How Poor Tool Calling Behavior Increases LLM Cost and Latency

Your AI agent just made twelve API calls to answer a question that needed two. Each unnecessary tool call burned tokens, added latency, and pushed your costs higher, all while the user waited.

Tool calling is what makes AI agents useful beyond text generation, but it's also where inefficiencies compound fastest. This guide breaks down exactly how poor tool calling behavior inflates LLM costs and latency, the warning signs to watch for, and the optimization strategies that actually work.

What is Tool Calling in LLMs?

Poor tool calling behavior in AI agents increases cost and latency through inefficient execution paths and unnecessary processing. When an LLM invokes external APIs, databases, or retrieval pipelines during a request, that's tool calling (also called function calling). This mechanism lets AI agents take real-world actions beyond generating text.

Here's the core vocabulary:

• Tool calling: The LLM requests execution of an external function during inference

• Function calling: Same concept, different name—used by OpenAI, Anthropic, and other providers

• AI agents: Systems that chain multiple tool calls together to complete multi-step tasks

Every tool call adds overhead. The model generates a structured request, waits for the external system to respond, then processes that response before continuing. When this runs efficiently, agents feel fast and costs stay predictable. When it doesn't, you're looking at ballooning token usage and frustrated users.

Common Signs of Poor Tool Calling Behavior

Before diving into cost and latency impacts, it helps to recognize the warning signs. The patterns below often indicate your AI agent implementation could use optimization.

Excessive Tool Calls per Request

When an LLM makes far more tool calls than a task actually requires, something's off. This typically happens with unclear instructions or overly granular tool definitions. A simple lookup that could happen in one call instead triggers five or six separate requests.

Redundant or Duplicate Calls

Watch for the same tool getting called multiple times with identical parameters within a single interaction. The model essentially forgets it already has the information, or the orchestration layer fails to cache results.

Sequential Calls That Could Run in Parallel

Independent tool calls executing one after another instead of concurrently add unnecessary wait time. If you're fetching user data and inventory data separately, and neither depends on the other, running them sequentially doubles your latency for no reason.

Ignoring or Misusing Tool Call Results

Sometimes the LLM fails to use returned data properly. It might re-fetch information it already has, misinterpret tool outputs, or ask for clarification when the answer is sitting right there in the context.

Failing to Cache Reusable Data

Without caching for frequently requested data, your agent hits external APIs repeatedly for static information. That's wasted time and money on every single request.

How Poor Tool Calling Increases LLM Costs

Costs in AI systems tie directly to token usage and computational resources consumed. Poor tool calling behavior inflates both.

Token Bloat From Verbose Tool Definitions

Overly detailed tool schemas consume input tokens on every request, even when those tools aren't called. If your tool definitions run 500 tokens each and you have ten tools, that's 5,000 tokens of overhead before the user even asks a question.

Wasted Tokens on Unnecessary Calls

Each tool call adds tokens for the request structure and the response content. Redundant calls multiply this cost directly. A model that makes three unnecessary API calls might double your token spend for that interaction.

Retry Storms From Weak Error Handling

Poor error handling leads to repeated failed attempts, each consuming additional tokens. Without exponential backoff or circuit breakers, a single flaky API can trigger dozens of retries. You pay for every one.

Context Window Overflow From Tool Results

Large tool outputs fill the context window fast. When you hit the limit, you're forced to truncate (losing information) or start a new session (losing all context). Either way, you're paying more for worse results.

How Poor Tool Calling Increases Latency

Latency, the delay between a user request and the AI's response, compounds quickly with inefficient tool use.

Synchronous Blocking on External APIs

Waiting for one API response before initiating the next creates sequential delays that stack. Five API calls at 200ms each means a full second of wait time, even if the LLM itself responds instantly.

Cold Start Delays in Tool Execution

Serverless functions and containerized tools often have cold start penalties. If a tool hasn't been invoked recently, initialization overhead can add hundreds of milliseconds before actual execution begins.

Network Round-Trip Overhead

Each tool call adds network transmission time. This compounds with geographic distance between your agent and external services. Poor routing or missing connection pooling makes it worse.

LLM Reasoning Time for Complex Schemas

Complicated tool definitions increase the time the LLM spends deciding which tool to call and how. Simpler, clearer schemas lead to faster decisions.

