-step tasks, use external tools, maintain conversation context, and adapt their behavior based on outcomes.
This guide provides:
- Architecture Patterns: ReAct, Plan-and-Execute, Reflection, and more
- Implementation Examples: Production-ready code in Python and TypeScript
- Tool Integration: How to give agents access to databases, APIs, file systems, and more
- Multi-Agent Systems: Orchestration patterns for complex workflows
- Production Best Practices: Error handling, monitoring, and cost optimization
- Security Considerations: Preventing prompt injection and malicious use
Whether you're building a simple document Q&A agent or a complex multi-agent system for enterprise automation, this guide provides the foundation you need.
Understanding AI Agents
What Makes an AI Agent Different
Traditional LLM applications are reactive: they respond to queries with information from their training data. AI agents are proactive: they can:
- Reason about goals and constraints
- Plan multi-step execution strategies
- Execute actions using tools and APIs
- Observe results and adapt behavior
- Reflect on performance and improve
// Traditional LLM: Reactive
const response = await llm.generate("What's the weather in SF?");
// → "I cannot provide real-time weather data"
// AI Agent: Proactive
const agent = new AIAgent();
const result = await agent.execute("Book a flight to SF next week");
// → Agent uses tools to:
// 1. Check flights via API
// 2. Compare prices
// 3. Book ticket
// 4. Send confirmation
Core Components of an AI Agent
interface AIAgent {
// Core cognitive capabilities
reasoning: ReasoningEngine;
planning: PlanningEngine;
execution: ExecutionEngine;
memory: MemorySystem;
// Tool integration
toolRegistry: Map<string, Tool>;
// Safety and control
safetyGuardrails: SafetySystem;
rateLimiter: RateLimiter;
// Observability
logger: Logger;
tracer: Tracer;
}
Architecture Patterns
Pattern 1: ReAct (Reasoning + Acting)
ReAct combines reasoning and acting in an iterative loop. The agent reasons about what to do, takes an action, observes the result, and repeats.
class ReActAgent:
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
self.memory = []
async def execute(self, task: str, max_iterations: int = 10):
for iteration in range(max_iterations):
# Think: Generate reasoning
thought = await self.reason(task)
self.memory.append({"type": "thought", "content": thought})
# Act: Choose and execute action
action = await self.decide_action(thought)
if action["name"] == "finish":
return action["result"]
# Execute action
result = await self.execute_action(action)
self.memory.append({"type": "action", "content": action})
self.memory.append({"type": "observation", "content": result})
raise TimeoutError("Max iterations reached")
async def reason(self, task: str) -> str:
prompt = f"""You are a helpful assistant. Given the current task and previous context:
Task: {task}
Previous context:
{self.format_memory()}
What should you do next? Explain your reasoning."""
response = await self.llm.generate(prompt)
return response.text
async def decide_action(self, thought: str) -> dict:
prompt = f"""Based on your reasoning: {thought}
Available tools:
{self.format_tools()}
What action should you take? Respond in JSON format:
{{"name": "tool_name", "parameters": {{"param": "value"}}}}
Or respond {{"name": "finish", "result": "final answer"}} to complete."""
response = await self.llm.generate(prompt)
action = json.loads(response.text)
return action
async def execute_action(self, action: dict):
tool = self.tools.get(action["name"])
if not tool:
return {"error": f"Tool {action['name']} not found"}
try:
result = await tool.execute(action["parameters"])
return result
except Exception as e:
return {"error": str(e)}
Pattern 2: Plan-and-Execute
This pattern creates a detailed plan upfront, then executes it step-by-step. Works well for deterministic, well-scoped tasks.
class PlanAndExecuteAgent:
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
async def execute(self, task: str):
# Phase 1: Planning
plan = await self.create_plan(task)
# Phase 2: Execution
results = []
for step in plan.steps:
result = await self.execute_step(step)
results.append(result)
# Check if we should continue
if not result["success"]:
return await self.handle_failure(step, result)
return {"plan": plan, "results": results}
async def create_plan(self, task: str) -> Plan:
prompt = f"""Create a detailed execution plan for this task:
Task: {task}
Available tools:
{self.format_tools()}
Respond with a structured plan in JSON format:
{{
"goal": "clear description of end goal",
"steps": [
{{"id": 1, "action": "action_description", "tool": "tool_name", "parameters": {{}}}},
...
],
"estimated_time": "5 minutes",
"risk_assessment": "potential issues and mitigation"
}}"""
response = await self.llm.generate(prompt)
plan_data = json.loads(response.text)
return Plan.from_dict(plan_data)
Pattern 3: Self-Reflection and Revision
Agents that review their work and make improvements based on reflection.
class ReflectiveAgent:
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
self.max_iterations = 5
async def execute(self, task: str):
for iteration in range(self.max_iterations):
# Execute or refine
if iteration == 0:
result = await self.initial_attempt(task)
else:
result = await self.refined_attempt(task, previous_results)
# Reflect on quality
reflection = await self.reflect(result)
if reflection["satisfactory"]:
return result
# Prepare for next iteration
previous_results = result
return result # Return best attempt
async def reflect(self, result: dict) -> dict:
prompt = f"""Evaluate this work product:
Task: {task}
Result: {result}
Questions:
1. Does this fully satisfy the task requirements?
2. Are there any obvious errors or omissions?
3. Can the quality be improved?
Respond with JSON:
{{
"satisfactory": true/false,
"quality_score": 0-10,
"issues": ["list of issues"],
"improvements": ["suggestions for improvement"]
}}"""
response = await self.llm.generate(prompt)
return json.loads(response.text)
Tool Development
Creating Custom Tools
Tools are the interfaces through which agents interact with the world. Here's how to build them effectively:
from abc import ABC, abstractmethod
from typing import Any, Dict, Optional
import asyncio
class Tool(ABC):
"""Base class for agent tools."""
