function calling agent: Strict validation of parameters to prevent errors

When AI Starts Calling Functions: Why the Small Parameters Break the Big AI Agent

There's a moment in the middle of development when you look at the screen and say to yourself: "This can't be." You've built a sophisticated AI agent, set up beautiful functions, perfected every line of code – and yet, one function call with one not-quite-accurate parameter brings down the entire tower. It passed in tests, worked in the demo, crashed at the client's.

In the new world of function calling agent, where language models know not just how to "write text" but also how to execute code, services, and APIs, the weak link is almost always the same old link: parameter validation. What used to be "well, we'll check on the server side" has today become a trench warfare between the model, the developer, and the unpredictable reality of users.

And if we're being honest for a moment: most people building smart AI agents today invest heavily in the model itself, a bit less in the wrapper, and almost always – way too little in upfront parameter validation. And that's where the really interesting failures begin.

The Transition from Chatbot to AI Agent: How One Function Becomes a Gateway to the Real World

In recent years, the industry has moved from the trend of "cute chatbot on a website" to a much more ambitious world: AI agents that operate semi-autonomously, communicate with systems, update data, order services, make technical decisions. In other words – no longer a toy, but a real player in the digital production line.

The moment you introduce function calling into the game, the model is no longer just "recommending" or "phrasing nicely", but actually executing functions in your code. It passes parameters, builds objects, makes API calls. All of this, of course, in real time, in front of users who expect it to "just work".

But here's the trap: we tend to treat the model as if it were a disciplined developer, one who wouldn't send any parameter without thinking twice. In practice, the model tries to guess. It guesses types, guesses formats, guesses user intentions. And when that guess goes directly to a critical function without strict validation – the result can range from a small glitch to real business damage.

The Paradox: The Smarter the AI Agent, the Tighter the Guard It Needs

This is the paradox that many product managers and CTOs in Israel quietly discuss between meetings: "The smarter our system became, the more we found ourselves fixing weird bugs". Why? Because a good AI agent knows how to invent. Sometimes it's amazing, sometimes it's dangerous.

Without strict parameter validation before a function call goes out – the chance of deviation increases. The value "tomorrow morning" can turn into tomorrow in an unrecognized date format; a username can arrive in Hebrew letters, when behind the scenes you expect an English slug; and an amount "around a thousand" might be interpreted as 100000. These aren't theoretical examples, this happens.

What is a Function Calling Agent, and How Does It Think About Parameters

To understand why parameter validation is critical, we need to pause for a moment on how a typical function calling agent works. Underneath all the marketing layer, there's a fairly clear pattern: a large language model (LLM), function definitions (tools), and a wrapper that mediates between them.

Wait, What Does a Typical AI Agent Look Like in the Field Today?

In a standard scenario, you define a list of functions for the model: create_order, get_user_profile, update_subscription – each with a name, description, and parameter schema in JSON format. The AI agent receives a request from the user, analyzes the text, decides which function is relevant and "assembles" parameters for it.

Seemingly, everything is organized. You even specify that the amount field is of type number, that the currency field is a string, and that the date field is a date in ISO format. But the model doesn't really "feel" JSON. It doesn't run unit tests. It simply generates text that looks like JSON, according to the examples it received and the patterns it learned.

Where Does the Error Occur? In the Small Details

Those who followed the first implementations of AI agents in Israel tell of a recurring pattern: maybe 90% of calls go smoothly, but the remaining 10% – those that come with rounded corners, creative phrasing, or unexpected input – that's exactly where the system breaks.

And we need to say this openly: generic models don't deeply understand Israeli income tax laws, nor all types of invoice numbering in the country, nor all the exceptions that have accumulated over the years in local ERP systems. They simply can't. And therefore, if you send them a function like create_invoice and expect them to always fill all parameters in a "legal" way – you're asking them for something that's almost impossible.

Strict Validation: Not a Bad Word, but the First Line of Defense

In the old world of software development, validation was often "decoration": some client-side check, a few server-side checks, and move on. In the world of function calling agents – this is already a matter of survival. Without a strong validation layer between the model and the code, you're essentially giving the model an open key to your database.