How Multiple Tool Calls Compound Cost and Latency

Each additional tool call doesn't just add cost and latency, it compounds them. Agentic workflows with many steps amplify inefficiencies exponentially.

The compounding effect works like this:

• Linear addition: Each call adds its own cost and time

• Context growth: Previous tool results expand the context for subsequent calls, increasing token usage

• Error propagation: One slow or failed call can cascade through the entire chain

A ten-step agent workflow with 10% inefficiency per step doesn't cost 10% more. It costs significantly more because each step builds on the previous one's overhead.

Key Metrics to Monitor Tool Calling Performance

You can't optimize what you don't measure. The metrics below reveal tool calling health and help you spot problems before they become expensive.

Tool Calls per Request

Track the average and distribution of tool calls per user request. Healthy patterns vary by use case, but most well-designed agents complete requests with fewer than five calls. Spikes often indicate poor tool design or unclear prompting.

Time to First Token

TTFT (Time to First Token) measures how long until the model starts responding. For conversational AI, this metric drives perceived responsiveness. High TTFT often points to slow initial tool calls or complex reasoning before the first output.

End-to-End Latency

Total request duration from user input to complete response, including all tool execution time. This is what users actually experience.

Token Usage per Interaction

Track input and output tokens separately to identify where bloat occurs. Sudden increases often correlate with verbose tool responses or unnecessary calls.

Error and Retry Rates

Monitor failed tool calls and automatic retries as early warning signs. Rising error rates usually precede cost spikes and latency degradation.

How to Optimize Tool Calling for Lower Cost and Latency

The strategies below directly address the inefficiencies covered earlier.

Simplify Tool Definitions

Keep schemas minimal with only required parameters. Avoid verbose descriptions that inflate token counts. If a parameter is rarely used, consider making it a separate tool.

Batch Related Operations

Combine multiple related data requests into single tool calls where possible. Instead of three separate database queries, design a tool that fetches all needed data at once.

Implement Parallel Execution

Execute independent tool calls concurrently rather than sequentially. Modern orchestration frameworks support this natively.

Cache Frequently Used Results

Store and reuse tool outputs that don't change frequently. A semantic cache can eliminate redundant calls for similar queries, reducing both cost and latency.

Add Smart Retry Logic With Backoff

Implement exponential backoff and circuit breakers to prevent retry storms. Set reasonable timeouts so a single slow API doesn't block the entire request indefinitely.

Tip: Start by instrumenting your current tool calls with timing and token counts. The data often reveals obvious optimization targets within the first week.

Best Practices for Efficient AI Agent Tool Calling

Beyond tactical fixes, the design principles below prevent poor behavior from occurring in the first place.

Design Tools With Clear Single Responsibilities

Each tool does one thing well. Overloaded tools that handle multiple operations confuse the LLM and lead to misuse. Think Unix philosophy: small, composable, predictable.

Minimize Tool Output Verbosity

Return only essential data from tools. Trim unnecessary fields before passing results to the LLM. A 10KB API response that gets summarized to 100 tokens wastes context window space.

Use Streaming for Long Operations

Stream partial results for time-intensive tools to improve perceived responsiveness. Users tolerate longer waits when they see progress.

Test Tool Calling Behavior in Isolation

Evaluate tool calling patterns separately from overall agent performance. This isolation helps identify inefficiencies that get masked in end-to-end testing.

Scaling Tool Calling Without Scaling Costs

As usage grows, maintaining efficiency becomes critical. The infrastructure strategies below help:

• Prewarm functions: Keep frequently used tools ready to avoid cold starts

• Connection pooling: Reuse connections to reduce network overhead

• Load balancing: Distribute tool calls across regions to minimize latency

• Observability: Trace every tool call to identify bottlenecks before they impact users

Platforms like CodeAnt AI help engineering teams monitor and optimize AI-driven workflows at scale, providing visibility into where inefficiencies hide.

Build Faster AI Pipelines With Smarter Tool Calling

Efficient tool calling isn't a one-time fix, it's an ongoing practice. As models evolve and use cases expand, the patterns that worked yesterday might not work tomorrow.

The payoff is real, though. Teams that invest in tool calling optimization see lower costs, faster responses, and more reliable systems. User experience improves. Operational overhead drops. And your AI agents actually deliver on their promise.

CodeAnt AI helps teams identify inefficiencies across their development workflows, from code review to security scanning. When your tools work smarter, your engineers can focus on what matters.