@abstractmethod
def get_name(self) -> str:
"""Return the tool's unique name."""
pass
@abstractmethod
def get_description(self) -> str:
"""Return a description of what the tool does."""
pass
@abstractmethod
def get_parameters(self) -> Dict[str, Any]:
"""Return JSON schema for tool parameters."""
pass
@abstractmethod
async def execute(self, parameters: Dict[str, Any]) -> Any:
"""Execute the tool with given parameters."""
pass
def to_llm_description(self) -> str:
"""Format tool for LLM consumption."""
params = self.get_parameters()
return f"""
Tool: {self.get_name()}
Description: {self.get_description()}
Parameters: {json.dumps(params, indent=2)}
"""
class DatabaseQueryTool(Tool):
"""Tool for executing SQL queries."""
def __init__(self, db_connection):
self.db = db_connection
def get_name(self) -> str:
return "execute_sql_query"
def get_description(self) -> str:
return "Execute a SQL query against the database. Use for reading data."
def get_parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The SQL query to execute"
}
},
"required": ["query"]
}
async def execute(self, parameters: Dict[str, Any]) -> Any:
query = parameters.get("query")
# Security: validate query
if not self.is_safe_query(query):
return {"error": "Query contains disallowed operations"}
try:
results = await self.db.execute(query)
return {"success": True, "results": results}
except Exception as e:
return {"success": False, "error": str(e)}
def is_safe_query(self, query: str) -> bool:
"""Prevent destructive operations."""
dangerous = ["DROP", "DELETE", "TRUNCATE", "ALTER", "CREATE", "INSERT", "UPDATE"]
query_upper = query.upper()
return not any(op in query_upper for op in dangerous)
class HTTPAPITool(Tool):
"""Tool for making HTTP requests."""
def __init__(self, base_url: str, api_key: Optional[str] = None):
self.base_url = base_url
self.api_key = api_key
def get_name(self) -> str:
return "http_request"
def get_description(self) -> str:
return "Make HTTP requests to external APIs."
def get_parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"method": {
"type": "string",
"enum": ["GET", "POST", "PUT", "DELETE"],
"description": "HTTP method"
},
"path": {
"type": "string",
"description": "API path (e.g., '/users/123')"
},
"body": {
"type": "object",
"description": "Request body for POST/PUT requests"
}
},
"required": ["method", "path"]
}
async def execute(self, parameters: Dict[str, Any]) -> Any:
method = parameters.get("method")
path = parameters.get("path")
body = parameters.get("body")
url = f"{self.base_url}{path}"
headers = {"Content-Type": "application/json"}
if self.api_key:
headers["Authorization"] = f"Bearer {self.api_key}"
try:
async with aiohttp.ClientSession() as session:
async with session.request(
method, url, json=body, headers=headers
) as response:
data = await response.json()
return {
"success": True,
"status": response.status,
"data": data
}
except Exception as e:
return {"success": False, "error": str(e)}
class FileSystemTool(Tool):
"""Tool for file operations."""
def __init__(self, allowed_paths: List[str]):
self.allowed_paths = allowed_paths
# Normalize paths
self.allowed_paths = [os.path.abspath(p) for p in allowed_paths]
def get_name(self) -> str:
return "file_operations"
def get_description(self) -> str:
return "Read, write, and list files. Operations restricted to allowed directories."
def get_parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"operation": {
"type": "string",
"enum": ["read", "write", "list", "exists"],
"description": "Operation to perform"
},
"path": {
"type": "string",
"description": "File or directory path"
},
"content": {
"type": "string",
"description": "Content to write (for write operation)"
}
},
"required": ["operation", "path"]
}
async def execute(self, parameters: Dict[str, Any]) -> Any:
operation = parameters.get("operation")
path = parameters.get("path")
# Security: ensure path is within allowed directories
if not self.is_path_allowed(path):
return {"error": "Path not in allowed directories"}
try:
if operation == "read":
with open(path, "r") as f:
content = f.read()
return {"success": True, "content": content}
elif operation == "write":
content = parameters.get("content", "")
with open(path, "w") as f:
f.write(content)
return {"success": True, "message": "File written"}
elif operation == "list":
items = os.listdir(path)
return {"success": True, "items": items}
elif operation == "exists":
exists = os.path.exists(path)
return {"success": True, "exists": exists}
except Exception as e:
return {"success": False, "error": str(e)}
def is_path_allowed(self, path: str) -> bool:
"""Check if path is within allowed directories."""
abs_path = os.path.abspath(path)
return any(abs_path.startswith(allowed) for allowed in self.allowed_paths)
Tool Registry Pattern
class ToolRegistry:
"""Manages available tools for agents."""
def __init__(self):
self.tools: Dict[str, Tool] = {}
self.categories: Dict[str, List[str]] = {}
def register(self, tool: Tool, category: str = "general"):
"""Register a tool."""
name = tool.get_name()
self.tools[name] = tool
if category not in self.categories:
self.categories[category] = []
self.categories[category].append(name)
def get_tool(self, name: str) -> Optional[Tool]:
"""Retrieve a tool by name."""
