OpenAI Agents SDK vs Dust: Build from scratch or use infrastructure?

Davis Christenhuis

-February 27, 2026

The OpenAI Agents SDK packages multi-agent coordination into a lightweight framework. You get agent loops, handoffs between specialized agents, guardrails for validation, and built-in tracing—the building blocks for agentic workflows.

Building production agents means adding infrastructure on top: data connectors, sync mechanisms, search pipelines, and deployment. This guide compares building that infrastructure yourself against using Dust's managed platform, and when each approach makes sense.

📌 TL;DR

Here's a short summary of the OpenAI Agents SDK article:

What the SDK gives you: Agent loops, handoffs between specialized agents, guardrails for validation, sessions for memory, and built-in tracing — the building blocks for agentic workflows
What comes after orchestration: Connecting agents to company data means building authentication, sync systems, search pipelines, and deployment on top of the SDK. That infrastructure work is where most of the time goes.
How Dust fits in: Dust provides that infrastructure layer as a managed platform — pre-built connectors, automatic synchronization, cross-source search, and a no-code agent builder for both technical and non-technical teams.
The team deployment problem: Building infrastructure once is manageable. Building it for every team that needs agents — customer support, data, people ops — means duplicating work across the organization.
When to use each: Use the SDK when multi-agent orchestration is your core product feature. Use Dust when multiple teams need agents without each building their own infrastructure.

What is the OpenAI Agents SDK?

The OpenAI Agents SDK is a framework for building multi-agent systems using a small set of primitives, available in both Python and JavaScript/TypeScript. It provides agents (LLMs with instructions and tools), handoffs (delegation between agents), and guardrails (input/output validation) as the building blocks for agentic workflows.

You define specialized agents for different tasks and configure how they delegate work to each other. The built-in agent loop handles tool invocation and model interaction automatically, running until the task completes or hits a turn limit. This abstracts away low-level coordination code so you can focus on orchestration patterns.

The framework works with OpenAI models through the Responses API and supports other providers through the Chat Completions API. LiteLLM integration extends compatibility across additional model providers. Built-in tracing visualizes agent behavior during execution and connects to external monitoring platforms for production observability.

Key features of the OpenAI Agents SDK

The OpenAI Agents SDK provides orchestration capabilities with minimal abstractions:

Built-in agent loop: Automatic coordination that handles tool calls, sends results back to the model, and continues until the agent produces final output or reaches a turn limit.
Handoffs for delegation: Specialized agents can transfer control to other agents, creating modular workflows where each agent handles one domain.
Guardrails for validation: Run input and output checks alongside agent execution — either in parallel for speed, or in blocking mode for strict fail-fast validation.
Function tools with automatic schemas: Convert Python functions into tools the agent can call, with Pydantic validation and automatic parameter schema generation.
Sessions for persistent memory: Built-in conversation history management that maintains context across agent runs without manual message handling.
MCP server integration: Call tools from Model Context Protocol servers the same way you use function tools, enabling standardized tool access.
Human-in-the-loop support: Pause agent execution to request human input, approval, or intervention at any point in the workflow.
Built-in tracing: Track agent runs with OpenAI's tracing interface or export to external platforms like Logfire, AgentOps, Braintrust, and Scorecard.
Provider flexibility: Works with OpenAI's Responses and Chat Completions APIs, plus other LLMs through integrations like LiteLLM.

What is Dust?

Dust is an AI platform that provides managed infrastructure for building and deploying enterprise AI agents. The platform centers on a no-code/low-code agent builder with full API access that lets teams configure and deploy agents without writing orchestration code. You select data sources, write instructions in natural language, and choose which model to use—all through a visual interface.

The platform handles data synchronization, permission mapping, and deployment automatically. This means both technical and non-technical team members can build and modify agents without writing orchestration code. Customer support builds help desk agents that search past tickets and product documentation. Data teams deploy analytics assistants that query data warehouses and surface insights from research reports.

People ops create onboarding agents that reference policies, benefits guides, and training materials. Each team builds what they need using shared infrastructure and centralized security policies.

