The OpenAI Operator is an AI agent that utilizes a model known as Computer-Using Agent (CUA), which is capable of independently executing actions on the web through a virtual web browser. It possesses computer vision capabilities to identify page elements and decide what to do with them.

A CUA operates in a cycle of:

1. Perception — capturing the screen

2. Reasoning — planning a chain of thought

3. Action — clicking, typing, and scrolling

This perception-reasoning-action cycle allows the CUA to function as a specialized tool within larger automated workflows. In typical AI agent-based systems, a coordinating agent decides what needs to be done and then invokes specific tools—like the CUA for web interactions, or other services for data retrieval or file processing—to carry out each individual action. By pairing decision-making agents with execution tools, developers can create intelligent, automated processes in fields like data analytics, compliance operations, customer service, and more.

Agents in OpenAI Workflows

In common agentic workflows, AI agents interpret the user’s intent and choose which tools to call; each tool performs a specific task such as retrieving data, sending a message, or writing a file. These agents are the brains that decide when and how to use these vision-equipped operators.

What Agents Do

1. Task understanding – identify what the user needs

2. Planning and reasoning – select the sequence of operations, the most appropriate operators, and process errors

3. Execution control – call operators, give them parameters, and evaluate the results

4. Decision making – decide whether to keep going forward, try again, escalate, or stop a workflow based on the operator’s response

Suppose, if a user requests to “Find the latest AI research papers about Robotics and summarize them into a report”, the OpenAI agent will:

• Call a search-tool (e.g., an arXiv or Semantic Scholar API wrapper) to retrieve the latest robotics papers.

• Invoke a summarization tool to condense each paper’s key points.

• Use a document-generation tool to compile the summaries into a structured report (Markdown, PDF, PPT, etc.).

This orchestration is where automation intelligence shines, making agents essential for flexible workflows.

Types of Workflows

The design of workflows can be done in various ways based on the complexity of the task. Linear, branching, and parallel are the most common approaches.

#1 Linear Workflows

Linear workflows are just straightforward chains that have one task leading to another:

• The result of one step feeds into the next, and a single agent can operate multiple tools in sequence.

• Because tool calls run sequentially by default, steps don’t collide; any needed retry or back-off logic should be implemented inside the tool wrapper or surrounding code.

Operator flexibility: The agent can detect tool errors and decide to retry, skip, or escalate, but low-level timing and availability handling live inside your code, not the agent itself.

#2 Branching and Conditional Workflows: Conditional Decision-Making

Agents make different decisions based on real-time conditions.

• Evaluation - the agent assesses the situation and selects the optimal path based on the conditions

• Choice of possible results - different results lead to different actions

• Real-time adaptation - workflows are modified according to the existing data

• Path finding - agents do not use the regular routes

Operator flexibility: Monitors status, changes routes, responds immediately on demand, and processes decision trees with multiple variables.

#3 Parallel Workflows: Scaling Efficiency Through Concurrent Execution

Tasks are performed simultaneously to avoid time waste:

• Tasks are executed simultaneously - more than one agent is working on different portions together

• Central coordination - more than one agent is working on different portions together

• Fan-out and fan-in pattern - work is spread out and then returned altogether, and it’s quicker than performing all in a sequence

Operator flexibility: Balances workloads across agents, handles different completion times, and manages resource allocation to prevent bottlenecks.

#4 Multi-Agent Orchestration: Collaborative Networks and Handoffs

Specialized agents work together in multi-agent systems:

• Specialization - each agent is dedicated to handling a specific issue

• Triage system - the central agent assigns tasks to the most suitable sub-agent

• Modular structure - agents can be easily added, removed, or modified

• Shared intelligence - when agents work together, the system becomes smarter

Operator flexibility: Routes incoming requests, monitors agent availability, transfers cases between sub-agents, and scales the workforce to match demand.

Integration of Operators with Data Sources

External data sources can be integrated with OpenAI operators to provide them with additional knowledge. This enables intelligent, data-driven processes that solve problems with real-time data instead of relying solely on pre-trained data.

#1 Connecting to APIs and Live Data

The function tools allow operators to connect with external services and access real-time information by requesting information from APIs:

• Developers write code to call external APIs, like stock market data or weather services, to create basic functions.

• The Agents SDK auto-derives a JSON schema from each tool function’s signature so the agent knows the required parameters and expected output.

• The agent both parses the user request and decides which tool/API to call; the tool simply executes the request and returns results.

• Instead of depending on outdated training data, agents receive up-to-date information.

Example: Weather service for a user's particular location

• Location agent invokes the location API to locate the coordinates of the user.