Three Levels of Validation Without Which It's Simply Dangerous to Work

We can put it simply: there are three validation layers that a healthy AI agent needs:

First layer – Schema-based validation. That is, using JSON Schema, Pydantic, or any other strict mechanism that ensures types, formats, and requirements (required) are indeed implemented. This is the minimum. The model might try to send you "amount": "five hundred", but the schema will stop it.

Second layer – Business validation. Here it's no longer enough to say "it's a number", you need to check: Is this a reasonable range? Is this user authorized to perform this action? Does the combination of parameters comply with the organization's internal rules? This is where the AI agent needs to cooperate with the good old world of logic.

Third layer – Contextual validation. Sometimes, the system simply needs to stop and say: "Wait, I don't understand". For example, when the user requested "book me the same package from a year ago" – and there's no unambiguous identifier. Instead of inventing, a good AI agent will return to the user with a clarifying question.

Validation Shouldn't Ruin the Experience – But Save It

There's a natural concern: if we put too much validation, we'll turn the AI agent into something clumsy, nagging, one that constantly asks repetitive questions. The truth is it depends on how you build it. Good validation doesn't have to manifest as a red error message, but as a smooth conversation: "I want to make sure I understood correctly: the payment is in 3 installments, 1200 NIS each?".

Those working on AI agents in the financial world in Israel today report that users actually appreciate the clarifying questions. Especially when it comes to money. The combination of automatic functions and validation that respects the user – that's already a different level of trust.

The Israeli Case: Language, Regulation, and Local Complexity That AI Agents Must Learn to Respect

Israel is a strange market, for better or worse. We have Hebrew, English, sometimes Russian and Arabic in the same support conversation. We have local regulation influenced by Europe but not quite identical to it, banking processes that don't always align with what's written in cloud vendor documentation, and many operational "workarounds" built over the years.

When you introduce an AI agent into an Israeli organization – especially one that does function calling against core systems – this versatility becomes a minefield. The user writes "make me a transfer to Ze'ev, like last time, just this time spread it out". What does "spread it out" mean? Installments? Standing order? Loan? The model guesses. Without validation that goes over the actual parameters, checks who Ze'ev is, what was done with him before, and what's allowed by law – this could turn into an angry phone call to customer service.

Validation Against Business Reality – Not Just Against Code

One of the differences between how AI agents work in Israel versus the US, for example, is that in Israel many businesses are still "half-digital". One system in the cloud, one on a local server, one entirely in Excel files. When you want to connect them through function calling, the fascinating (or frustrating) world of integration is revealed.

Strict parameter validation here no longer deals only with the question "is this valid JSON", but with a much deeper question: Is this information reliable? Is it complete? Is there a chance that some fields are outdated? An AI agent that receives a customer_id parameter from one system needs to make sure it's not using it incorrectly in another system, where the indexes changed two years ago.

A Small Example from the Field

One of the large banks, which recently tested integrating an AI agent for customer service, quickly discovered that problems don't start in the conversation, but in function calls. A simple customer request "increase my credit limit" was translated into parameters that the core system didn't recognize, or worse – misunderstood. In the first round, this created an unusual amount of "manual correction requests". In the second round, after introducing a strict validation layer, the AI agent started asking short clarifying questions before calling the function. The failure rate dropped.

What Does "Smart" Validation Look Like in the World of AI Agents?

The interesting question is not "do we need validation", because the answer is clear, but "how should it look in an era where a language model is the one writing the function call?". The instinctive tendency is to build one big layer of ifs and check everything. This doesn't work long-term. It's not scalable, and it's also not maintainable.

Combining Declarative Validation with Model Intelligence

The more sophisticated approach is to use two heads: on one hand, declarative validation, defined in clear schemas, that can be documented and versioned. On the other hand, use the model itself to help with clarification – but not to make definitive decisions.

For example, if an AI agent receives free text from the user like "book me a double room for next weekend in Eilat, nothing expensive", the model can try to map this to parameters of a function like book_hotel. But before executing the call, a validation layer can:

Check that the dates are valid, that there's no conflict with known limitations (for example, minimum nights), and that every essential parameter is indeed filled. If something is missing – the model receives back a "soft error" phrased in natural language, and asks the user a complementary question. This way a function calling agent becomes a conversation with a logical pace, not Russian roulette.

Validation as an Observer, Not a Policeman

Another insight is starting to form among advanced AI agent developers: validation doesn't have to be a blocking "iron wall", but can also operate as an observer. That is, instead of just blocking a call, it also learns from it, records patterns, identifies anomalies over time.