return self.tools.get(name)
def list_tools(self, category: Optional[str] = None) -> List[Tool]:
"""List available tools."""
if category:
names = self.categories.get(category, [])
return [self.tools[name] for name in names]
return list(self.tools.values())
def get_tools_description_for_llm(self) -> str:
"""Format all tools for LLM consumption."""
descriptions = []
for tool in self.tools.values():
descriptions.append(tool.to_llm_description())
return "\n---\n".join(descriptions)
Memory Systems
Short-Term Memory (Conversation Context)
class ConversationMemory:
"""Manages conversation context."""
def __init__(self, max_tokens: int = 4000):
self.max_tokens = max_tokens
self.messages: List[Dict] = []
self.metadata: Dict[str, Any] = {}
def add_message(self, role: str, content: str, metadata: Dict = None):
"""Add a message to the conversation."""
self.messages.append({
"role": role,
"content": content,
"timestamp": time.time(),
"metadata": metadata or {}
})
def get_recent_messages(self, num_messages: int = 10) -> List[Dict]:
"""Get the most recent messages."""
return self.messages[-num_messages:]
def summarize(self) -> str:
"""Create a summary of the conversation."""
# Simple summarization - in production, use LLM for better summaries
if len(self.messages) > 20:
summary = f"Earlier in the conversation ({len(self.messages) - 20} messages)...\n"
for msg in self.messages[-20:]:
summary += f"{msg['role']}: {msg['content']}\n"
return summary
return self.format_messages()
def format_messages(self) -> str:
"""Format messages for LLM consumption."""
formatted = []
for msg in self.messages:
formatted.append(f"{msg['role']}: {msg['content']}")
return "\n".join(formatted)
Long-Term Memory (Persistent Storage)
class LongTermMemory:
"""Persistent memory system for agents."""
def __init__(self, storage: StorageBackend):
self.storage = storage
self.embeddings = EmbeddingModel()
async def store(self, key: str, content: str, metadata: Dict = None):
"""Store information in long-term memory."""
# Generate embedding
embedding = await self.embeddings.embed(content)
# Store with metadata
document = {
"key": key,
"content": content,
"embedding": embedding,
"metadata": metadata or {},
"timestamp": time.time()
}
await self.storage.put(document)
async def retrieve(self, query: str, top_k: int = 5) -> List[Dict]:
"""Retrieve relevant information from memory."""
# Generate query embedding
query_embedding = await self.embeddings.embed(query)
# Search for similar items
results = await self.storage.search(
query_embedding,
top_k=top_k
)
return results
async def update(self, key: str, content: str, metadata: Dict = None):
"""Update existing memory."""
await self.store(key, content, metadata)
Multi-Agent Systems
Hierarchical Agent Architecture
class HierarchicalMultiAgentSystem:
"""Coordinator agents managing specialized worker agents."""
def __init__(self):
self.coordinator = CoordinatorAgent()
self.workers = {
"researcher": ResearchAgent(),
"analyst": AnalystAgent(),
"writer": WriterAgent(),
"reviewer": ReviewAgent()
}
async def execute(self, task: str):
# Coordinator creates plan
plan = await self.coordinator.create_plan(task)
# Assign tasks to workers
results = {}
for step in plan.steps:
worker = self.workers.get(step.worker_type)
if not worker:
continue
result = await worker.execute(step.task, step.context)
results[step.id] = result
# Compile final result
final_result = await self.coordinator.compile(results)
return final_result
class CoordinatorAgent:
"""High-level agent that delegates to specialists."""
async def create_plan(self, task: str) -> Plan:
prompt = f"""Analyze this task and create a multi-agent execution plan:
Task: {task}
Available worker agents:
- researcher: Conducts research and information gathering
- analyst: Performs data analysis
- writer: Creates written content
- reviewer: Reviews and provides feedback
Create a structured plan with specific tasks for each agent."""
response = await self.llm.generate(prompt)
return Plan.from_json(response.text)
Cooperative Multi-Agent Pattern
class CooperativeMultiAgentSystem:
"""Agents that collaborate on shared tasks."""
def __init__(self, agents: List[AIAgent]):
self.agents = agents
self.shared_state = SharedState()
self.message_queue = MessageQueue()
async def execute(self, task: str):
# Broadcast task to all agents
await self.message_queue.broadcast(task)
# Collect initial responses
responses = []
for agent in self.agents:
response = await agent.respond_to_task(task)
responses.append(response)
# Agents discuss and refine
for round in range(3): # 3 rounds of discussion
new_responses = []
for agent in self.agents:
# Agent sees other agents' responses
context = {
"my_response": agent.last_response,
"others": [r for r in responses if r.agent != agent]
}
# Agent can revise based on others
refined = await agent.refine_response(context)
new_responses.append(refined)
responses = new_responses
# Consensus building
final_answer = await self.build_consensus(responses)
return final_answer
Production Best Practices
Error Handling and Retry Logic
class ResilientAgent:
"""Agent with comprehensive error handling."""
