AI Agent Development and Optimization Series (Part 2): Understanding Performance Challenges

In our previous article, you learned how to build a simple but effective AI agent that could handle basic queries efficiently. While that foundation serves us well, the real world often demands agents with expanded capabilities—agents that can handle complex, multi-step workflows and integrate with multiple tools and systems.

However, adding these capabilities comes with a cost. As agents grow more sophisticated, they face performance challenges that can dramatically impact user experience, operational costs, and system reliability. This article explores what happens when agents evolve from simple to complex, examining the performance bottlenecks that emerge and the strategies needed to address them.

By understanding these challenges early, you'll be better prepared to build scalable agent systems that maintain high performance as they grow in capability.
 

From Simple to Complex: The Evolution Challenge

Let's continue where we left off with our basic user lookup agent and see what happens when we add more functionality. Real-world customer service scenarios often require multiple data sources and complex decision chains.

Extending Our Agent with Multiple Tools

Starting with our simple agent that could only look up user information, let's add several customer service capabilities:

@tool
def get_user_info(user_id: str) -> str:
    """
    Get basic information about a user.

    Parameters:
    - user_id: The ID or name of the user to look up.

    Returns:
         A string containing the user's information.
    """
    # Simulate API call
    return f"User {user_id}: Active account, Premium tier"


@tool
def get_user_orders(user_id: str, tier: str) -> str:
    """
    Get recent orders for a user.

    Parameters:
    - user_id: The ID or name of the user to look up.
    - tier: The subscription tier of the user.

    Returns:
        A string containing the user's recent orders.
    """

    if tier == "Premium":
        return f"User {user_id} recent orders: Order #A001, Order #A002"


@tool
def get_order_details(order_id: str) -> str:
    """
    Get detailed information about a specific order.

    Parameters:
    - order_id: The ID of the order to look up.

    Returns:
        A string containing the order's details.
    """
    return f"Order {order_id}: $299.99, shipped to New York"


@tool
def check_shipping_status(order_id: str, destination: str) -> str:
    """
    Check shipping status of an order.

    Parameters:
    - order_id: The ID of the order to check.
    - destination: The destination address for the order.

    Returns:
        A string containing the order's shipping status.
    """
    return f"Order {order_id}: In transit, expected delivery tomorrow"


# Enhanced agent with multiple tools
enhanced_agent = create_react_agent(
   model=model,
   tools=[get_user_info, get_user_orders, get_order_details, check_shipping_status],
   prompt="You are a customer service assistant. Help users with account and order inquiries."
)

This enhanced agent can now handle complex queries that require multiple steps to resolve. But let's see what happens when we test it with a seemingly simple question.

The Hidden Complexity of "Simple" Questions

Consider this user query: "What's the shipping status of my order shipped to New York? I'm user Bob". From the user's perspective, this seems straightforward. However, our enhanced agent must now:

1. Verify the user's identity and account status
2. Retrieve the user's recent orders
3. Identify which order was shipped to New York
4. Check the shipping status for that specific order
5. Synthesize all this information into a coherent response

Let's examine the actual output to understand the performance implications:

Analyzing Multi-Step Agent Execution

When we run this query, the enhanced agent produces a much more complex execution trace:

