Three Ways to Build AI Agents with OpenAI: How to Choose the Right Level of Abstraction

When building AI agents, your technology choices often determine the complexity of everything that follows. OpenAI offers three distinct paths for building agents, and picking the wrong level of abstraction means taking on unnecessary engineering overhead. These three approaches correspond to different levels of abstraction — from low-level control to fully managed — and understanding their differences is essential to making the right call.

Three Paths: From Low-Level to Fully Managed

At their core, OpenAI's three building approaches represent a tradeoff between control and convenience. The lower the level, the more control developers have — but the more engineering details they must handle themselves. The higher the level, the more of the runtime "plumbing" is handled by the platform, but with a corresponding reduction in flexibility and data control.

Responses API: Maximum Low-Level Control

The Responses API is the lowest-level path, built around a model plus hosted and custom tools. This approach gives developers the greatest degree of control — you interact directly with model calls and organize your own tool-use logic. It's well-suited for scenarios that require deep customization and freedom from framework constraints, but the tradeoff is that you must build more of the foundational logic yourself.

Agents SDK: Run the Loop Inside Your Own App

The Agents SDK runs the agent loop inside your own application. This means tool calls, agent-to-agent handoffs, state management strategies, and approvals are all under your control.

This model suits teams that want to retain full control over the execution flow without building everything from scratch. It strikes a middle balance between control and convenience — the framework handles the loop mechanics, but the key orchestration decisions remain in your hands.

The "agentic loop" is a core concept in agent architecture. It refers to the iterative process a model follows to complete a task: perceive inputs, decide whether to call a tool, receive the tool's output, incorporate that result into context, and continue reasoning — until the task is complete or a termination condition is met. Managing this loop involves considerable engineering detail: tracking conversation history, deciding when to truncate context, whether to retry on errors, and how to pass control between multiple agents (handoffs). The value of the Agents SDK lies precisely in abstracting and encapsulating this loop mechanism, so developers only need to focus on business logic — defining tools, writing handoff rules, configuring approval nodes — without implementing the loop scheduler from scratch.

Agents API: The Fully Managed Option

The Agents API sits at the other extreme: it runs a managed codex harness that provides durable sessions, orchestration, context compaction, and recovery out of the box. In other words, a large portion of the runtime capabilities that engineering teams would otherwise need to build themselves are handled directly by the platform.

The advantages are substantial: excellent tool integration, sandbox environments, mature multi-agent collaboration patterns, and extremely low runtime plumbing overhead. For teams looking to rapidly deploy complex, general-purpose agents, this is one of the fastest routes available.

The Tradeoffs to Consider

Convenience never comes free. The main tradeoff with the Agents API is coupling to the managed runtime — your application becomes deeply tied to OpenAI's hosted infrastructure.

More critically, there are data compliance constraints: the Agents API currently supports US data residency only and is not eligible for ZDR (Zero Data Retention). For enterprises with strict data control and compliance requirements, these two points may be hard blockers.

ZDR (Zero Data Retention) is a data-handling commitment OpenAI offers for enterprise compliance scenarios. Under ZDR, the content of requests and model responses is not retained by OpenAI for any purpose (including model training or uses beyond security review) — data is discarded immediately after processing. This is especially critical in highly regulated industries such as finance, healthcare, and law, where compliance requirements often explicitly prohibit sensitive information from being retained in third-party systems beyond what is strictly necessary. Because the Agents API does not currently support ZDR, data generated during the managed runtime process may be retained under OpenAI's standard policies. Enterprises should evaluate whether this aligns with their privacy agreements, regulatory obligations, or customer contract terms before proceeding.

How to Choose the Right Level

In summary, the three approaches reflect three distinct engineering philosophies:

• Responses API: For teams that want maximum control and are willing to build their own logic
• Agents SDK: For teams that want to retain orchestration control within their app while skipping the low-level loop implementation
• Agents API: For teams prioritizing fast deployment and minimal runtime overhead, where data compliance constraints are acceptable

The core question when choosing is simple: Are your data control requirements compatible with a managed solution? If data residency and retention policies are acceptable, the Agents API is one of the fastest paths to deploying complex, general-purpose agents. If not, you'll need to step back to the SDK or API level — trading additional engineering effort for greater control.

For teams planning their agent architecture, identifying your constraints upfront — compliance requirements, desired control granularity, and engineering resources — matters more than rushing to start building. Choose the right level of abstraction, and you'll avoid being bogged down by unnecessary engineering complexity down the road.