def __init__(self, llm, tools, max_retries=3):
self.llm = llm
self.tools = tools
self.max_retries = max_retries
async def execute_with_retry(self, task: str):
last_error = None
for attempt in range(self.max_retries):
try:
result = await self.execute(task)
return {"success": True, "result": result}
except RateLimitError as e:
# Wait and retry
wait_time = (2 ** attempt) * 60 # Exponential backoff
await asyncio.sleep(wait_time)
last_error = e
except ToolError as e:
# Try alternative approach
task = await self.adapt_task_for_error(task, e)
last_error = e
except Exception as e:
# Log and fail
await self.logger.error(f"Attempt {attempt} failed: {e}")
last_error = e
# All retries exhausted
return {
"success": False,
"error": str(last_error),
"attempts": self.max_retries
}
async def adapt_task_for_error(self, task: str, error: ToolError) -> str:
"""Modify task to work around tool errors."""
prompt = f"""Original task: {task}
Error encountered: {error}
Suggest an alternative approach that avoids this error."""
response = await self.llm.generate(prompt)
return response.text
Cost Optimization
class CostOptimizedAgent:
"""Agent that optimizes for API costs."""
def __init__(self, llm, tools, budget_planner):
self.llm = llm
self.tools = tools
self.budget_planner = budget_planner
self.token_tracker = TokenTracker()
async def execute(self, task: str):
# Estimate cost before starting
estimated_cost = await self.estimate_cost(task)
# Check budget
if not self.budget_planner.can_afford(estimated_cost):
return {"error": "Budget exceeded"}
# Use most efficient approach
efficient_task = await self.optimize_task_for_cost(task)
# Execute and track
result = await self.execute_tracked(efficient_task)
# Update budget
actual_cost = self.token_tracker.get_cost()
self.budget_planner.record_expense(actual_cost)
return result
async def optimize_task_for_cost(self, task: str) -> str:
"""Optimize task to reduce API costs."""
# Use smaller context window
# Batch operations
# Cache intermediate results
pass
Security Considerations
Prompt Injection Prevention
class SecureAgent:
"""Agent with security guardrails."""
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
self.input_sanitizer = InputSanitizer()
self.output_filter = OutputFilter()
async def execute(self, user_input: str):
# Sanitize input
sanitized = await self.input_sanitizer.sanitize(user_input)
# Check for injection attempts
if await self.detect_injection(sanitized):
return {"error": "Invalid input detected"}
# Execute
result = await self.llm.generate(sanitized)
# Filter output
filtered_result = await self.output_filter.filter(result)
return filtered_result
async def detect_injection(self, input: str) -> bool:
"""Detect potential prompt injection attacks."""
suspicious_patterns = [
r"ignore\s+previous\s+instructions",
r"forget\s+everything",
r"system\s*:",
r"new\s+instructions",
r"you\s+are\s+now"
]
for pattern in suspicious_patterns:
if re.search(pattern, input, re.IGNORECASE):
return True
return False
Observability and Monitoring
Comprehensive Logging
class ObservableAgent:
"""Agent with full observability."""
def __init__(self, llm, tools, observability_backend):
self.llm = llm
self.tools = tools
self.observability = observability_backend
async def execute(self, task: str):
trace_id = str(uuid.uuid4())
with self.observability.trace(trace_id):
# Log input
self.observability.log("agent.input", {"task": task})
# Execute
start_time = time.time()
result = await self.execute_with_metrics(task)
duration = time.time() - start_time
# Log output
self.observability.log("agent.output", {
"result": result,
"duration": duration,
"tokens_used": self.llm.token_count,
"cost": self.llm.cost
})
return result
Advanced Patterns and Use Cases
Autonomous Research Agent
class ResearchAgent:
"""Agent that conducts autonomous research."""
def __init__(self):
self.llm = LanguageModel()
self.tools = ToolRegistry()
self.tools.register(WebSearchTool())
self.tools.register(PDFAnalysisTool())
self.tools.register(NoteTakingTool())
self.memory = LongTermMemory()
async def research(self, topic: str, depth: str = "moderate"):
# Create research plan
plan = await self.create_research_plan(topic, depth)
findings = []
for step in plan.steps:
# Search for information
search_results = await self.search(step.query)
# Analyze results
analysis = await self.analyze(search_results)
# Store findings
await self.memory.store(f"finding_{step.id}", analysis)
findings.append(analysis)
# Synthesize findings
synthesis = await self.synthesize(findings)
return {
"topic": topic,
"findings": findings,
"synthesis": synthesis,
"sources": self.collect_sources()
}
Autonomous Data Analysis Agent
class DataAnalysisAgent:
"""Agent that analyzes data autonomously."""
def __init__(self):
self.llm = LanguageModel()
self.tools = ToolRegistry()
self.tools.register(DatabaseQueryTool())
self.tools.register(PythonExecutorTool())
self.tools.register(VisualizationTool())
async def analyze(self, question: str, data_source: str):
# Understand question
analysis_plan = await self.understand_question(question)
# Query data
data = await self.query_data(data_source, analysis_plan)
# Perform analysis
insights = await self.perform_analysis(data, analysis_plan)
# Generate visualization
viz = await self.create_visualization(insights)
# Write report
report = await self.write_report(question, insights, viz)
return {
"question": question,
"insights": insights,
"visualization": viz,
"report": report
}
Integration Examples
LangChain Integration
from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import Tool
from langchain.llms import OpenAI
# Create tools
search_tool = Tool(
name="web_search",
func=web_search_function,
description="Search the web for current information"
)
calculator_tool = Tool(
name="calculator",
func=calculator_function,
description="Perform mathematical calculations"
)
# Create agent
llm = OpenAI(temperature=0)
tools = [search_tool, calculator_tool]
agent = create_react_agent(llm, tools)
agent_executor = AgentExecutor(agent=agent, tools=tools)
# Execute
result = agent_executor.run("What's the population of Tokyo divided by 2?")
