Guide to Understanding, Constructing, and Optimizing API-Calling Agents

The role of Artificial Intelligence in technology firms is rapidly evolving;  AI use cases have evolved from passive information processing to proactive agents able to executing tasks. In keeping with a March 2025 survey on global AI adoption conducted by Georgian and NewtonX, 91% of technical executives in growth stage and enterprise firms are reportedly using or planning to make use of agentic AI.

API-calling agents are a primary example of this shift to agents. API-calling agents leverage Large Language Models (LLMs) to interact with software systems via their Application Programming Interfaces (APIs).

For instance, by translating natural language commands into precise API calls, agents can retrieve real-time data, automate routine tasks, and even control other software systems. This capability transforms AI agents into useful intermediaries between human intent and software functionality.

Firms are currently using API-calling agents in various domains including:

• Consumer Applications: Assistants like Apple’s Siri or Amazon’s Alexa have been designed to simplify every day tasks, resembling controlling smart home devices and making reservations.
• Enterprise Workflows: Enterprises have deployed API agents to automate repetitive tasks like retrieving data from CRMs, generating reports, or consolidating information from internal systems.
• Data Retrieval and Evaluation: Enterprises are using API agents to simplify access to proprietary datasets, subscription-based resources, and public APIs so as to generate insights.

In this text I’ll use an engineering-centric approach to understanding, constructing, and optimizing API-calling agents. The fabric in this text is predicated partly on the sensible research and development conducted by Georgian’s AI Lab. The motivating query for much of the AI Lab’s research in the world of API-calling agents has been: “If a company has an API, what’s essentially the most effective technique to construct an agent that may interface with that API using natural language?”

I’ll explain how API-calling agents work and find out how to successfully architect and engineer these agents  for performance. Finally, I’ll provide a scientific workflow that engineering teams can use to implement API-calling agents.

I. Key Definitions:

• API or Application Programming Interface : A algorithm and protocols enabling different software applications to speak and exchange information.
• Agent: An AI system designed to perceive its environment, make decisions, and take actions to realize specific goals.
• API-Calling Agent: A specialized AI agent that translates natural language instructions into precise API calls.
• Code Generating Agent: An AI system that assists in software development by writing, modifying, and debugging code. While related, my focus here is totally on agents that APIs, though AI may also help these agents.
• MCP (Model Context Protocol): A protocol, notably developed by Anthropic, defining how LLMs can connect with and utilize external tools and data sources.

II. Core Task: Translating Natural Language into API Actions

The elemental function of an API-calling agent is to interpret a user’s natural language request and convert it into a number of precise API calls. This process typically involves:

1. Intent Recognition: Understanding the user’s goal, even when expressed ambiguously.
2. Tool Selection: Identifying the suitable API endpoint(s)—or “tools”—from a set of accessible options that may fulfill the intent.
3. Parameter Extraction: Identifying and extracting the crucial parameters for the chosen API call(s) from the user’s query.
4. Execution and Response Generation: Making the API call(s), receiving the response(s), after which synthesizing this information right into a coherent answer or performing a subsequent motion.

Consider a request like, “Hey Siri, what is the weather like today?” The agent must discover the necessity to call a weather API, determine the user’s current location (or allow specification of a location), after which formulate the API call to retrieve the weather information.

For the request “Hey Siri, what is the weather like today?”, a sample API call might seem like:

GET /v1/weather?location=Latest%20York&units=metric

Initial high-level challenges are inherent on this translation process, including the anomaly of natural language and the necessity for the agent to keep up context across multi-step interactions.

For instance, the agent must often “remember” previous parts of a conversation or earlier API call results to tell current actions. Context loss is a standard failure mode if not explicitly managed.

III. Architecting the Solution: Key Components and Protocols

Constructing effective API-calling agents requires a structured architectural approach.

1. Defining “Tools” for the Agent

For an LLM to make use of an API, that API’s capabilities should be described to it in a way it may understand. Each API endpoint or function is usually represented as a “tool.” A strong tool definition includes:

• A transparent, natural language description of the tool’s purpose and functionality.
• A precise specification of its input parameters (name, type, whether it’s required or optional, and an outline).
• An outline of the output or data the tool returns.

2. The Role of Model Context Protocol (MCP)

MCP is a critical enabler for more standardized and robust tool use by LLMs. It provides a structured format for outlining how models can connect with external tools and data sources.

MCP standardization is useful since it allows for easier integration of diverse tools, it promotes reusability of tool definitions across different agents or models. Further, it’s a best practice for engineering teams, starting with well-defined API specifications, resembling an OpenAPI spec. Tools like Stainless.ai are designed to assist convert these OpenAPI specs into MCP configurations, streamlining the technique of making APIs “agent-ready.”

