Understanding AI Agents
AI Agents are autonomous systems that perceive their environment, make decisions, and take actions to achieve specific goals.
What is an AI Agent?
An AI Agent is a software system that:
- Perceives - Receives inputs from users, systems, or environment
- Reasons - Uses AI/LLM to understand and make decisions
- Acts - Takes actions to achieve goals (API calls, responses, tool usage)
- Learns - Adapts based on feedback and outcomes
code
Input → Perception → Reasoning → Action → Output
↑ ↓
└────────── Feedback Loop ───────────────┘
Agent Characteristics
Autonomy
Agents operate independently without continuous human intervention:
python
# Agent autonomously handles multi-step tasks
agent = CustomerSupportAgent()
result = agent.handle_customer_query(
"I need to return my order and get a refund"
)
# Agent will:
# 1. Verify order details
# 2. Check return policy
# 3. Initiate return process
# 4. Process refund
# 5. Send confirmation
Reactivity
Agents respond to changes in their environment:
typescript
agent.on('new_message', async (message) => {
const context = await agent.analyzeContext(message)
const response = await agent.generateResponse(context)
await agent.send(response)
})
Pro-activeness
Agents take initiative to achieve goals:
python
# Agent proactively identifies issues
if order.status == 'delayed' and days_delayed > 3:
agent.notify_customer(order)
agent.offer_compensation(order)
agent.escalate_to_support(order)
Social Ability
Agents communicate with other agents and humans:
typescript
// Multi-agent collaboration
const orderAgent = new OrderAgent()
const inventoryAgent = new InventoryAgent()
const shippingAgent = new ShippingAgent()
// Agents coordinate to fulfill order
const order = await orderAgent.create(orderData)
const stock = await inventoryAgent.reserve(order.items)
const shipment = await shippingAgent.schedule(order, stock)
Types of AI Agents
1. Simple Reflex Agents
Respond directly to inputs based on rules:
typescript
if (query.includes('order status')) {
return getOrderStatus(userId)
}
if (query.includes('cancel order')) {
return cancelOrder(orderId)
}
Use Cases: Simple chatbots, rule-based systems
2. Model-Based Agents
Maintain internal state and model of the world:
python
class CustomerSupportAgent:
def __init__(self):
self.conversation_history = []
self.customer_context = {}
def handle_query(self, query):
# Uses conversation history for context
self.conversation_history.append(query)
context = self.build_context()
response = self.llm.generate(query, context)
return response
Use Cases: Conversational AI, context-aware assistants
3. Goal-Based Agents
Work towards specific goals:
typescript
const agent = new GoalBasedAgent({
goal: 'resolve_customer_issue',
maxSteps: 10
})
await agent.run({
query: "My order hasn't arrived",
customerId: '123'
})
// Agent will try different actions until goal is achieved:
// 1. Check order status
// 2. Track shipment
// 3. Contact carrier
// 4. Offer solutions
// 5. Verify customer satisfaction
Use Cases: Task completion, problem-solving systems
4. Utility-Based Agents
Optimize for specific metrics or utility functions:
python
class UtilityAgent:
def select_action(self, state, possible_actions):
# Evaluate each action's expected utility
utilities = [
self.calculate_utility(action, state)
for action in possible_actions
]
# Select action with highest utility
return possible_actions[argmax(utilities)]
def calculate_utility(self, action, state):
return (
0.4 * customer_satisfaction +
0.3 * cost_efficiency +
0.3 * time_to_resolution
)
Use Cases: Optimization systems, resource allocation
5. Learning Agents
Improve performance over time:
typescript
class LearningAgent {
async handleQuery(query: string) {
const response = await this.generateResponse(query)
// Collect feedback
const feedback = await this.getFeedback(response)
// Learn from feedback
await this.updateModel(query, response, feedback)
return response
}
}
Use Cases: Personalization, adaptive systems
Agent Architectures
ReAct (Reasoning + Acting)
Interleaves reasoning and acting:
python
def react_agent(query):
while not task_complete:
# Thought: Reason about what to do
thought = llm.generate(f"Thought: {query}")
# Action: Decide and take action
action = llm.generate(f"Action: {thought}")
result = execute_action(action)
# Observation: Observe result
observation = f"Observation: {result}"
# Continue or conclude
if is_answer_complete(observation):
break
return final_answer
Chain of Thought (CoT)
Breaks down complex reasoning:
typescript
const result = await agent.run({
prompt: "Calculate the total cost",
strategy: 'chain-of-thought'
