Managing AI Hallucinations: The Power of Tools & Parameters

Summary: This article outlines practical strategies to mitigate hallucinations using tools, parameters, and structured approaches to agent design. Drawing on insights from real-world projects with Cognigy.AI, we provide a roadmap for creating robust AI Agents that combine the creativity of LLMs with the reliability of deterministic logic.

Generative AI is moving fast from experimental pilots to enterprise-critical applications. Large Language Models (LLMs) and AI Agents promise efficiency, automation, and new customer experiences, but they also introduce risks. Chief among them are so-called hallucinations: confident, yet false or misleading outputs that damage trust.

What Do We Mean by “Hallucinations”?

In everyday language, a hallucination means perceiving something that isn’t real. In AI, the term is used metaphorically. Large Language Models (LLMs) don’t dream up islands in deserts; instead, they produce outputs that are incomplete, outdated, incorrect, biased, or simply misaligned with what the user expected. For example:

• A booking assistant asks for booking details, even though that requirement was never defined.
• A cycling assistant suggests how to fix a slipped chain instead of dispatching roadside assistance.

The key point: such outputs aren’t always objectively “wrong.” More often, they represent a gap between the model’s learned patterns and the user’s, or more often, business partner's expectations. Yet for business applications, even these subtle mismatches undermine trust and reliability.

Why Hallucinations Happen

LLMs process tokens, not characters or words

Hallucinations aren’t random. They are the direct outcome of how LLMs work, for instance:

• Tokens, not words: Current models process text in tokens, not characters or words. This makes them prone to mistakes in tasks like counting letters, parsing IDs, or validating codes.
• Pattern completion over truth: LLMs usually act like advanced autocomplete systems. When prompted with “God save the…,” they predict the most likely continuation (e.g., “Queen”), not necessarily the factually correct one. It's like a multiple-choice exercise where you have to pick one choice, and no answer is not an option.
• No reward for “I don’t know”: Models are typically not incentivized to admit uncertainty. Instead, they produce the most plausible completion even if it’s wrong.

Risk Mitigation Checklist

Risk Mitigation Strategies

Dealing with hallucinations isn’t about finding a single fix. It’s about applying safeguards at multiple levels of your AI project. Think of it as a layered defense strategy. Each layer gives you opportunities to guide, restrict, and validate the model’s behavior, reducing the risk of unexpected or misleading outputs. For instance:

• User Layer - Set Expectations Upfront: disclaimers, terms of use, codes of conduct.
• App Layer - Control the Experience: orchestration, restricted inputs/outputs, isolated prompts.
• Prompt Engineering - Structure the Model’s Behavior: system messages, examples (in-context learning), prompt templates.
• Model Layer- Choose and Customize Wisely: model selection, fine-tuning, model garden (combine models).
• Filters - Moderate and Protect: moderation, PII detection, output control.
• Validation - Close the Loop: human-in-the-loop, rule-based checks, deterministic flows
  ...

This holistic approach ensures that risks are addressed at every layer, not just the LLM. By applying these layers consistently, businesses can build AI Agents that not only sound intelligent but also behave responsibly. The result is higher trust, fewer errors, and a smoother path to scaling AI into production.

Another powerful option is tools (or functions), which allow you to break out of the default “completion” behavior of Generative AI. Instead of letting the model guess, you define explicit actions and parameters. This gives you back control: Bridging natural conversation with deterministic business logic.

The Path Forward: Control with Tools and Parameters

Instead of relying on "prompt & pray", simply separate concerns

To get beyond a "prompt & pray" approach, effective AI Agents combine generative AI with deterministic logic. The secret lies in separating concerns using tools:

• Conversation Layer (LLM): Focused on language tasks such as understanding intent, maintaining natural dialogue, and shaping the user experience.
• Tool Layer (functions/parameters): Acts as the bridge between conversation and control, translating user input into structured actions.
• Business layer: Validates parameters, ensures compliance with rules and processes, and connects and processes the business logic.