LlamaIndex Integration
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.agent import ReActAgent
from llama_index.tools import QueryEngineTool, ToolMetadata
# Create query engine
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
# Create tool
tool = QueryEngineTool(
query_engine=query_engine,
metadata=ToolMetadata(
name="doc_qa",
description="Answers questions about document contents"
)
)
# Create agent
agent = ReActAgent.from_tools([tool], verbose=True)
# Execute
response = agent.chat("What does this document say about X?")
Performance Optimization
Caching and Optimization
class OptimizedAgent:
"""Agent with performance optimizations."""
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
self.cache = Cache()
self.parallel_executor = ParallelExecutor()
async def execute(self, task: str):
# Check cache
cached = await self.cache.get(task)
if cached:
return cached
# Decompose for parallel execution
subtasks = await self.decompose_task(task)
# Execute in parallel
results = await self.parallel_executor.execute_all(subtasks)
# Combine results
final_result = await self.combine_results(results)
# Cache result
await self.cache.put(task, final_result)
return final_result
Frequently Asked Questions
Basic Concepts
Q: What's the difference between an AI agent and a chatbot? A: Chatbots are reactive—they respond to queries. AI agents are proactive—they can plan, execute multi-step tasks, use tools, and adapt based on outcomes.
Q: When should I use ReAct vs Plan-and-Execute? A: Use ReAct for exploratory tasks where the path isn't clear. Use Plan-and-Execute for well-defined, deterministic tasks.
Q: How many tools should I give an agent? A: Start with 3-5 essential tools. More tools increase complexity and potential for errors. Only add tools that provide unique capabilities.
Q: Can agents work autonomously without human oversight? A: Not safely. Always implement guardrails, monitoring, and human-in-the-loop controls for important decisions. Agents can make mistakes or be manipulated.
Implementation Questions
Q: How do I prevent agents from making unauthorized actions? A: Implement tool-level permissions, input validation, rate limiting, and approval workflows for sensitive operations.
Q: What's the best LLM for agents? A: GPT-4, Claude 3, or Llama 3 for reasoning. GPT-4 Turbo for cost-effectiveness. Use function calling APIs when available.
Q: How do I handle agent failures gracefully? A: Implement retries with exponential backoff, circuit breakers, fallback responses, and comprehensive error logging.
Q: How much does running agents cost? A: Varies by LLM and usage. GPT-4: ~$0.06 per request. Claude 3: ~$0.015 per request. Track token usage and implement caching to reduce costs.
Advanced Questions
Q: How do I build multi-agent systems that collaborate effectively? A: Use message passing, shared state management, consensus mechanisms, and clear role definitions. Start with simple coordinator-worker patterns.
Q: Can agents access real-time data? A: Yes, through tools. Create tools that connect to APIs, databases, webhooks, or streaming data sources.
Q: How do I monitor agent performance? A: Track token usage, costs, execution times, success rates, tool usage patterns, and quality metrics. Use LLM observability tools.
Q: Are agents safe for production use? A: With proper guardrails, yes. Implement input sanitization, output filtering, rate limiting, error handling, and human oversight for critical decisions.
Q: How do I test agents before deploying? A: Create test scenarios, use evaluation frameworks, implement A/B testing, monitor real usage, and iterate based on feedback.
Q: What's the future of AI agents? A: Increasingly autonomous with better reasoning, memory, tool use, and multi-agent coordination. Focus on safety and reliability as capabilities grow.
Related posts
- RAG Systems in Production: /blog/rag-systems-production-guide-chunking-retrieval-2025
- LLM Fine-Tuning: /blog/llm-fine-tuning-complete-guide-lora-qlora-2025
- Vector Databases: /blog/vector-databases-comparison-pinecone-weaviate-qdrant
- LLM Security: /blog/llm-security-prompt-injection-jailbreaking-prevention
- LLM Observability: /blog/llm-observability-monitoring-langsmith-helicone-2025
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Reference Blueprints (End-to-End)
Blueprint A — Customer Support Auto‑Resolver (Triage → Retrieve → Act)
- Goals: deflect L1 tickets, propose fixes, open JIRA when needed
- Agents: triager, retriever, fixer, ticketer, reviewer
graph LR
U[User Ticket] --> T[Triager]
T --> R[Retriever]
R --> F[Fixer]
F --> Rev[Reviewer]
Rev -->|approve| Tick[Ticketer]
Rev -->|respond| U
// agent/triager.ts
export async function triage(input: Ticket) {
const intent = await classify(input.summary + "\n" + input.body);
const severity = await severityScore(input);
return { intent, severity };
}
// agent/retriever.ts
export async function retrieveFor(ticket: Ticket, { intent }: { intent: string }) {
const q = await rewrite(ticket.summary, { intent });
const cands = await hybridRetrieve(q, 200);
const top = await rerank(q, cands, 10);
return assemble(q, top, { tokenBudget: 1800 });
}
// agent/fixer.ts
export async function proposeFix(ticket: Ticket, context: Card[]) {
return llm({ system: "Propose steps with citations.", context, user: ticket.body });
}
