Unlocking AI’s Full Potential: Structured Outputs, Agentic Function Calling, and AI Agents

Artificial Intelligence (AI) is evolving beyond simple text generation and prediction tasks. Modern AI systems are now capable of structured outputs, agentic function calling, and autonomous AI Agents, allowing for more precise, interactive, and goal-driven applications. These advancements bring AI closer to real-world usability, making systems more efficient, reliable, and effective across industries.

Structured Outputs: Making AI More Reliable

Many business operations rely on a human to convert unstructured data into a usable, structured format—consider the process by which a client can email the details of an order and how a business will input those details into an order processing form. This manual task requires human intervention and takes valuable time from that employee's schedule. Enter Large Language Models (LLMs) and their ability to handle large amounts of unstructured data. A LLM’s Structured output capability solves this issue of manual tasks by ensuring that AI responses follow a predefined format, improving accuracy, consistency, and integration with other systems.

How Do Structured Outputs Work?

Schema Definition: The LLM is given a structured format to follow, such as JSON, XML, or tabular data.
Controlled Response Generation: Instead of open-ended text, the model generates structured data that can be directly used by applications.
Improved Validation & Parsing: Since the output follows a defined structure, it becomes easier to validate and process programmatically.
Enabling Programmatic Response: With structured outputs, LLMs can enable a business to programmatically leverage unstructured data based on defined scenarios.

Applications of Structured Outputs

Data Extraction: AI can return structured data from unstructured text, such as extracting key details from legal contracts, user surveys, or client emails.
API Responses: AI can generate structured JSON outputs for seamless API integration, enabling businesses to engineer systems to handle predefined scenarios despite the unstructured inputs.
Automated Report Generation: AI can provide well-organized tables, charts, and summaries rather than free-text reports.

By enabling structured outputs, AI models become significantly more practical for business applications, enhancing automation and system interoperability.

Agentic Function Calling: Enabling AI to Take Action

While traditional AI models generate responses, they often lack the ability to execute actions based on user input. Agentic function calling extends a LLM and allows AI to interact with external tools, APIs, and databases, transforming it from a passive assistant into an active agent capable of taking meaningful steps.

How Does Agentic Function Calling Work?

Function Definition: Developers define a set of functions AI can call, specifying input and output requirements.
AI Recognizes Intent: The model determines when a function call is needed based on user input.
Action Execution: AI triggers the appropriate function, retrieves or modifies data, and integrates the response back into the conversation.

What is the Model Context Protocol?

Model Context Protocol (MCP) provides a standardized structure for the implementation of API capable AI Agents.
MCP abstracts the API functionality outside of the AI Agent and instead gives that power to an MCP Client and MCP Server. The AI Agent communicates with the MCP Client which programmatically interacts with the MCP Server to conduct the API calls.
This bifurcation of the AI Agent and the API calls allows developers to build tools (MCP Clients and Servers) that can interact with a multitude of different AI Agents.
Much like the early days of API development, MCP attempts to provide structure and repeatability for developers and engineers.

Real-World Use Cases

Customer Support Agents: AI can fetch order details, process refunds, or update user accounts without human intervention.
Automated Scheduling: AI can book meetings by interacting with calendar APIs.
Financial Transactions: AI can retrieve real-time stock prices or execute trades based on user input.

By leveraging function calling, AI systems become interactive problem solvers rather than just information providers.

AI Agents: The Next Step in Autonomous AI

AI Agents take agentic function calling a step further by incorporating reasoning, memory, and decision-making capabilities. These autonomous AI systems can plan, execute multi-step tasks, and adapt to dynamic situations.

Key Features of AI Agents

Goal-Oriented Behavior: AI Agents work toward defined objectives rather than just responding to isolated prompts.
Memory and Context Retention: They remember past interactions, allowing for more coherent and personalized assistance.
Multi-Step Task Execution: AI Agents can break down complex problems into smaller steps and execute them in sequence.

Applications of AI Agents

Personal Assistants: AI-powered assistants can manage emails, schedules, and reminders proactively.
Automated Research Agents: AI can browse the web, gather relevant data, and summarize findings.
Business Process Automation: AI Agents can optimize workflows by making intelligent decisions based on real-time data.

With AI Agents, businesses can automate higher-level cognitive tasks, reducing human workload and improving operational efficiency.

The Future of AI: Combining These Advancements

The combination of structured outputs, agentic function calling, and AI Agents is revolutionizing AI applications. Structured outputs ensure reliability, function calling enables action, and AI Agents introduce autonomy. Businesses that leverage these technologies will gain a significant competitive edge by automating complex workflows, improving efficiency, and enhancing user experiences.

As AI continues to evolve, we are moving toward systems that don’t just generate information but actively assist in decision-making and execution. The future of AI is here, and it’s more intelligent, interactive, and impactful than ever.

Book a Demo