Tools, Parameters, and Validation

Consider an agent designed to calculate calories for different fruits. At first glance, this seems like a straightforward task, but it illustrates why parameters and validation are critical. The tool definition includes a fruit parameter and, optionally, an amount parameter. Once captured, the parameter undergoes deterministic validation in the business logic layer. This validation ensures that the fruit exists in the catalog, checks for optional fields like quantity, and returns meaningful error messages if something is missing or invalid. For example:

• If a user says, “I want calories for 3 apples,” the schema is filled correctly, and the business logic calculates the result.
• If the user says, “I want calories for dragonfruit,” and that fruit is not in the database, the system responds with a clear explanation rather than guessing.

This design shows how combining conversational input with explicit parameters and validation closes the gap between user-friendly dialogue and reliable business outcomes.

With this architecture, LLMs handle what they do best: language and interaction. Deterministic systems take care of precision, validation, and reliable execution. This reduces hallucinations and makes AI Agents easier to debug, maintain, and scale.

The Complex Parameter Approach

One tool with a complex parameter set for a pizza order, including confirmation, removes the need for additional tools 

When designing AI Agents, many teams create separate tools for each step of a process. For example, in a pizza ordering scenario, one tool might capture the order, another confirms it, and a third validates the delivery address. While this mirrors the business process, it often leads to brittle results that depend heavily on fragile prompt engineering.

A more reliable alternative is the complex parameter approach. Instead of splitting the process into multiple tools, you define a single structured parameter model that contains all required information. For a pizza order, this could include: Pizza base (e.g., thin crust, regular), type of pizza, Toppings (optional list), customer details (name, address, phone number), and confirmation flag (whether the order has been confirmed).

The LLM’s role is to fill this structured schema during the conversation. As the user provides details step by step, the LLM leads the conversation until all required parameters are complete. This approach has several advantages:

• Simplifies design: One schema instead of multiple fragile tools.
• Supports natural dialogue: Users can provide details in any order, and the model fits them into the right structure.
• Separates concerns: The LLM handles conversation, while the business layer handles processing and validation.

For example, when a user says, “I’d like a salami pizza with no extra toppings,” the agent fills the schema accordingly. Later, when asked for the delivery address and phone number, the missing fields are populated. Finally, once confirmed, the tool is called, and the order is executed in the business layer. By using complex parameters, AI Agents can support more sophisticated workflows while remaining robust and easier to maintain.

Practical Techniques to Reduce Hallucinations

Seizing control using Tools

Hallucinations cannot be eliminated entirely using common models, but they can be reduced significantly by applying a structured design approach. The following techniques, distilled from practice, provide a pragmatic path forward:

• Consider Context or Tools: Decide early whether information should live in the conversation context or be retrieved through tools. Context works well for small FAQs or simple data. For structured or factual information, such as product catalogs or booking codes, tools are the safer choice.
• Separate Conversation and Control: Keep language tasks and business logic apart. Let the LLM manage the conversational experience while deterministic flows and tools handle precise execution. This separation makes agents more reliable and easier to maintain.
• Tools and Parameters: Define tools and parameters to guide the agent’s behavior. Avoid overly strict schemas that can introduce fragility, but embrace complexity when needed. For example, a single tool with a structured parameter set (like a full pizza order) is often more robust than multiple small tools.
• Validate explicitly instead of relying on "prompt & pray": Do not rely on LLMs to enforce critical constraints. Instead, validate inputs deterministically with rules and code. This approach ensures correctness and allows you to provide meaningful feedback to users when something goes wrong.
• Translate Conversation into Business Logic: Treat tools and parameters as a conversational interface. Once filled, translate them into business logic where processes are executed, transactions are validated, and compliance is enforced. This closes the gap between natural language interaction and operational reliability.

Conclusion

Hallucinations will not vanish overnight. They are a natural consequence of how large language models operate. However, by separating the conversational interface from the business logic, and by combining LLMs with tools, parameters, and explicit validation, organizations can create AI Agents that are reliable, explainable, and trustworthy.

The future of Conversational AI and AI Agents lies in this hybrid model: allowing LLMs to excel at language and adaptability, while delegating mission-critical logic to deterministic systems. With this approach, businesses can move confidently beyond demos and prototypes toward production-ready AI that genuinely enhances customer experience.