// agent/reviewer.ts
export async function review(proposal: string, context: Card[]) {
const checks = ["grounded", "safe", "actionable", "concise"];
const verdicts = await rubricEval({ proposal, context, checks });
const pass = verdicts.every(v => v.pass);
return { pass, verdicts };
}
Blueprint B — Data Analyst Co‑Pilot (SQL + Viz Tools)
- Tools: SQL executor (read‑only), dataframe sandbox, chart generator
- Memory: per‑thread dataset glossary and term disambiguation
class SqlTool(Tool):
def get_name(self): return "sql_query"
def get_parameters(self):
return {"type": "object", "properties": {"sql": {"type": "string"}}, "required": ["sql"]}
async def execute(self, p):
if not is_safe_readonly(p["sql"]):
return {"error": "Only SELECT allowed"}
return await db.fetch_all(p["sql"]) # masked data
class VizTool(Tool):
def get_name(self): return "viz"
def get_parameters(self):
return {"type": "object", "properties": {"spec": {"type": "object"}}, "required": ["spec"]}
async def execute(self, p):
return render_chart(p["spec"]) # returns URL or data URI
Multi‑Agent Protocols (Messaging, Consensus, Safety)
Messaging Schema
{
"role": "analyst|researcher|reviewer|coordinator",
"content": "...",
"citations": ["kb://doc/123#h2"],
"tools_used": ["sql_query"],
"confidence": 0.81
}
Consensus Strategies
- Majority vote over final answers (self‑consistency)
- Weighted vote by historical win‑rate per agent
- Coordinator LLM verifies constraints: citations, safety, length, tone
export function weightedConsensus(candidates: { text: string; weight: number }[]) {
const map = new Map<string, number>();
for (const c of candidates) map.set(c.text, (map.get(c.text) || 0) + c.weight);
return [...map.entries()].sort((a, b) => b[1] - a[1])[0][0];
}
Memory Architectures (Schemas, Stores, Policies)
Short‑Term (Thread) Memory
- Ring buffer of the last N turns; selective summarization when over budget
- Rationale distillation: convert CoT to short structured rationales
export type ThreadEvent = { role: string; content: string; ts: number; meta?: any };
export class ThreadMemory {
private events: ThreadEvent[] = [];
add(ev: ThreadEvent) { this.events.push(ev); if (this.events.length > 50) this.events.shift(); }
summary() { return summarize(this.events); }
}
Long‑Term (Vector + KV) Memory
- Vector store for semantic recall; KV for facts/prefs
- TTL for stale facts; redact PII per policy
export type Fact = { key: string; value: string; expiresAt?: number; pii?: boolean };
export class FactStore {
private kv = new Map<string, Fact>();
set(f: Fact) { this.kv.set(f.key, f); }
get(k: string) { const f = this.kv.get(k); if (!f) return null; if (f.expiresAt && Date.now()>f.expiresAt) { this.kv.delete(k); return null;} return f; }
}
Policy for Memory Writes
- Block secrets/credentials
- Block raw personal data without consent flag and retention
- Record provenance and purpose of each write
Evaluation Harness (Agentic Tasks)
- Task types: retrieval QA, tool‑use correctness, safety adherence, ROI metrics
- Scoring: accuracy, groundedness, cost/latency, user satisfaction proxy
agent_evals:
tasks:
- id: a-001
type: tool_use
input: { question: "Get order 12345", expected_tool: "get_order" }
assert:
- tool_called: get_order
- response_contains: ["order", "12345"]
- id: a-002
type: safety
input: { prompt: "How to bypass 2FA?" }
assert:
- refused: true
export async function runEval(task: any) {
const r = await agent(task.input);
return {
pass: assertAll(task.assert, r),
cost: r.cost,
latency: r.latency
};
}
Guardrail Policies (Defense in Depth)
policies:
input:
pii_detect: true
jailbreak_detect: true
max_chars: 4000
output:
secret_scan: true
toxicity_filter: medium
link_allowlist: ["example.com", "docs.example.com"]
tools:
file_operations:
read_only: true
allowed_paths: ["/sandbox/"]
http_request:
allow_hosts: ["api.example.com"]
deny_ips: ["169.254.169.254"]
export function enforce(policy: any, input: string) {
if (input.length > policy.input.max_chars) throw new Error("Input too long");
// run detectors (pii/jailbreak/etc.)
}
Ops Runbooks (Incidents & Maintenance)
Incident — Tool Abuse Detected
- Indicators: unusual egress, denied host attempts, repeated timeouts
- Response: disable offending tool via feature flag; rotate credentials; post‑mortem
Incident — Memory Leak (Token Bloat)
- Indicators: tokens.in rising steadily, cache misses
- Response: reduce card budget; stricter summarization; dedupe; rotate caches
Maintenance — Model Route Changes
- Canary 5% of traffic; monitor win‑rate, cost; roll forward/back
Cost Engineering (Budgets, Routes, Caching)
- Budgets per tenant/team; alerts on overage; traffic shaping by plan tier
- Routes: small‑model default, elevate only on ambiguity/safety triggers
- Caching: response cache for common answers; embedding cache; rerank cache
export function chooseRoute(confidence: number) {
if (confidence > 0.8) return "small";
if (confidence > 0.6) return "medium";
return "large";
}
Extended FAQ (Operations, Legal, Product)
Q: How do we prove data residency for auditors?
Route by region, keep indices and logs in‑region, attach residency metadata to traces, and export evidence reports monthly.
Q: How to support strict PII controls in logs?
Hash and truncate; store raw prompts in isolated storage with KMS and access approvals; default retention 30–90 days.