• Forces the weather API to get the local forecast.

• The operators handle the API calls as well as the data flow coordination between them.

• End user receives precise and up-to-date weather at their specific location.

#2 Database Connectivity

It is also possible to design agents that communicate with data that is stored in proprietary databases. Here are common examples:

• Retrieval-Augmented Generation (RAG) process - operators search through internal data to find relevant information.

• Data processing and indexing - internal files and databases are arranged for quick searching.

• Smart search capabilities - the operators can locate the most useful information for each user query.

• Grounded responses - agents answer using the latest, most accurate company information.

#3 File and Document Repositories

Operators retrieve files from the cloud, such as Google Drive, S3, and SharePoint. It gives agents access to unstructured knowledge sources.

Example:

• A Knowledge Base Operator pulls FAQs or user manuals to assist in customer support.

#4 Real-Time Event Streams

Custom developer-written tools can subscribe to queues or webhooks and then pass incoming events back to the agent for reasoning. It helps to make low latency decisions using real-time signals.

Example:

• A Log Monitoring Operator listens for system alerts and triggers diagnostics.

• A Fraud Detection Operator checks payment events as they are received.

#5 Data Transformation & Enrichment

Operators clean and supplement data before sending it to agents, giving them complete and ready-to-use information for improved reasoning in their decisions.

Example:

• A Location Operator gets coordinates. It adds census information.

• A Product Operator calls multiple APIs, combines their outputs, and provides a single normalized view.

Managing Task Dependencies

Agents reason about task dependencies; tools carry out individual steps but do not manage the overall workflow. Below are key examples:

#1 E-commerce Order Processing

When customers place orders, operators execute tasks in sequence:

• Payment is verified before inventory is allocated.

• Complete inventory checks before creating shipping labels.

• Address validation is done before selecting the shipping method.

• All steps completed before sending confirmation email.

The operator prevents shipping preparations for failed payments and manages database connections efficiently across payment and inventory systems.

#2 Customer Support Workflow

Operators handle branching dependencies based on conditions:

• Ticket classification determines specialist team assignment.

• Customer tier affects response time requirements.

• Issue complexity decides escalation or automated resolution.

• Previous interactions influence approach and tone.

For example, operators send premium billing issues directly to senior specialists while providing technical teams with relevant context for other problems.

#3 Content Creation Pipeline

Marketing workflows are coordinated in parallel:

• Research tasks are carried out concurrently across the topics.

• The creation and captioning of images occur while the research is ongoing. Legal and technical reviews occur simultaneously.

• Final approval waits for both review types.

Operators optimize resource allocation, schedule expert time efficiently, and batch system updates to reduce load.

In summary, the common approaches for handling task dependencies can be illustrated as follows:

Open Source Alternatives to OpenAI Operator

While OpenAI provides a powerful, managed ecosystem for agentic development, the open-source community offers developers even greater flexibility, control, and transparency:

• LangChain and LangGraph are frameworks that let you create and orchestrate more complex agentic workflows based on open-source models and parallel agents.

• Developers can choose from a range of large language models (LLMs), such as GPT-5, Claude, or other open-source options. This flexibility allows them to utilize models based on their strengths, as well as manage decisions about where to deploy and how to operate them.

OpenAI's CUA vs. Open-Source Browser-Use

When selecting between a commercially managed solution like OpenAI’s CUA and an open-source framework like Browser-Use, we have to weigh the pros and cons of each option.

The table below compares OpenAI’s CUA and Open-Source Browser-Use.

When you want to scale the automations with Browser-Use or other framework, you can use Anchor Browser's cloud platform to provision unlimited concurrent browsers and robust execution of your code. Anchor allows you to focus on your automation, only paying for the executions without needing to setup and maintain expensive automation infrastructure at scale for production environments.

Final Thoughts

The Computer-Using Agent (CUA) is the AI model that OpenAI Operator relies on. It can observe the website, plan what to do, and use a virtual mouse and keyboard to interact with it. This lets it perform human-like tasks, such as booking hotel reservations or filling out forms.

Within a workflow, the agent decides and the tools execute. Operator is itself one such agent, not the generic executor layer. Agents play a key role in automation by understanding tasks, planning actions, and managing execution. Workflows may follow linear, branching, parallel, or multi-agent patterns.

While OpenAI provides a strong managed ecosystem, the open-source community offers tools like Browser-Use that give developers more control and flexibility in their browser automation. Regardless of platform, organizations must carefully integrate AI agents with human oversight and clear governance to realize their full potential while managing risks.

The future depends on collaboration between human skills and these increasingly capable AI systems.