Agentic Tool Calling Challenges

And how to solve the reliability crisis
06/30/2025

Earlier this month, our AI agent confidently fabricated an entire organization name when searching for documents. The model called our search API with completely fictional parameters, returned no results, then hallucinated explanations for why the "missing" organization wasn't in our system. Our user lost an hour troubleshooting a problem that didn't exist.

This wasn't a one-off bug. It represents a systematic reliability challenge that's blocking AI agent deployments across industries.

The Production Reality Check

When function calling emerged in 2023, it seemed like we'd solved AI's biggest limitation: connecting language models to real-world tools and APIs. Two years later, the capability has matured significantly, but reliability remains the bottleneck for production deployments.

The numbers tell the story. Recent benchmarking reveals that even the highest performing LLMs achieve little over 70% average success rate across complex domains, well below the reliability threshold needed for autonomous operation. According to LangChain's 2024 survey of over 1,300 professionals, about 51% of respondents are using agents in production today with performance standing out as the top concern.

The cost compounds quickly. Failed tool calls trigger manual interventions, delay workflows, and erode user trust. For real-world deployments, this translates to operational overhead that often exceeds the efficiency gains AI was supposed to deliver.

How Tool Calling Actually Fails

Recent research reveals that failures follow predictable patterns. Understanding these patterns is the first step toward building reliable systems.

Tool Selection Hallucinations

Models choose completely inappropriate tools for the task at hand. A financial analyst asks for quarterly revenue data, and the agent calls a calendar API instead of the financial database. Or agents get stuck in loops, calling the same tool repeatedly with identical inputs.

Parameter Fabrication

This is the most insidious category. When agents lack sufficient information, they invent parameter values rather than asking for clarification. Our fabricated organization example fits here—the model created a plausible company name rather than admitting it didn't have enough context to proceed.

Format and Timing Failures

Invalid JSON structures, incorrect parameter names, and wrong data types create downstream errors. Even worse, agents often call tools at inappropriate moments in workflows, like triggering email sends before validation steps complete.

Hpw Multi-Step Workflows Amplify Problems

Single tool call failures are manageable. Multi-step workflow failures can be catastrophic and quickly get out of hand.

Small errors compound exponentially. A single incorrect parameter in step one cascades through an entire sequence. The agent uses fabricated data as input for subsequent tools, creating layers of fiction that become increasingly difficult to detect.

Context degradation accelerates the problem. Research shows AI performance degrades when critical information sits in the middle of long contexts—the "lost in the middle" phenomenon. As workflows grow longer, agents lose track of earlier results, leading to contradictory tool calls and parameter guessing.

Detection becomes nearly impossible. These failures often look completely reasonable to human observers. The agent follows logical steps, generates plausible explanations, and produces outputs that pass surface-level review. Organizations discover problems only when end users report incorrect results.

What Success Looks Like

Treating reliability as a systems engineering problem rather than a model limitation is a crucial key to success.

Defense-in-depth architectures assume failures will occur and build systematic safeguards accordingly. Just as distributed systems achieve reliability through redundancy and error handling, AI agents need similar engineering discipline.

Strategic human integration replaces the myth of full automation. Successful deployments implement approval gates for high-stakes operations, escalation protocols when confidence drops, and manual intervention points at critical junctions.

Multi-agent architectures can improve reliability when properly orchestrated. Anthropic's multi-agent blog shows that multi-agent systems help spend enough tokens to solve problems, with token usage explaining 80% of performance variance in complex browsing tasks. Rather than one agent handling complex workflows, specialized agents with clear responsibilities can isolate failures and enable targeted validation.

The Reliability Engineering Playbook

Organizations achieving production success follow consistent patterns:

1. Layer Your Defenses

Start with input validation—screen requests for completeness before any tool calling begins. Add tool selection validation through secondary agents that verify choice appropriateness. Implement parameter generation review to ensure values derive from actual user input rather than hallucination.

Monitor execution in real-time with automatic circuit breakers. Validate outputs against expected patterns before returning results. Log everything—you can't fix problems you can't measure.

2. Invest in Observability Early

Track tool call success rates across different contexts. Monitor parameter accuracy and implement hallucination detection. Measure context degradation in long conversations. Set up cost monitoring with automatic safeguards against runaway expenses—multi-agent systems typically use about 15× more tokens than chat interactions.

3.Use Technology Force Multipliers

Modern frameworks like PydanticAI extract parameter descriptions from function docstrings and enforce schema compliance at runtime. vLLM and similar systems use guided decoding to guarantee valid tool call formats, preventing entire categories of formatting errors.

Implement retrieval-augmented approaches for dynamic documentation lookup and context-aware parameter generation. While specific performance improvements for tool calling vary by implementation, RAG techniques have shown significant benefits for enhancing LLM accuracy and reliability by grounding responses in relevant external data

Start Conservative, Scale Systematically

Begin with narrow, well-defined use cases where requirements are clear and failure modes are understood. Administrative automation—document processing, scheduling, data retrieval—provides excellent learning ground before expanding to complex workflows.

Timeline expectations matter. Development timelines for fully functional AI agents typically range from 8-9 months with basic agents taking 4-8 weeks. Budget for additional infrastructure costs, typically 20-30% above base compute costs for validation layers and monitoring.

Measure what matters. Track accuracy improvements, operational overhead reduction, and user trust metrics using agent-specific evaluations. Focus on key metrics that reveal agent behavior: task completion rates (whether agents actually finish what users ask), tool calling accuracy (correct function selection and parameter usage), and reasoning quality (logical step-by-step progression). Microsoft's Azure AI Evaluation library and similar frameworks provide standardized approaches for measuring these agent-specific capabilities. Track system performance through latency, costs per agent run, and automated evaluation scores using LLM-as-judge approaches.

Next Steps

Tool calling reliability represents a solvable engineering challenge, not a fundamental limitation. The frameworks and patterns documented by early adopters provide a solid foundation for building autonomous systems that actually work in production.

1. Implement basic logging and error tracking for your existing tool calls. You need visibility before you can improve reliability.
2. Add input validation and schema enforcement. These provide immediate reliability gains with minimal complexity.
3. Design your defense-in-depth architecture with human integration points. The organizations that solve reliability first will dominate their markets as AI adoption accelerates.

The competitive advantage belongs to teams that treat AI agents as distributed systems requiring proper engineering discipline. Start building that muscle and see how agents enable you to solve complex problems.