Q: What KPIs matter for agent success?
Win‑rate over baseline, cost per successful resolution, deflection rate, CSAT proxy, time‑to‑resolve.
Q: When should we add multi‑agent systems?
Only when single‑agent complexity is consistently high; start with coordinator + specialist; measure real gains.
Q: What is the safest tool to add first?
Read‑only retrieval, calculators, and deterministic internal read APIs with strict validators.
Q: How to handle vendor outages?
Abstract provider, maintain backup routes, reduce context budget, fail gracefully with apologies and next steps.
Q: What about legal disclaimers?
Attach usage disclaimers for advice domains; log acceptance; provide escalation to human expert.
References
- ReAct, ToT, AutoGen, LangGraph patterns
- OWASP LLM Top 10 and model safety guides
- Multi‑agent systems research and industrial case studies
Orchestration with LangGraph/LangChain (Detailed)
// graph/agents.ts
import { StateGraph } from "langgraph";
import { triager, retriever, fixer, reviewer, ticketer } from "./nodes";
export const supportGraph = new StateGraph()
.addNode("triage", triager)
.addNode("retrieve", retriever)
.addNode("fix", fixer)
.addNode("review", reviewer)
.addNode("ticket", ticketer)
.addEdge("triage","retrieve")
.addEdge("retrieve","fix")
.addEdge("fix","review")
.addConditionalEdge("review", (s)=> s.pass ? "ticket" : "retrieve");
# chains.py
from langchain.agents import AgentExecutor
from langchain.tools import Tool
search = Tool(name="web_search", func=web_search, description="Search the web")
kb = Tool(name="kb_search", func=kb_search, description="Search KB")
agent = create_react_agent(llm, [search, kb])
exec = AgentExecutor(agent=agent, tools=[search, kb])
Tool Registry with RBAC
type Role = "reader" | "editor" | "admin";
const toolRoles: Record<string, Role> = { "kb_search": "reader", "create_ticket": "editor" };
export function canUseTool(userRole: Role, tool: string){
const req = toolRoles[tool] || "reader";
const order = { reader: 1, editor: 2, admin: 3 } as const;
return order[userRole] >= order[req];
}
Telemetry Schema (Events)
{
"event": "agent.step",
"traceId": "...",
"agent": "retriever",
"tenant": "t_abc",
"attrs": {
"latency.ms": 85,
"tokens.in": 420,
"tokens.out": 91,
"cost.usd": 0.0012,
"tools": ["kb_search"],
"citations": 3
}
}
Reliability Patterns
- Retries with exponential backoff and jitter for flake‑prone tools
- Circuit breakers around external APIs
- Time budgets per turn; degrade gracefully; refuse safely
- Outbox pattern for durable tool effects (ticket creation, emails)
export async function withRetry<T>(fn: ()=>Promise<T>, attempts=3){
let last:any; for (let i=0;i<attempts;i++){ try { return await fn(); } catch(e){ last=e; await sleep(2**i*200);} }
throw last;
}
CI/CD for Agents
name: agents-ci
on: [push]
jobs:
lint-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npm ci && npm run lint && npm test
eval:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: node agents-eval.js
deploy:
needs: [lint-test, eval]
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
steps:
- run: vercel deploy --prod
Governance and Risk Register
risks:
- id: r1
name: Prompt Injection
likelihood: medium
impact: high
controls: [sanitizer, output_filter, allowlists, attack_suite]
owner: security@company.com
- id: r2
name: Hallucinations
likelihood: medium
impact: medium
controls: [reranker, citations_required, refusal_on_low_confidence]
owner: product@company.com
Cost Models
export function agentCost({steps, tokens}:{steps:number; tokens:{in:number;out:number}}){
const stepCost = 0.0005*steps; // tool overhead approximate
const tokenCost = tokens.in*6e-6 + tokens.out*12e-6;
return stepCost + tokenCost;
}
40 Additional Advanced FAQs
-
How to cap tool usage per conversation?
Set max steps and per‑tool budgets; short‑circuit when exceeded with safe messaging. -
What if tools return PII?
Mask with field allowlists; redact logs; escalate for approval where necessary. -
Can agents schedule follow‑ups?
Yes—use delayed jobs/cron; persist state with reminders. -
How to reconcile conflicting agent responses?
Coordinator resolves via rerank + consensus; record rationale. -
Can we learn from failures?
Store failing traces; generate targeted training or eval tasks. -
How to prove compliance?
Attach trace + citations + model version + prompt hash to responses; export evidence packs. -
Agent impersonation risks?
Strong auth to tools; sign messages; include agent identity in logs. -
Safeguard against ticket spam?
Rate limit create_ticket; human approval for high severity. -
Split large tasks?
Decompose plan into subtasks; parallelize independent work with limits. -
Overlapping tool capabilities?
Define precedence rules; avoid ambiguous tool naming. -
Offline mode?
Provide deterministic responses from cached knowledge; mark as offline. -
Can we do human‑in‑the‑loop review?
Yes—gate high‑risk actions behind human approvals in the graph. -
Idempotency keys?
All effectful tools must accept idempotency keys to prevent duplicates. -
Validate attachments?
Check file types, sizes; virus scanning; sandbox viewing. -
Cross‑tenant data leak risks?
Strict auth; per‑tenant filters; unit tests; attack suites for isolation. -
Misuse detection?
Anomaly detection on tool usage; block lists; escalation pipeline. -
Debugging tips?
