Beyond "Chat Completions": Open Responses, the Universal Standard for Agentic AI
TL;DR:
The industry is moving from stateless "Chat Completions" to stateful _Agentic Loops_. The Open Responses standard formalizes this by offloading control flow to the inference engine, treating reasoning as a first-class stream, and establishing a vendor-neutral protocol for sub-agent orchestration.
1. The Problem: Client-Side Orchestration
The current "Chat Completions" paradigm relies on brittle client-side loops. To build a reliable agent today, you are effectively forced to write a custom runtime. You verify intent, prompt the model, wait for a raw text stream, parse regex or JSON, execute tools locally, and then re-prompt with context.
This "Ping-Pong" architecture introduces critical bottlenecks:
- Latency: Every tool execution requires a full network round-trip.
- Fragility: You are writing regex parsers for non-deterministic token streams.
- Fragmentation: Switching from OpenAI (which hides reasoning) to DeepSeek (which exposes it) requires rewriting your entire orchestration layer.
2. What is Open Responses?
Open Responses: A Universal Agentic Inference Standard that shifts the Agentic Loop (reasoning → tool execution → observation) from the client application to the Model Provider.
It is not just an API spec; it is a Protocol for Autonomy. It defines how a system should "think" and "act" over time, rather than just how it should "talk" in a single turn.
3. The Evidence: The Sub-Agent Loop
The standard defines a server-side "Sub-Agent Loop". Instead of managing the control flow in Python/TypeScript, you treat the API as a stateful inference engine that autonomously iterates until a halt condition (max_tool_calls or final answer) is met.
1. Server-Side Control Flow
Delegated Autonomy. By passing max_tool_calls, you authorize the provider to manage the intermediate reasoning and execution steps. This reduces round-trip latency and allows the model to error-correct its own parameters within the inference window.
2. Typed "Items" Schema
Structured State Management. The protocol replaces unstructured text streams with a strict schema of Items that map to the agent's cognitive state. This ensures strict interoperability:
MessageItem: Standardizes context history.ToolCallItem: Strongly-typed execution requests (eliminating regex parsing).ReasoningItem: A dedicated channel for Chain-of-Thought (CoT) traces.1
3. Semantic Streaming
Event-Driven Inference. The protocol emits discrete Semantic Events rather than raw tokens. This allows your UI to distinguish between internal state (thinking) and external communication (speaking):
# The "Old Way": Parsing distinct blobs of text
# The "New Way": Listening for Semantic Events
async for event in client.stream(input="Analyze Q3 sales", tools=[draw_chart]):
if event.type == "response.reasoning.delta":
# Render "Thinking..." UI or log trace
logger.debug(f"Thinking: {event.delta}")
elif event.type == "response.tool_call":
# Agent decided to act
ui.show_action("Executing Tool...")
elif event.type == "response.text.delta":
# Final answer to user
print(event.delta, end="")
4. The Mental Model: Inference as an OS
Legacy LLMs are Text Generators. They predict the next token based on a context window. They are unaware of "tools" or "plans"—these are abstractions we hacked on top of text completion.
Open Responses is an Agentic Operating System. It manages the "process" (the agentic loop), allocates resources (context window, tools), and handles I/O (tool calls/results). As an engineer, you stop writing the "scheduler" for the model and start treating the API as a standardized runtime that executes your agentic workloads.
5. Why the Shift?
- Obsolescence of "Chat": The "Chat" abstraction fails to capture the complexity of multi-turn agentic reasoning. It treats tool-use as a side effect rather than a core primitive.
- The "Black Box" Problem: As models like o1 and DeepSeek-R1 advance, reasoning becomes the product. Open Responses standardizes Visibility Tiers—allowing models to expose raw traces (for open science), encrypted traces (for enterprise audit), or summaries (for UX) without breaking the client.2
- Vendor Lock-in: Proprietary SDKs create silos. If you build on OpenAI's Assistants API, you cannot easily swap to Anthropic. With Open Responses, you write your agent logic once and run it on any compliant provider—whether it's GPT-4o, Claude 3.5, or a local DeepSeek-R1 via Ollama.
6. Implementation Strategy
Adopt the Universal Inference Protocol for model-agnostic agents.
- Update Dependencies: Install the Python SDK (udapy/openresponses-python) to use strict Pydantic models for the protocol.
- Refactor Client Logic: Replace custom
whileloops with theAsyncOpenResponsesClientto offload orchestration. - Consume Semantic Events: Update your stream handlers to distinguish between
reasoning.delta(for debugging/logs) andtext.delta(for UX). - Standardize Inputs: Use
MessageItemandToolCallItemto define your agent's state. This ensures your agent is portable—you can prototype on a local LM Studio instance and deploy to a cloud provider without changing a single line of code.
References
Open Responses Team. (n.d.). Open Responses Specification: A Universal Agentic Inference Standard. GitHub Repository. Retrieved from https://github.com/openresponses/openresponses
Unofficial Open Responses Python SDK. (2026). Unofficial Open Responses Python SDK: A Reference Implementation. GitHub Repository. https://github.com/udapy/openresponses-python
Smith, S., Burtenshaw, B., Merve, & Cuenca, P. (2026, January 15). Open Responses: What you need to know. Hugging Face Blog. https://huggingface.co/blog/open-responses