})
// Agent thinking process:
// Step 1: Identify all items → [item1, item2, item3]
// Step 2: Get price for each → [$10, $15, $20]
// Step 3: Sum prices → $45
// Step 4: Add tax (10%) → $49.50
// Final Answer: $49.50
Tree of Thoughts (ToT)
Explores multiple reasoning paths:
python
def tree_of_thoughts(problem):
# Generate multiple solution paths
paths = []
for thought in generate_thoughts(problem):
score = evaluate_thought(thought)
if score > threshold:
paths.append(explore_path(thought))
# Select best path
best_path = max(paths, key=lambda p: p.score)
return best_path.solution
Multi-Agent Systems
Multiple agents collaborate:
typescript
class MultiAgentSystem {
agents = {
coordinator: new CoordinatorAgent(),
researcher: new ResearchAgent(),
writer: new WriterAgent(),
editor: new EditorAgent()
}
async execute(task: string) {
// Coordinator breaks down task
const subtasks = await this.agents.coordinator.plan(task)
// Research agent gathers information
const research = await this.agents.researcher.gather(subtasks)
// Writer creates content
const draft = await this.agents.writer.create(research)
// Editor refines output
const final = await this.agents.editor.refine(draft)
return final
}
}
Agent Components
1. Perception Layer
Processes inputs and extracts information:
python
class PerceptionLayer:
def process_input(self, raw_input):
# Parse and understand input
intent = self.classify_intent(raw_input)
entities = self.extract_entities(raw_input)
sentiment = self.analyze_sentiment(raw_input)
return {
'text': raw_input,
'intent': intent,
'entities': entities,
'sentiment': sentiment
}
2. Memory
Stores and retrieves information:
typescript
class AgentMemory {
shortTerm: ConversationHistory
longTerm: VectorDatabase
async remember(key: string, value: any) {
// Store in short-term
this.shortTerm.add(key, value)
// Index in long-term
await this.longTerm.index(key, value)
}
async recall(query: string) {
// Check short-term first
const recent = this.shortTerm.get(query)
if (recent) return recent
// Search long-term memory
return await this.longTerm.search(query)
}
}
3. Reasoning Engine
Makes decisions:
python
class ReasoningEngine:
def decide(self, context, options):
# Analyze situation
analysis = self.analyze(context)
# Evaluate options
evaluations = [
self.evaluate(option, analysis)
for option in options
]
# Select best option
best_option = max(evaluations, key=lambda x: x.score)
return best_option.action
4. Action Executor
Performs actions:
typescript
class ActionExecutor {
tools = {
'search': new SearchTool(),
'calculate': new CalculatorTool(),
'database': new DatabaseTool(),
'api': new APITool()
}
async execute(action: Action) {
const tool = this.tools[action.tool]
try {
const result = await tool.run(action.params)
return { success: true, result }
} catch (error) {
return { success: false, error }
}
}
}
Monitoring Agents with AgenticAnts
Basic Agent Monitoring
typescript
const ants = new AgenticAnts({ apiKey: process.env.AGENTICANTS_API_KEY })
class MonitoredAgent {
async run(input: string) {
// Start trace
const trace = await ants.trace.create({
name: 'customer-support-agent',
input: input,
metadata: {
agentType: 'support',
version: '1.0.0'
}
})
try {
// Agent execution
const result = await this.process(input, trace)
// Complete trace
await trace.complete({
output: result,
metadata: {
success: true,
steps: this.steps.length
}
})
return result
} catch (error) {
await trace.error({ error: error.message })
throw error
}
}
}
Multi-Step Agent Monitoring
python
from agenticants import AgenticAnts
ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
class MultiStepAgent:
def run(self, query):
# Create parent trace
trace = ants.trace.create(name='multi-step-agent', input=query)
# Step 1: Classification
classification_span = trace.span('classify-intent')
intent = self.classify(query)
classification_span.end(output=intent)
# Step 2: Retrieval
retrieval_span = trace.span('retrieve-context')
context = self.retrieve(intent)
retrieval_span.end(documents=len(context))
# Step 3: Generation
generation_span = trace.span('generate-response')
response = self.generate(query, context)
generation_span.end(tokens=response.usage.total)
trace.complete(output=response.text)
return response.text
Best Practices
1. Clear Goals
Define specific, measurable goals for your agents
2. Robust Error Handling
Agents should gracefully handle errors and edge cases
3. Human-in-the-Loop
Include human oversight for critical decisions
4. Observability
Monitor agent behavior, decisions, and outcomes
5. Continuous Improvement
Learn from agent interactions and improve over time