Agentic AI is moving from simple copilots to autonomous systems that plan, execute, and iterate. In these pipelines, an LLM acts as a controller: it reasons over a goal, selects tools, observes results, and adapts its next action. This shift changes the calculus of AI infrastructure. Agentic workloads generate long, stateful contexts and require reliable function calling, low latency, and cost predictability. Oxlo.ai is designed for this transition, with request-based pricing that does not scale with input length, no cold starts on popular models, and a broad catalog of reasoning and coding models that support tool use.
What Are Agentic Tasks?
An agentic task is any workflow where an LLM makes autonomous decisions across multiple steps instead of returning a single response. Classic examples include software engineering agents that edit files and run tests, research agents that query databases and synthesize reports, and operations agents that orchestrate APIs to resolve incidents.
These systems share a common loop:
- The model receives a system prompt, user goal, and available tool definitions.
- It reasons and emits structured tool calls.
- The application executes the tools and returns observations.
- The model incorporates the observations and repeats until the task is complete.
Each iteration appends new tokens to the conversation history. Over ten to fifty steps, the accumulated context can grow to tens or hundreds of thousands of tokens. Under token-based billing, this expansion makes costs unpredictable. Under a flat per-request model, the price of each reasoning step remains constant, which makes agentic architectures economically viable at scale.
Infrastructure Requirements for Agentic Workloads
Running agents in production imposes three strict requirements on inference infrastructure.
Long context windows. Agents need to retain system instructions, tool schemas, conversation history, and prior observations. Oxlo.ai offers models with extended context limits, including DeepSeek V4 Flash with 1M context and Kimi K2.6 with 131K context, so state does not get truncated mid-task.
Reliable tool use. Function calling must parse correctly on the first attempt. A malformed tool call breaks the loop and forces expensive retries. Oxlo.ai supports function calling and JSON mode across its chat models, including Qwen 3 32B, GLM 5, and Llama 3.3 70B.
Low latency and no cold starts. Agents often chain serial requests. A cold start on any step adds friction that compounds across the loop. Oxlo.ai serves popular models without cold starts, keeping iteration latency tight.
Key Model Capabilities
Not every model is equally suited to every agentic role. Oxlo.ai organizes its catalog so developers can route tasks to the right capability.
Reasoning and planning. For steps that require deep analysis or chain-of-thought reasoning, DeepSeek R1 671B, Kimi K2.5 Thinking, and GLM 5 provide strong performance on complex decision trees.
Coding and tool orchestration. When the agent must generate code, parse APIs, or manage structured outputs, Kimi K2.6, Minimax M2.5, Qwen 3 32B, and DeepSeek V3.2 are optimized for code generation and agentic tool use.
Long-context synthesis. For agents that ingest large documents or maintain very long conversation state, DeepSeek V4 Flash offers a 1M context window with efficient MoE architecture.
Vision. Some agents interact with graphical interfaces or parse images. Kimi VL A3B and Gemma 3 27B support vision inputs, so an agent can, for example, read a UI screenshot and decide which button to click.
Building an Agent with Tool Use
Because Oxlo.ai is fully OpenAI SDK compatible, you can build an agent with standard Python tooling and point it at https://api.oxlo.ai/v1. The example below shows a two-step agent loop using Qwen 3 32B.
import os
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ.get("OXLO_API_KEY")
)
tools = [
{
"type": "function",
"function": {
"name": "search_knowledge_base",
"description": "Search internal documents for a given topic",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string"}
},
"required": ["query"]
}
}
},
{
"type": "function",
"function": {
"name": "calculate",
"description": "Evaluate a mathematical expression",
"parameters": {
"type": "object",
"properties": {
"expression": {"type": "string"}
},
"required": ["expression"]
}
}
}
]
messages = [
{"role": "system", "content": "You are a research agent. Use tools to find facts, then perform calculations if needed."},
{"role": "user", "content": "What was our Q3 revenue, and what is the average monthly revenue if we assume 15% growth?"}
]
# Step 1: model decides to call tools
response = client.chat.completions.create(
model="qwen3-32b",
messages=messages,
tools=tools,
tool_choice="auto"
)
message = response.choices[0].message
messages.append(message)
# Step 2: execute tools and return observations
for tool_call in message.tool_calls:
if tool_call.function.name == "search_knowledge_base":
observation = "Q3 revenue was $12 million"
elif tool_call.function.name == "calculate":
observation = "4.6"
else:
observation = "Error: unknown tool"
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": observation
})
# Step 3: model synthesizes the final answer
final = client.chat.completions.create(
model="qwen3-32b",
messages=messages,
tools=tools
)
print(final.choices[0].message.content)
In this loop, the context grows with every tool result. On a token-based provider, the cost of the second and third requests would increase as the conversation history expands. On Oxlo.ai, each request incurs the same flat cost regardless of prompt length, so the agent can iterate without a linear cost penalty.
Cost Predictability and Request-Based Pricing
Agentic workloads are uniquely expensive under token-based pricing because they combine three cost drivers: large system prompts, lengthy tool schemas, and accumulated multi-turn history. A single agent session can consume more input tokens than a standard chat session by an order of magnitude.
Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For long-context and agentic workloads, this can be significantly cheaper than token-based alternatives because cost does not scale with input size. Teams can budget by request volume rather than by token count, and they do not have to truncate context aggressively to save money. For exact plan details, see https://oxlo.ai/pricing.
Model Selection for Agentic Pipelines
Sophisticated agents often use a router or supervisor pattern, where different models handle different subtasks. Oxlo.ai makes this practical because every model is accessible through the same OpenAI-compatible endpoint and the same request-based pricing structure.
| Role | Recommended Oxlo.ai Models |
|---|---|
| Planner / deep reasoning | DeepSeek R1 671B, GLM 5, Kimi K2.5 Thinking |
| Code generation / tool use | Kimi K2.6, Qwen 3 32B, Minimax M2.5, DeepSeek V3.2 |
| Long-document analysis | DeepSeek V4 Flash, Kimi K2.6 |
| Vision tasks | Kimi VL A3B, Gemma 3 27B |
| Fast routing / summarization | Oxlo.ai Coder Fast, Llama 3.3 70B |
Because all models support streaming, JSON mode, and multi-turn conversations through a unified API, you can compose heterogeneous agent graphs without managing multiple provider SDKs.
Conclusion
The next generation of AI is agentic. Systems will not just answer questions; they will complete objectives across multiple tools and long time horizons. This architecture demands inference infrastructure that tolerates long contexts, exposes reliable function calling, and keeps costs predictable as state accumulates. Oxlo.ai meets these requirements with flat per-request pricing, no cold starts, and a catalog of reasoning, coding, and vision models that support sophisticated agentic loops. If you are building autonomous pipelines, Oxlo.ai provides the API and cost structure to iterate without constraints.
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