Agentic systems are moving from research prototypes into production pipelines. Unlike simple chat completions, these workloads require models to reason across multiple steps, invoke external tools, and maintain state over long sessions. The infrastructure you choose must support unpredictable context growth, reliable function calling, and cost structures that do not penalize the very characteristic that makes agents useful: their ability to iterate and accumulate information. This guide covers the architectural patterns, model selection, and deployment practices needed to run agentic workload systems at scale, with concrete implementation details you can apply immediately.
What Defines an Agentic Workload
An agentic workload is characterized by an autonomous loop in which a language model generates a thought, selects a tool or action, executes it, and feeds the result back into the context window for the next iteration. This pattern, commonly implemented through ReAct or plan-and-solve architectures, differs from standard inference in three critical ways. First, the prompt length is not fixed. It grows with every tool call and observation. Second, the model must reliably emit structured output, typically JSON, to interface with external APIs. Third, the system is stateful across multiple turns, requiring the infrastructure to handle long-context windows without degradation.
Core Architectural Patterns
Most production agentic systems follow one of three patterns. The ReAct pattern interleaves reasoning traces with tool calls, allowing the model to observe intermediate results and adjust its plan. Plan-and-solve systems separate reasoning from execution: a reasoning model drafts a step-by-step plan, and worker models or functions carry it out. Multi-agent systems partition responsibilities across specialized models, such as a planner, a coder, and a verifier, coordinated through an orchestration layer.
Each pattern imposes distinct demands on the inference backend. ReAct requires low-latency streaming so users can see reasoning traces in real time. Plan-and-solve benefits from high-capacity reasoning models with large context windows. Multi-agent setups need diverse model capabilities, from vision understanding to code generation, often within the same request lifecycle.
Infrastructure Requirements for Production Agents
Running agents in production requires more than a chat endpoint. You need function calling that adheres to a strict schema, JSON mode for deterministic parsing, and vision capabilities when agents process screenshots or diagrams. Context windows must accommodate system prompts, tool definitions, conversation history, and observation buffers without truncation.
Reliability is equally important. Cold starts add unacceptable latency to iterative tool loops. If a user waits several seconds between agent steps, the system feels broken. Oxlo.ai serves popular models with no cold starts, so agent loops proceed without interruption. The platform is fully OpenAI SDK compatible, which means you can point your existing agent framework at Oxlo.ai by changing a single configuration value.
Model Selection for Agentic Tasks
Not every model handles agentic behavior equally. You need strong instruction following, robust tool-use formatting, and sufficient context length to retain state across iterations.
For deep reasoning and complex coding, DeepSeek R1 671B MoE and Kimi K2.6 are strong candidates. Kimi K2.6 also brings advanced agentic coding and vision capabilities with a 131K context window. Qwen 3 32B is purpose-built for multilingual reasoning and agent workflows, making it ideal for global deployments. For long-horizon tasks that require sustained coherence, GLM 5 (744B MoE) excels at agentic planning. Minimax M2.5 targets coding and agentic tool use, while DeepSeek V4 Flash offers a 1M context window with efficient MoE architecture for near state-of-the-art open-source reasoning.
Oxlo.ai hosts these models alongside 45+ others across seven categories, including dedicated code models like Qwen 3 Coder 30B and vision models like Kimi VL A3B. This breadth lets you route different agent steps to specialized backends without managing multiple providers.
Implementing a Tool-Using Agent
The fastest way to prototype is with the OpenAI SDK configured for Oxlo.ai. The following example shows a ReAct-style loop using Qwen 3 32B with function calling. The agent can search a knowledge base and calculate values before returning a final answer.
import openai
import json
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
tools = [
{
"type": "function",
"function": {
"name
Top comments (0)