{
    "messages": [
        HumanMessage(content="What's the shipping status of my order shipped to New York? I'm user Bob", ...), 
        AIMessage(
            content="", 
            response_metadata={
                "token_usage": {
                    "completion_tokens": 90, 
                    "prompt_tokens": 351, 
                    "total_tokens": 441,
                },
                "finish_reason": "tool_calls",
            },
            tool_calls=[
                {
                    "name": "get_user_info", 
                    "args": {"user_id": "Bob"}
                }
            ]
        ), 
        ToolMessage(content="User Bob: Active account, Premium tier", ...), 
        AIMessage(
            content="", 
            response_metadata={
                "token_usage": {
                    "completion_tokens": 86, 
                    "prompt_tokens": 415, 
                    "total_tokens": 501,
                }, 
                "finish_reason": "tool_calls",
            },
            tool_calls=[
                {
                    "name": "get_user_orders", 
                    "args": {"tier": "Premium", "user_id": "Bob"}
                }
            ]
        ), 
        ToolMessage(content="User Bob recent orders: Order #A001, Order #A002", ...), 
        AIMessage(
            content="", 
            response_metadata={
                "token_usage": {
                    "completion_tokens": 113, 
                    "prompt_tokens": 475, 
                    "total_tokens": 588
                }, 
                "finish_reason": "tool_calls"
            }, 
            tool_calls=[
                {
                    "name": "get_order_details", 
                    "args": {"order_id": "A001"}
                }
            ],
        ), 
        ToolMessage(content="Order A001: $299.99, shipped to New York", ...), 
        AIMessage(
            content="", 
            response_metadata={
                "token_usage": {
                    "completion_tokens": 60,
                    "prompt_tokens": 515, 
                    "total_tokens": 575,
                },
                "finish_reason": "tool_calls"
            },
            tool_calls=[
                {
                    "name": "check_shipping_status", 
                    "args": {"order_id": "A001", "destination": "New York"},
                }
            ]
        ), 
        ToolMessage(content="Order A001: In transit, expected delivery tomorrow", ...), 
        AIMessage(
            content="Your order (A001) shipped to New\u202fYork is currently **in transit** and is expected to be delivered **tomorrow**.", 
            response_metadata={
                "token_usage": {
                    "completion_tokens": 80, 
                    "prompt_tokens": 574, 
                    "total_tokens": 654
                },
                "finish_reason": "stop"
            }
        )
    ]
}

Total time consumption: 12.98 seconds

Let's analyze the output structure for this more complex query. The agent's response now consists of a longer sequence of message types, each corresponding to a step in the agent's reasoning and tool execution chain:

1. HumanMessage:
       - The user's initial request, now more complex and requiring multiple steps to resolve.
2. First AIMessage:
       - The AI model determines that it needs to verify the user's identity.
       - Tool call details: selects get_user_info with the appropriate arguments.
       - Token usage increases (441 tokens), reflecting the more complex prompt and reasoning.
3. ToolMessage
       - The agent executes get_user_info and returns the user's account information.
4. Second AIMessage
       - The AI model reasons that it now needs to fetch the user's recent orders.
       - Tool call details: selects get_user_orders with the user's tier and ID.
       - Token usage continues to grow (501 tokens).
5. ToolMessage
       - The agent executes get_user_orders and returns a list of recent orders.
6. Third AIMessage
       - The AI model decides to get details for the most relevant order.
       - Tool call details: selects get_order_details for the latest order.
       - Token usage increases further (588 tokens).
7. ToolMessage
       - The agent executes get_order_details and returns order specifics, including destination.
8. Fourth AIMessage
       - The AI model determines it needs to check the shipping status for the order and destination.
       - Tool call details: selects check_shipping_status with order ID and destination.
       - Token usage remains high (575 tokens).
9. ToolMessage
       - The agent executes check_shipping_status and returns the shipping update.
10. Final AIMessage
        - The AI model synthesizes all gathered information into a comprehensive, user-facing response.
        - This message is the final answer, formatted for clarity.
        - Token usage peaks at 654 tokens for this step.

Summary:
This single query required ten message exchanges, each step involving additional reasoning and tool calls. The total time consumption rose to 12.98 seconds, and the output length and token usage increased dramatically compared to the simple agent example. This illustrates how agent complexity leads to longer reasoning chains, higher costs, and slower responses.
 

Identifying the Core Performance Challenges

As agents become more capable, three fundamental problems emerge that impact both cost and reliability.
 

