Evaluating Agentic Tool-Calling Reliability in Production

Technical Challenge & Context

Agentic workflows rely on LLMs outputting structured JSON function arguments. In real-world environments, models frequently produce malformed JSON, invalid data types, or missing required keys, breaking backend execution pipelines.

System Architecture & Engineering Pattern

We implement a 3-layer validation harness using Pydantic schema validation, automated LLM re-prompting on failure, and deterministic fallback execution graphs. Benchmark suites measure Tool Accuracy, Function Recall, and Self-Correction Pass Rates across 1,000+ edge-case prompts.

Production Code Blueprint

from pydantic import BaseModel, Field, ValidationError

class ReRoutePO(BaseModel):
    po_number: str = Field(..., pattern=r"^PO-\d{6}$")
    new_supplier_id: str
    qty_adjusted: int = Field(..., gt=0)

def validate_and_execute_tool(raw_json_str: str):
    try:
        data = ReRoutePO.model_validate_json(raw_json_str)
        return {"status": "SUCCESS", "payload": data.model_dump()}
    except ValidationError as e:
        return {"status": "RETRY_REQUIRED", "error": str(e)}

Key Operational Takeaways

• Always wrap LLM function calling with strict runtime schema validators (Pydantic / Zod).
• Feed exact validation error tracebacks back into the prompt context for instant self-correction.
• Maintain a golden benchmark test suite to regression-test model updates before deployment.

Author Overview

Rohit

Senior AI & Data Science Consultant

2+ Decades AI Experience

First built neural networks in C language in 2004 at IIT Roorkee under the mentorship of Dr. Sunil Padhi (HOD, Electrical Department) to predict annual sunspots. Today designing enterprise Agentic AI workflows, vLLM GPU clusters, and Custom RAG.