3. Agent Frameworks & Implementation Selections

Several frameworks can aid in constructing the agent itself. These include:

• Pydantic: While not exclusively an agent framework, Pydantic is helpful for outlining data structures and ensuring type safety for tool inputs and outputs, which is significant for reliability. Many custom agent implementations leverage Pydantic for this structural integrity.
• LastMile’s mcp_agent: This framework is specifically designed to work with MCPs, offering a more opinionated structure that aligns with practices for constructing effective agents as described in research from places like Anthropic.
• Internal Framework: It is also increasingly common to make use of AI code-generating agents (using tools like Cursor or Cline) to assist write the boilerplate code for the agent, its tools, and the encircling logic. Georgian’s AI Lab experience working with firms on agentic implementations shows this could be great for creating very minimal, custom frameworks.

IV. Engineering for Reliability and Performance

Ensuring that an agent makes API calls reliably and performs well requires focused engineering effort. Two ways to do that are (1) dataset creation and validation and (2) prompt engineering and optimization.

1. Dataset Creation & Validation

Training (if applicable), testing, and optimizing an agent requires a high-quality dataset. This dataset should consist of representative natural language queries and their corresponding desired API call sequences or outcomes.

• Manual Creation: Manually curating a dataset ensures high precision and relevance but could be labor-intensive.
• Synthetic Generation: Generating data programmatically or using LLMs can scale dataset creation, but this approach presents significant challenges. The Georgian AI Lab’s research found that ensuring the correctness and realistic complexity of synthetically generated API calls and queries may be very difficult. Often, generated questions were either too trivial or impossibly complex, making it hard to measure nuanced agent performance. Careful validation of synthetic data is totally critical.

For critical evaluation, a smaller, high-quality, manually verified dataset often provides more reliable insights than a big, noisy synthetic one.

2. Prompt Engineering & Optimization

The performance of an LLM-based agent is heavily influenced by the prompts used to guide its reasoning and power selection.

• Effective prompting involves clearly defining the agent’s task, providing descriptions of accessible tools and structuring the prompt to encourage accurate parameter extraction.
• Systematic optimization using frameworks like DSPy can significantly enhance performance. DSPy means that you can define your agent’s components (e.g., modules for thought generation, tool selection, parameter formatting) after which uses a compiler-like approach with few-shot examples out of your dataset to seek out optimized prompts or configurations for these components.

V. A Beneficial Path to Effective API Agents

Developing robust API-calling AI agents is an iterative engineering discipline. Based on the findings of Georgian AI Lab’s research, outcomes could also be significantly improved using a scientific workflow resembling the next:

1. Start with Clear API Definitions: Begin with well-structured OpenAPI Specifications for the APIs your agent will interact with.
2. Standardize Tool Access: Convert your OpenAPI specs into MCP Tools like Stainless.ai can facilitate this, making a standardized way on your agent to grasp and use your APIs.
3. Implement the Agent: Select an appropriate framework or approach. This might involve using Pydantic for data modeling inside a custom agent structure or leveraging a framework like LastMile’s mcp_agent that’s built around MCP.
   • Before doing this, consider connecting the MCP to a tool like Claude Desktop or Cline, and manually using this interface to get a feel for the way well a generic agent can use it, what number of iterations it normally takes to make use of the MCP appropriately and every other details which may prevent time during implementation.
4. Curate a Quality Evaluation Dataset: Manually create or meticulously validate a dataset of queries and expected API interactions. That is critical for reliable testing and optimization.
5. Optimize Agent Prompts and Logic: Employ frameworks like DSPy to refine your agent’s prompts and internal logic, using your dataset to drive improvements in accuracy and reliability.

VI. An Illustrative Example of the Workflow

Here’s a simplified example illustrating the really useful workflow for constructing an API-calling agent:

Step 1: Start with Clear API Definitions

Imagine an API for managing an easy To-Do list, defined in OpenAPI:

openapi: 3.0.0

info:

title: To-Do List API

version: 1.0.0

paths:

/tasks:

post:

summary: Add a brand new task

requestBody:

required: true

content:

application/json:

schema:

type: object

properties:

description:

type: string

responses:

‘201′:

description: Task created successfully

get:

summary: Get all tasks

responses:

‘200′:

description: List of tasks

Step 2: Standardize Tool Access

Convert the OpenAPI spec into Model Context Protocol (MCP) configurations. Using a tool like Stainless.ai, this might yield:

Step 3: Implement the Agent

Using Pydantic for data modeling, create functions corresponding to the MCP tools. Then, use an LLM to interpret natural language queries and choose the suitable tool and parameters.

Step 4: Curate a Quality Evaluation Dataset

Create a dataset:

Step 5: Optimize Agent Prompts and Logic

Use DSPy to refine the prompts, specializing in clear instructions, tool selection, and parameter extraction using the curated dataset for evaluation and improvement.

By integrating these constructing blocks—from structured API definitions and standardized tool protocols to rigorous data practices and systematic optimization—engineering teams can construct more capable, reliable, and maintainable API-calling AI agents.