Solutions
Custom Built AI Agents for Production
Design, deploy, and scale intelligent AI agents that plan, reason, call tools, and execute multi-step tasks — powered by Qubrid's high-performance AI infrastructure.
Talk to our ExpertsThe Problem
Most AI Agents Work in Demos — Not in Production
Prototype agents often fail under real workloads due to model limits, tool failures, latency spikes, and missing orchestration controls.
Agents lack reliable tool execution
Most AI agents depend on external tools and APIs, but without structured tool routing, retries, and validation layers, failures cascade — leading to broken workflows and unreliable task completion.
No control over model cost and latency
Using a single large model for planning, reasoning, and execution drives up inference cost and response time, making agent systems too slow and expensive for real production workloads.
No tracing or step visibility
When agent decisions and tool calls are not traceable step-by-step, teams cannot debug failures, audit behavior, or optimize performance — a major gap for enterprise deployment.
The Solution
Build Reliable, Tool-Using AI Agents That Scale
Multi-Model Agent Stack
Use multiple planning, reasoning, and execution across different models for better cost and performance.
Tool & API Calling
Agents can securely call external tools, APIs, databases, and internal services.
Memory + RAG Integration
Retain agent's long-term memory and retrieval access for context-aware decisions.
Step Tracing & Logs
Track every agent step, tool call, and output for debugging and optimization.
Workflow Orchestration
Design multi-step agent workflows with conditional logic and human in the loop.
Production Deployment
Deploy agents on dedicated GPU with scalable versioning and environment controls.
Recommended Models
Models for AI Agent Systems
Optimized for multi-step planning, tool selection, and structured decision flows.
Introducing gpt-oss-120B, OpenAI's flagship open-weight model built for advanced reasoning, large-scale agentic workloads, and enterprise-grade automation. With 120B parameters and a highly optimized MoE architecture, it activates 12B parameters during inference — delivering exceptional intelligence for complex multi-step agents.
Qwen3-VL-30B-A3-Instruct is a large-scale vision-language instruction model designed for advanced multimodal reasoning. It delivers significantly stronger visual understanding, OCR accuracy, document reasoning, long-context comprehension, and agent-style interactions.
Qwen3-Coder-30B-A3B-Instruct is a sparse MoE model with ~30.5B total parameters (3.3B active per inference), supporting extremely long context up to 262K tokens — ideal for code-using agents and tool-driven workflows.
Don't let legacy AI stacks slow you down. Build Production AI the Qubrid Way.
Have questions? Want to deploy AI Agents at scale? Planning a larger AI rollout? Let our solutions team help.
“Qubrid enabled us to deploy production AI agents with reliable tool-calling and step tracing. We now ship agents faster with full visibility into every decision and API call.”
AI Agents Team
Agent Systems & Orchestration