Challenge 1: Token Consumption Explosion

Every interaction with the AI model consumes tokens, and these costs accumulate rapidly. Consider our enhanced customer service agent handling the shipping status query:
Token Consumption Analysis for a Single Query:

• Initial request processing: The user's input and prompt setup consume approximately 351 tokens.
• Tool selection reasoning: The agent's first reasoning step, including tool call planning, adds 90 completion tokens (total 441 tokens so far).
• First API call (`get_user_info`): The tool's response is processed, and the next reasoning step increases the prompt to 415 tokens, with 86 more completion tokens (total 501 tokens).
• Second API call (`get_user_orders`): The agent parses order data, pushing the prompt to 475 tokens and adding 113 completion tokens (total 588 tokens).
• Third API call (`get_order_details`): Detailed order information is handled, with the prompt at 515 tokens and 60 more completion tokens (total 575 tokens).
• Fourth API call (`check_shipping_status`): Shipping status is retrieved, and the final reasoning step brings the prompt to 574 tokens, with 80 completion tokens (total 654 tokens).
• Final response generation: The agent combines all gathered information to produce a comprehensive answer, bringing the cumulative token usage to over 650 tokens. With four function calls involved, the total token consumption for this query exceeds 2,000 tokens.

Result:
Even in this example, each tool is designed to accept and return only simple, short strings. If your tools handle larger inputs—such as documents, images, or lengthy text—or generate more verbose outputs, token usage will increase much more rapidly. As agent workflows grow in sophistication and tool input/output expands, a single complex query can easily surpass thousands of tokens. Each additional step compounds token consumption, driving up both operational costs and response times.
When you multiply this across hundreds or thousands of daily queries, costs become significant. Worse, token usage tends to grow exponentially as agent complexity increases.
 

Challenge 2: Decision Loops and Instability

Agents can get trapped in unproductive patterns. Here's a typical problematic scenario:

These loops not only waste money but create poor user experiences with slow, unreliable responses. The situation becomes even worse when the agent selects incorrect tools or provides wrong parameters — the model must then spend additional time and tokens recognizing the error, backtracking, and attempting corrections. This error-and-retry pattern can triple the execution time for a single query, while the user waits through multiple failed attempts before receiving a useful response.

Challenge 3: Context Overload

As we add more tools and capabilities, the AI model's context becomes cluttered:

# Agent with too many tools becomes confused
massive_agent = create_react_agent(
   model=model,
   tools=[
       get_user_info, get_user_orders, get_order_details, check_shipping_status,
       process_return, cancel_order, update_address, change_payment_method,
       apply_discount, check_inventory, schedule_delivery, send_notification,
       generate_invoice, update_preferences, check_loyalty_points,
       # ... 20+ more tools
   ],
   prompt="Handle all customer service requests..."
)

With too many options, the model:

• Takes longer to decide which tool to use
• Makes more selection errors
• Requires more reasoning tokens for each decision
• Produces less reliable results
   

The Cost Reality: A Concrete Example

Let's put these challenges into perspective with a concrete cost scenario. Assume we're using the o4-mini model, with input tokens priced at $4 per 1M and output tokens at $16 per 1M. If we estimate an average blended rate of $6 per 1M tokens (based on typical input/output ratios), and our customer service agent processes 1,000 queries per day, the monthly costs for different agent complexities would look like this:

The cost increase isn't just linear—it's exponential due to increased complexity at every step:

• More tool selection decisions
• Increased error rates requiring retries
• Longer context windows for each operation
• More complex reasoning chains
   

Performance Impact Beyond Cost

The following performance data is based on our observations and estimates from running the sample agent described above. Actual results may vary depending on implementation details and workload. 

• Response Time Degradation (Estimated):
      - Simple agent: 2-3 seconds average response
      - Enhanced agent: 8-12 seconds average response 
      - Complex agent: 15-30 seconds average response
• Reliability Decline (Estimated):
      - Simple agent: 95% success rate
      - Enhanced agent: 75% success rate
      - Complex agent: 60% success rate
• User Experience Issues:
      - Unpredictable response times
      - Inconsistent answer quality
      - Frequent timeout errors
      - Unclear error messages when things go wrong
   

What's Next

The next article will introduce a range of optimization strategies designed to address the performance challenges outlined above. These approaches will focus on improving efficiency and scalability as agent complexity increases.