Tools and APIs
Let the agent request queries and actions through tool calling. Your application validates arguments and executes authorised tools.
Use cases / Text and agents
AI agents
Build AI agents that connect your data with actions.
Create AI agents that retrieve information, use tools and help complete tasks. Connect language models with the APIs and processes your company already uses.
QDivZero lets you run LLMs that support tool calling and combine Retrieval and routing in your architecture. You define the tools and agent logic, and choose the model that best fits each task.
From answers to actions
Give the model access to the tools it needs.
Connect an LLM to data and tools to complete tasks. Your application coordinates execution and retains control.
Agents with your data
Retrieve context from your documents before deciding the next step. Limit sources according to the user and task.
Multi-step tasks
Coordinate multiple queries and actions to reach a goal. Retain state and define limits, errors and confirmations within your integration.
A model for each task
Compare models by reasoning, tool use and latency. Configure the selection each stage needs and evaluate the complete workflow.
Build it with QDivZero
Connect reasoning, tools, and actions.
Connect an LLM to context and tools. Your application validates arguments, executes authorised actions and retains state; the agent uses their results to prepare the next step.
Your application flow
Request
The user describes the task and desired outcome.
Reasoning
The LLM proposes an answer or a tool call.
Tools
Your application executes the authorized query or action.
Result
Return information to the agent to continue the task.
Response
The agent responds or begins the next process step.
Open-weight / Hugging Face
You can start with…
An agent needs both reasoning and reliable tool requests. Compare GLM, DeepSeek and MiniMax with your APIs, instructions and actual tasks to choose based on execution quality, latency and capacity. Include tools returning empty results or errors and tasks requiring clarification. Assess how the model continues after receiving application information, alongside its initial proposal.
Code and agents
GLM-5.3
For coding tasks and agents working through longer processes.
View model on Hugging FaceMultimodal reasoning
DeepSeek V4.1 Flash
Explore reasoning and understanding of text and images in multi-step tasks.
View model on Hugging FaceTool calling
MiniMax M2.7
For agents that use tools and complete multi-step tasks.
View model on Hugging FaceAI agents
AI agents: frequently asked questions
What is an AI agent?
An AI agent combines a language model with tools, data, and execution logic to complete tasks. It can interpret a request, seek information, and propose actions. Your application coordinates the steps and executes connected tools.
How do I build an AI agent with my own tools?
Choose a tool-calling model, define tools, and connect the APIs your workflow needs. Your application receives model calls, executes authorized actions, and returns results so the agent can continue. QDivZero serves the model through its endpoint.
What is the difference between a chatbot and an AI agent?
A chatbot focuses on conversation and answers. An AI agent adds tools and logic to query systems or complete actions. Start with a conversational assistant and extend its integration when you need multi-step task execution.
Can I build AI agents connected to company data?
Yes. Combine an agent with Retrieval and RAG to query internal documentation before answering or acting. Your application supplies tools and controls data access. QDivZero provides models, retrieval, and routing components for the architecture.
Which open-weight models support agents and tool calling?
Start by comparing GLM-5.3, DeepSeek V4.1 Flash, and MiniMax M2.7 on reasoning and tool-use tasks. Check runtime compatibility and evaluate calls your application needs. Results depend on the model, instructions, and available tools.
Can I use multiple LLMs inside an AI agent?
Yes. Assign models to different steps or use LLM Model Routing with configured intents, priorities, and destinations. Compare capabilities and organize agent tasks while keeping tools and logic in your application.
How do I control the actions an AI agent executes?
Define available tools and check each call in your application before executing it. Apply permissions, validate parameters and require approval for actions that need it. The model proposes a tool call; your integration decides what runs and how the result is communicated.
How much does deploying an AI agent cost?
It depends on the model, concurrent tasks and the number of steps each execution needs. Include retrieval and any external services the agent uses. Compare QDivZero pricing with expected volume and measure a complete task to size capacity, alongside the duration of an individual response.
Ready to give your AI the tools to act?
Build agents connected to your data, tools and services. Deploy the models your process needs in QDivZero and keep control of actions, context and application logic.