Agentic systems push large language models beyond single-turn chat into autonomous loops of reasoning, tool execution, and memory retrieval. Unlike simple completion tasks, these workloads generate unpredictable token volumes. A single agent trajectory can involve multi-turn tool calls, extensive system prompts, and large context windows that inflate costs on traditional token-based inference providers. Building reliable agentic infrastructure requires more than a capable model. You need a backend that handles long contexts economically, exposes robust tool-use interfaces, and scales without cold-start latency.
Designing the Agent Architecture
Before writing code, define the boundaries of your agent. A production agentic workload typically decomposes into four components: a planner that breaks user intent into subtasks, a tool registry that exposes executable functions, a memory store that retains state across turns, and an executor that orchestrates the loop. The planner relies on an LLM with strong reasoning and instruction-following capabilities, while the executor depends on low-latency inference and reliable function-calling semantics.
Your model backend must support function calling, JSON mode, and multi-turn conversations. Without these primitives, the agent cannot reliably parse tool schemas or maintain coherent state across iterative steps. Streaming responses are also valuable, because they let you emit reasoning traces or partial results to the user while the agent continues internal processing.
Selecting the Inference Backend
Inference costs dominate the economics of agentic systems. On token-based providers, every tool result, system instruction, and historical message accrues input charges. Agent loops amplify this effect. A workflow that iterates ten times with lengthy context can generate bills that scale linearly with token count, even when the actual business value per step is small.
Oxlo.ai approaches this differently. It is a developer-first AI inference platform with request-based pricing: one flat cost per API request regardless of prompt length. For agentic and long-context workloads, this model removes the penalty of large inputs. Unlike token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, your cost does not scale with input length. In practice, this can make Oxlo.ai 10-100x cheaper for long-context workloads.
Oxlo.ai offers 45+ open-source and proprietary models across 7 categories, fully OpenAI SDK compatible, with no cold starts. For agentic deployments, several models stand out. Qwen 3 32B is built for multilingual reasoning and agent workflows. DeepSeek V4 Flash provides an efficient MoE architecture with a 1 million context window and near state-of-the-art open-source reasoning, making it ideal for agents that must retain extensive documentation or conversation history. Kimi K2.6 supports advanced reasoning, agentic coding, vision, and a 131K context window. GLM 5, a 744B MoE model, targets long-horizon agentic tasks, while Minimax M2.5 specializes in coding and agentic tool use. For general-purpose backbones, Llama 3.3 70B and DeepSeek R1 671B MoE deliver deep reasoning and complex coding capabilities.
Because Oxlo.ai is fully OpenAI SDK compatible, you can point your existing Python or Node.js client to https://api.oxlo.ai/v1 without rewriting your application logic.
Implementing the Tool-Use Loop
The core of an agent is the reasoning-acting loop. The LLM receives a user query and a list of available tools defined in OpenAI-style function schemas. If the model decides a tool is needed, it returns a structured function call. Your executor runs the tool, appends the result to the message history, and calls the model again. This repeats until the model produces a final answer.
Below is a minimal Python implementation using the OpenAI SDK against Oxlo.ai. It assumes an agent with a single search_knowledge_base tool.
import os
import json
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ["OXLO_API_KEY"]
)
tools = [
{
"type": "function",
"function": {
"name": "search_knowledge_base",
"description": "Retrieve documents matching a query",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string"}
},
"required": ["query"]
}
}
}
]
def search_knowledge_base(query: str) -> str:
# Production implementation calls your vector store or API.
return f"Results for: {query}"
messages = [
{"role": "system", "content": "You are a research agent. Use the search tool when needed
