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Abstract

As large language models evolve from chatbots into agents, the industry is undergoing a protocol shift.
What began as “chat completion” is moving toward response generation, and what looks like an API redesign is better understood as the emergence of a new generation layer in the AI stack.

This article examines:

  • Why OpenAI introduced the Responses API
  • Why conversation state was separated from execution
  • Why the community is now building OpenResponses
  • How this mirrors prior infrastructure standardization efforts such as OpenTelemetry, OCI, and Kubernetes

The thesis is that OpenResponses represents the natural next step: a vendor-neutral generation layer for agent systems.

Background: Chat Was Never the Right Abstraction

For years, the dominant interface to LLMs looked like this:

{
  "messages": [
    { "role": "system", "content": "..." },
    { "role": "user", "content": "..." },
    { "role": "assistant", "content": "..." }
  ]
}

This model worked well for:

  • Chatbots
  • Demos
  • Simple assistants

But it began to break down as soon as we asked LLMs to do more than talk.

From an engineering perspective, Chat Completion conflates multiple concerns:

Concern Chat Completion
Generation ✔️
Conversation state ❌ (caller-managed)
Tool calls Awkward
Multi-modal outputs Bolted on
Agent orchestration Fragile
Observability Poorly structured

In short:

Chat was a UI metaphor masquerading as a system interface.

The Shift: OpenAI Responses API

The introduction of the Responses API marks a fundamental reframing.

Instead of “chat,” OpenAI moved to a more general concept:

A response is the result of a generation request, not necessarily a message.

Conceptually, the Responses API is:

  • Stateless by default
  • Multi-modal by design
  • Tool-first
  • Event-structured
  • Agent-friendly

A single response can include:

  • Text and structured outputs
  • Tool calls and tool results
  • Multi-modal content (e.g., images)
  • Streaming events and intermediate states

In the OpenAI documentation, a response is a first-class object: requests provide input content and optional tools, while results return structured output items and streamable events. This framing makes the API suitable for agentic workflows that need predictable, inspectable execution traces.

This is not a chat API.

It is a generation primitive aligned with the Responses API object model, where a request yields a structured response with explicit inputs, outputs, and events.

Why Conversation Was Split Out

Alongside Responses, OpenAI separated conversation state from execution.

This separation is critical.

It only:

  • Stores items (messages, tool outputs, events)
  • Maintains ordering
  • Provides retrieval and replay

In other words:

Conversation is a persistence layer, not an intelligence layer.

This is a deliberate architectural decision:

flowchart LR
  C[Conversation State, Session, State Store] -->|retrieve/replay| R[Responses API, Execution Engine]
  R -->|append events| C

Why This Matters for Agents

Once you separate generation from conversation, something interesting happens:

You unlock agent architectures.

Agents require:

  • Deterministic tool invocation
  • Partial responses
  • Structured outputs
  • Retryable execution
  • Observability hooks
  • Context compression
  • Graph-based control flow

Responses API fits naturally here. Chat Completion does not.

Enter OpenResponses: The Community Response

This is where OpenResponses comes in.

OpenResponses is:

  • A community-driven open specification
  • Inspired by the Responses API model
  • Vendor-neutral by design

The goal is to standardize the response object schema, tool invocation semantics, and event structure so adapters and frameworks can interoperate across providers.

OpenResponses is not:

  • An OpenAI product
  • An OpenAI endpoint
  • A hosted service

Think of it as:

flowchart LR
  A[Responses API Implementation] --> B[OpenResponses Specification]

Why the Industry Needs OpenResponses

History repeats itself in infrastructure.

Proprietary Open Standard
Vendor observability SDKs OpenTelemetry
Docker runtime OCI
Cloud-specific APIs Kubernetes
ChatCompletion APIs OpenResponses (emerging)

The pattern is clear:

Once an abstraction becomes foundational, the ecosystem demands an open contract.

Responses are becoming that contract.

Technical Analogy: Where OpenResponses Fits

flowchart TB
  AF[Agent Frameworks LangGraph, CrewAI, A2A]
  GL[Generation Layer OpenResponses]
  PA[Provider Adapters OpenAI, Anthropic, Bedrock, DeepSeek]
  M[Models]
  AF --> GL --> PA --> M

Why This Is Especially Important for MCP and Agents

For systems using:

  • MCP (Model Context Protocol)
  • Tool servers
  • Multi-agent orchestration
  • Observability pipelines

A vendor-neutral generation layer enables:

  • Pluggable backends
  • Consistent telemetry
  • Safer migrations
  • Long-term maintainability

Context Compression and Long-Running Agents

Long-running agents introduce a new problem: context growth.

Responses-style APIs explicitly support:

  • Structured history
  • Event granularity
  • Context compaction

This is impossible to standardize cleanly on top of raw chat messages.

Future Outlook: From De-Facto to Open Standard

Today:

  • OpenAI Responses API is a de-facto reference implementation
  • OpenResponses is an emerging open specification

Tomorrow:

  • Multiple providers implement OpenResponses-compatible adapters
  • Agent frameworks target OpenResponses natively
  • Observability tools instrument response events uniformly

Key Takeaways

  1. Chat Completion was a UI convenience, not a system primitive
  2. Responses API is a true generation primitive
  3. Conversation state cleanly separates state from execution
  4. OpenResponses generalizes this model into a vendor-neutral spec
  5. Agents need this layer to scale, interoperate, and evolve

Final Thought

We are witnessing the emergence of a new layer in the AI stack:

The Agent Generation Layer

OpenAI’s Responses API shows what that layer should look like, OpenResponses is how the ecosystem ensures it belongs to everyone.

References