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.

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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.

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 and with scalable, versioning, and environment controls.

Recommended Models for AI Agent Systems

Optimized for multi-step planning, tool selection, and structured decision flows.

GPT-OSS 120B

Introducing gpt-oss-120B, OpenAI's flagship open-weight model in the gpt-oss series, built for advanced reasoning, large-scale agentic workloads, and enterprise-grade automation. With 120B parameters and a highly optimized Mixture-of-Experts (MoE) architecture, it activates 12B parameters during inference, delivering exceptional intelligence while maintaining competitive latency. Designed for complex reasoning, multi-task agents, and long-horizon planning, gpt-oss-120B brings frontier-level capability to commercial and self-hosted deployments.

Qwen3-Coder 30B A3B

Qwen3-Coder-30B-A3B-Instruct is a sparse Mixture-of-Experts (MoE) model with around 30.5B total parameters (3.3B active per inference), 48 layers, supporting extremely long context (native 262,144 tokens — extendable to 1M in some deployments).

Fara 7B

Fara 7B is a compact and efficient transformer model developed by Microsoft for high-speed inference, instruction following, text generation, and lightweight reasoning tasks. Its small parameter size allows easy deployment on consumer GPUs and edge devices while maintaining strong performance.

Mistral 7B

Mistral 7B is a 7.3B parameter language model celebrated for its efficiency, outperforming larger models on many benchmarks. The v0.3 instruct version is specifically fine-tuned for chat and instruction-following tasks.

Explore all models

Don't let legacy AI stacks slow you down. Build Production AI the Qubrid Way. Get in touch

Have questions? Want to deploy AI Agents at scale? Planning a larger AI rollout? Let our solutions team help.

Build Production Agents

"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