Function Calling Reliability Is the Real Bottleneck for AI Agents
Function Calling Reliability Is the Real Bottleneck for AI Agents
Everyone talks about which LLM is smartest. Nobody talks about which one is most reliable at calling functions. For agentic workflows, reliability is what actually matters.
The Compounding Failure Problem
Say your agent needs to execute a 10-step workflow - open an app, find a button, click it, read the result, copy data, switch apps, paste it, format it, save it, confirm. If each function call succeeds 95% of the time, your overall success rate is 0.95^10 = 60%. That is a 40% failure rate on a simple workflow.
At 99% per-call reliability, your 10-step workflow succeeds 90% of the time. At 99.9%, it is 99%. The difference between 95% and 99.9% function calling accuracy is the difference between a toy demo and a production tool.
Benchmarking Matters More Than Vibes
Most people pick their LLM based on chatbot benchmarks or coding contests. Those measure reasoning ability, not function calling precision. A model can score top marks on HumanEval and still hallucinate tool parameters, miss required fields, or call the wrong function entirely.
What you actually need to benchmark is structured output reliability - does the model return valid JSON with the correct schema every time? Does it pick the right function from a list of 20 options? Does it pass the correct arguments when the parameter names are similar?
What We Have Learned Building Fazm
For desktop automation, function calling reliability directly translates to user trust. If you tell your agent to "send that email" and it clicks the wrong button, you lose confidence instantly. We found that smaller models with fine-tuned function calling often outperform larger general-purpose models for specific tool-use patterns.
The practical approach is to benchmark on your actual tool definitions, not generic benchmarks. Run 100 calls with each model against your real function schema and measure exact match rates. The results will surprise you - the "best" model is often not the most reliable one.
Fazm is an open source macOS AI agent. Open source on GitHub.