5 AI Agents Every Startup Should Have for Increasing Productivity

Startups operate in a fast-paced environment where efficiency, scalability, and automation are critical for success. AI agents are evolving beyond simple automation tools into advanced systems capable of handling complex workflows, decision-making, and infrastructure management. In this blog we will explore 5 AI agents that will enhance startup productivity in key operational areas.

1. NEO AI – Automated Machine Learning Engineer

NEO AI automates machine learning workflows, handling tasks like data pre-processing, model selection, and experiment tracking. Through its chat-based interface, startups can streamline AI development and optimize models efficiently.

Key Benefits:

• Automates end-to-end ML workflows, reducing manual effort.
• Runs multiple experiments to identify the best-performing models.
• Enables faster AI development through an intuitive chat interface.

Example: A startup building a predictive analytics tool can use NEO AI to automate model selection and tuning, saving time and resources.

2. Auto-GPT – Autonomous Task Management

Auto-GPT is an open-source AI agent that autonomously develops and manages projects by iteratively generating prompts based on predefined goals. It leverages advanced language models to perform tasks such as market research, content creation, and strategic planning without continuous human intervention.

Key Benefits:

• Conducts comprehensive market analysis, providing insights into industry trends and competitor strategies.
• Produces high-quality content for blogs, social media, and marketing materials, saving time and resources.
• Assists in developing business strategies by analyzing data and forecasting potential outcomes.

Example: A startup can utilize Auto-GPT to generate detailed market reports, enabling informed decision-making and efficient allocation of resources.

3. HireVue – AI-Driven Recruitment Agent

HireVue is an AI agent that streamlines the recruitment process by automating candidate screening, assessment, and interview scheduling. It uses machine learning algorithms to evaluate candidate suitability, reducing the time and effort required in hiring.

Key Benefits:

• Quickly filters through resumes to identify candidates that match job requirements.
• Conducts initial assessments through AI-driven interviews, evaluating competencies and cultural fit.
• Manages interview scheduling, minimizing administrative tasks.

Example: A growing startup can implement HireVue to handle the influx of applications, ensuring a swift and effective recruitment process that secures top talent.

Dust – AI-Powered Workflow & Knowledge Management Agent

Dust is an AI agent designed to streamline workflows and enhance knowledge management for startups. It enables teams to build AI assistants that integrate seamlessly with internal tools like Notion, Slack, GitHub, and Google Drive, providing context-aware automation and intelligent data retrieval.

Benefits for Startups:

• Connects to company data sources, offering relevant insights in real-time.
• Automates repetitive tasks, reducing manual effort and improving efficiency.
• Centralizes knowledge, making information easily accessible across teams.

Example: A startup can use Dust to build a support assistant that pulls relevant documents from Notion, provides instant responses in Slack, and automates ticket resolution—saving time and improving team productivity.

5. Vigilant AI – AI Agent for Compliance and Risk Management

For startups navigating regulatory landscapes, Vigilant AI monitors compliance risks, automates legal documentation, and provides real-time alerts on policy changes. This is especially critical for fintech, healthcare, and AI-driven startups.

Key Benefits:

• Monitors and ensures compliance with industry regulations.
• Automates contract reviews and risk assessments.
• Reduces legal overhead and improves decision-making in regulatory matters.

Example: A fintech startup can integrate Vigilant AI to automate compliance checks, reducing legal risks and ensuring regulatory adherence.

Step- By- Step Guide to Build Custom AI Agents on MonsterAPI

While these AI agents provide significant advantages, every startup has unique requirements that may not be fully addressed by pre-built solutions. MonsterAPI offers a flexible platform to create customized AI agents tailored to specific business needs. In this guide, we will walk through how to build an AI-powered agent using MonsterAPI and LangChain. The agent will be able to process user queries, execute Python code dynamically, and remember conversation history.

Step 1: Install Dependencies

Before starting, ensure you have installed the required dependencies:

pip install langchain requests

Step 2: Import Required Libraries

To begin, we import the necessary modules from LangChain and standard Python libraries.

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.tools import tool
from langchain.tools.render import format_tool_to_openai_function
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.schema.runnable import RunnablePassthrough 
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.schema.agent import AgentFinish
from typing import Optional, Any
from langchain.agents.format_scratchpad import format_to_openai_functions

Step 3: Set Up MonsterAPI Credentials

To connect with MonsterAPI, we define the API key and base URL.

MONSTERAPI_KEY = "YOUR_API_KEY"
BASE_URL = "https://llm.monsterapi.ai/v1/"
MODEL = "deepseek-ai/DeepSeek-V3"

Step 4: Define a Code Execution Tool

We create a function that allows the AI agent to execute Python code.

@tool
def execute_code(code: str, result_var: str = "result") -> Optional[Any]:
    """
    Executes the given Python code and returns a variable's value.
    If an error occurs, returns an error message.
    """
    local_vars = {}
    try:
        exec(code, {}, local_vars)
        return local_vars.get(result_var, None)
    except Exception as e:
        return f"Error during code execution: {e}"

Step 5: Register the Tool

The agent needs access to the execute_code function.

tools = [execute_code]

This allows the agent to call execute_code() when needed.

Step 6: Create a Prompt Template

The AI agent needs a structured prompt to guide interactions.

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are an expert software engineer. Help the user with their queries. You can execute code as needed."),
        MessagesPlaceholder(variable_name="chat_history"),
        ("user", "{input}"),
        MessagesPlaceholder(variable_name="intermediate_steps"),
    ]
)

Step 7: Configure the AI Model

We initialize MonsterAPI’s LLM and bind the registered tool.

model = ChatOpenAI(
    openai_api_key=MONSTERAPI_KEY,
    openai_api_base=BASE_URL,
    model_name=MODEL
).bind(functions=[format_tool_to_openai_function(t) for t in tools])

Step 8: Create the AI Agent

Now, we define the agent chain, which processes queries and executes responses.

agent_chain = prompt | model | OpenAIFunctionsAgentOutputParser()
memory = ConversationBufferMemory(return_messages=True, memory_key="chat_history")
agent1 = AgentExecutor(agent=agent_chain, tools=tools, verbose=True, memory=memory)

Step 9: Accept User Input

The agent should take a user query, process it, and return results.

intermediate_steps = []
input_text = input("Enter your query: ")
r = agent_chain.invoke(
    {"input": input_text, "chat_history": [], "intermediate_steps": format_to_openai_functions(intermediate_steps)}
)

Step 10: Handle AI's Response in a Loop

The agent should keep processing responses until execution completes.

while type(r) != AgentFinish:
    o = globals()[r.tool].run(r.tool_input)
    intermediate_steps.append((r, o))
    
    if len(intermediate_steps) > 5:
        break
    
    r = agent_chain.invoke(
        {"input": input_text, "chat_history": [], "intermediate_steps": format_to_openai_functions(intermediate_steps)}
    )

Step 11: Print AI Response

Once execution is done, print the final result.

print(r.return_values["output"])

This AI agent provides a powerful coding assistant using MonsterAPI, making development faster and more interactive! 

Conclusion:

Integrating AI agents like NEO AI for machine learning automation, Auto-GPT for workflow automation, Dust AI for knowledge management, HireVue for hiring, and Vigilant AI for compliance can provide startups with a strong technological foundation. Platforms like MonsterAPI allow businesses to create customized AI agents that align with their operational needs. By leveraging AI-driven systems, startups can focus on innovation, scalability, and efficiency in an increasingly competitive market.