Key features of Dust

Dust provides production infrastructure for multi-agent deployments:

Pre-built data connectors with authentication handled: Connect to Slack, Notion, GitHub, Google Drive, and other tools without building OAuth flows, managing credentials, or writing sync logic yourself.
Automatic data synchronization: The platform monitors connected sources for changes and updates agent context automatically, eliminating the need to build polling systems or webhook handlers.
Context-aware search across all sources: Query company data using semantic search that works across multiple tools simultaneously, with relevance ranking and permission filtering built in—no vector database infrastructure to maintain.
No-code agent configuration: Define agent instructions, select data sources, and adjust behavior through a visual interface, letting both technical and non-technical teams deploy agents without writing orchestration code.
Space-based permission management: Organize data into open or restricted Spaces, so agents only access data their users are authorized to see
Cross-team infrastructure sharing: Deploy specialized agents for different teams—customer support, data analytics, knowledge management—all using shared connectors and security policies without duplicating infrastructure.
Enterprise-grade security: GDPR compliant and SOC 2 Type II certified, with HIPAA-enabling configuration that safeguards company data and workspace privacy without building security infrastructure yourself.
Multi-model flexibility: Switch between OpenAI, Claude, Gemini, and other providers per agent through configuration, avoiding vendor lock-in at the infrastructure level.

💡 Want to deploy agents across teams without building connectors? Try Dust free for 14 days →

How Dust eliminates the infrastructure work the SDK requires

The OpenAI Agents SDK handles agent coordination—how agents hand off tasks, call tools, and work together. It doesn't handle the infrastructure needed to connect those agents to your company's data.

The infrastructure gap:

After you build agent logic with the SDK, you still need to connect agents to Slack, Notion, and GitHub. That means writing authentication for each tool, building systems that sync data as it changes, creating search that works across all your sources, and deploying everything securely. The orchestration logic might take a week. The infrastructure takes months.

How Dust works differently:

Dust provides the infrastructure as part of the platform. Teams configure agents through a visual interface, select which data sources to connect, and deploy without writing connector code. When company data updates, Dust syncs it automatically. The Dust team maintains connectors when third-party APIs change, so you don't manage integration code yourself.

Watershed took this approach when they needed agents to work across teams — from sales ops workflows synced with Gong and Salesforce, to engineering design documentation and performance review coaching in People. Rather than building and maintaining connectors for each use case, they used Dust to scale company-wide AI adoption from 20% to 90%.

When to use each:

Use Dust when multiple teams need agents without each building their own infrastructure. Use the SDK when multi-agent orchestration is your core product feature or when you're adding agent capabilities to existing infrastructure you already maintain. These tools can also be complementary — the SDK for custom product features, Dust for company-wide agent deployment across teams.

💡 See how other teams deploy agents. Read more customer stories →

Frequently asked questions (FAQs)

What's the difference between agent orchestration and agent infrastructure?

Agent orchestration refers to the coordination logic between agents—how agents hand off tasks, invoke tools, and manage conversations. The OpenAI Agents SDK provides these orchestration primitives. Agent infrastructure refers to the operational layer around orchestration—data connectors, authentication, synchronization, search, permissions, and deployment. While the SDK handles orchestration, infrastructure requires additional development to connect agents to company data and deploy them in production. Dust provides both layers: you configure agents through a visual interface while the platform handles infrastructure.

How does Dust handle multi-agent workflows compared to the OpenAI Agents SDK?

The OpenAI Agents SDK gives you programmatic control over multi-agent coordination through handoffs and agent-as-tool patterns. You write code that defines when agents delegate work to each other and how they share context. Dust handles multi-agent orchestration through configuration rather than code—you define specialized agents in the builder and configure how they work together using the same data sources and permissions model. Both approaches support complex workflows, but Dust eliminates the need to write and maintain orchestration code while the SDK gives you maximum flexibility for custom coordination patterns.

Can non-technical teams use agents built with the OpenAI Agents SDK?

Agents built with the OpenAI Agents SDK require developers to modify code, redeploy, and manage infrastructure when changes are needed. Non-technical users can interact with deployed agents but can't adjust instructions, change data sources, or configure behavior themselves. Dust's agent builder lets both technical and non-technical team members configure agents, select data sources, and modify instructions through a visual interface without code changes or redeployment. This means teams can adapt agents to their needs without waiting for engineering capacity.