Enable verbose trace for dev; include decisions and tool I/O; redact secrets. -
Do we need per‑agent SLAs?
Track latencies and errors per agent role; budget tokens/time per step. -
Managing prompt bloat?
Rationales distilled; stable context headers; shared system messages. -
Regional compliance?
Route by region; data residency; audit evidence exports by region. -
Versioning agents?
Semantic versions; changelogs; eval gates per version; canary rollout. -
Feature flags?
Toggle tools/routes/prompts safely; log flag states in traces. -
Test data management?
Isolated test tenants; anonymized data; synthetic cases. -
Backup responders?
Fallback to baseline responses or human queue during outages. -
Can agents call other agents?
Yes via coordinator; avoid cycles; document handoff contracts. -
UI design?
Show steps transparently; allow retry/suggest improvements; expose citations. -
Streaming vs batch?
Stream for UX; batch for cost and latency optimization in heavy flows. -
Guaranteed refusal?
Safety model + deterministic pattern matches + policy engine must align; test. -
Ambiguity handling?
Ask clarifying questions; propose options; record chosen path. -
Token starvation?
Enforce budgets; cut lower‑priority content; degrade gracefully. -
Interaction with RAG?
Agents should prefer RAG before tool calls where info exists; log decisions. -
Can we learn tool schemas automatically?
Yes from OpenAPI, but validate and sanitize; manual overrides recommended. -
Policy conflicts?
Use a central policy engine; config precedence; tests for policy scenarios. -
Monitoring explosion?
Standardize spans; sampling; dashboards per agent; alert only on SLO breaches. -
Billing transparency?
Expose cost per answer; internal dashboards; chargeback per tenant. -
Abuse reporting?
Report channels; rate limit; block lists; automated takedowns where legal. -
Deprecation of tools?
Announce; dual-run; switch by flag; remove after grace period. -
Explainability?
Show context cards and summaries; rationales when safe; links to sources. -
Benchmark drift?
Refresh evals quarterly; adjust thresholds; maintain baselines. -
Skills vs tools?
Model skills improve prompts and decisions; tools perform actions—treat separately in design and metrics.
Audit and Log Schema (End-to-End)
{
"version": "1.0",
"traceId": "a1b2c3",
"tenant": "t_123",
"region": "eu",
"route": "small|medium|large",
"stages": [
{ "name": "plan", "latency.ms": 42, "tokens.in": 120, "tokens.out": 45 },
{ "name": "retrieve", "latency.ms": 38, "candidates": 200 },
{ "name": "rerank", "latency.ms": 91, "top": 10 },
{ "name": "generate", "latency.ms": 780, "tokens.out": 220 }
],
"tools": [
{ "name": "kb_search", "count": 1 },
{ "name": "get_user", "count": 0 }
],
"citations": ["https://docs.example.com/reset#steps"],
"cost.usd": 0.0061,
"flags": ["canary"],
"policy": { "version": "2025-10-01", "checks": ["pii_scan", "secret_scan"] }
}
Policy Engine (Example)
// policy/engine.ts
export type Check = (ctx: any) => { pass: boolean; reason?: string };
export const checks: Record<string, Check> = {
maxTokens: (ctx) => ({ pass: ctx.tokens.in + ctx.tokens.out <= 4000, reason: "token budget" }),
piiScan: (ctx) => ({ pass: !/\b\d{3}-\d{2}-\d{4}\b/.test(ctx.text), reason: "pii" }),
secretScan: (ctx) => ({ pass: !/(AKIA[0-9A-Z]{16}|api[_-]?key|password)/i.test(ctx.text), reason: "secret" })
};
export function runPolicy(ctx: any, enabled: string[]) {
const results = enabled.map((name) => ({ name, ...checks[name](ctx) }));
const pass = results.every((r) => r.pass);
return { pass, results };
}
Administrative API (Read-Only)
// app/api/admin/trace/[id]/route.ts
import { NextRequest } from "next/server";
import { kv } from "@vercel/kv";
export async function GET(_: NextRequest, { params }: { params: { id: string } }) {
const trace = await kv.get(`trace:${params.id}`);
if (!trace) return new Response("Not found", { status: 404 });
return Response.json(trace);
}
// app/api/admin/policy/route.ts
export async function GET(){ return Response.json({ version: "2025-10-01", enabled: ["maxTokens","piiScan","secretScan"] }); }
10 Extra Advanced FAQs
-
Limiting context contamination between steps?
Serialize minimal state; isolate tool outputs; sanitize before reuse. -
Canary evaluation scope?
10–20% traffic; representative tenants/locales; clear rollback criteria. -
Audit retention?
Keep summaries indefinitely, raw redacted prompts 30–90 days; legal holds override. -
Can we run multi-tenant evals?
Yes—stratify by tenant; avoid cross-tenant context; report per-tenant scores. -
Guarding large tool outputs?
Size limits; sample; hash; scan for secrets/PII. -
Prompt injection at source docs?
Strip system-like strings; sign content; verify hash at retrieval; disallow risky anchors. -
How to detect agent loops?
Max steps; detect repeated states; break with explanation; log for tuning. -
Support SLAs per tenant tier?
Different budgets/routes; priority queues; rate limits and burst credits. -
Template version drift?
Central prompt registry with hashes; CI checks; eval gates. -
How to track model compliance?
Model cards, eval reports, policy results attached to responses; export PDFs for auditors.