For example, if you see that in 30% of cases the model fills a certain field partially or incorrectly, that's not just a "bug". It's an indication for improvement in function design, in the description passed to the model, or even in the user interface. In this sense, good validation is also a sensing system.

Questions and Answers: What Everyone Asks When Starting to Work with AI Agents and Function Calling

Question: If the Model Already Returns JSON According to Schemas, Why Do We Need Additional Validation?

Seemingly, that should be enough. In practice, a language model "plays" JSON, it doesn't really run data types. It can return a date "2025-13-40" – formally it looks valid, logically it's absurd. Basic schemas won't catch this. Additionally, there are things only you know: what are the allowed values, who is the current user, which fields must be consistent.

Question: Won't Strict Validation Kill the Conversation Experience with the AI Agent?

Depends on how you implement it. If the system simply throws technical errors – yes, it's frustrating. If, on the other hand, you translate validation failures into natural questions – the user will even feel that the service "invests in them". "Just making sure – is this the final amount, including VAT?" sounds much better than "parameter amount is invalid".

Question: Can We Trust the Model Itself to Do Validation?

You can use it for completions, clarifications, suggesting corrections, but not as the sole line of defense. An AI agent needs to go through explicit rules that you control. The model is good at understanding language, context, and intentions. Less good at enforcing consistent rules over time.

Question: How Do You Start Introducing Validation to an Organization That's Already Running Function Calling Without It?

Usually you start with a modest layer: detailed logging for all function calls, analysis of edge cases and failures, then adding validation to the most critical types of operations (money, sensitive data, irreversible actions). From there you can expand. You don't have to, and maybe shouldn't, introduce heavy validation for everything at once.

Table: Summary of Key Aspects of Validation in AI Agents with Function Calling

Practical Insights: How to Think About Validation When Planning AI Agents

Instead of seeing validation as a late "punishment", it's worth introducing it already at the planning stage. When you define a new function for function calling, try asking yourself: if the model were a talented 10th-grade student, what's the chance it would invent a value instead of saying "I'm not sure"? And what damage could occur if that invention goes through?

A good AI agent is not one that pretends to know everything, but one that knows when to stop. When to say: "Here I need a moment of help from the user", or "Here I need to check again with the core system". Strict parameter validation is essentially this help mechanism – not just protection for the code, but also protection for the system's dignity in front of the user.

Another point that's not always discussed: good validation also allows you to expand the AI agent's capabilities without fear. When you know that every new feature must pass through a strict testing layer, you can experiment with more scenarios, give the model more freedom of expression, without worrying that every small experiment will reach directly to the most sensitive database in the organization.

No More "Let's Do a Pilot and See" – But Conscious Planning of Boundaries

In Israel we love pilots. "Let's roll it out to a hundred users, see what happens". In the worlds of AI agents with function calling, this approach can be expensive. A pilot without organized validation is a pilot where every small mistake can create organizational trauma and fear of technology "that's not really ready".

A smart pilot, on the other hand, is one that comes with clear boundaries: what the AI agent can do, what it can't, where double validation will always be performed, and which scenarios still remain entirely human. Here too, parameters are the story: where exactly does the meeting point pass between the smart conversation and the dangerous call.

A Final Word: Why Validation is Not "Anti-AI" But the Opposite

Sometimes, in hallway conversations, you hear sentences like "if we already brought an AI agent, why do we need so many limitations?". This is an understandable perception, but also somewhat dangerous. Strict parameter validation is not anti-AI, it's the condition for using AI in real, meaningful places, and not just on a demo page.

In the end, a function calling agent is a bridge between two worlds: human conversation, flexible, sometimes ambiguous – and code, the deterministic world, that doesn't tolerate half-values. Validation is the barrier in the middle of the bridge, the one that ensures not every nice sentence instantly becomes a system command.

If you're at a stage where your Israeli organization is examining AI agents, debating function calling, or already experiencing strange parameter failures – this is exactly the moment to stop and plan a serious validation layer. Not as another "ticket" in Jira, but as part of the architecture.

And if you want to break down this complexity together, build a smart agent that respects both language and code, we'd be happy to help with an initial consultation at no cost – at least so you know where you stand, before the next function breaks your night.