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Framework Design
Framework
Consumer vs Enterprise Use Case Taxonomy
Consumer software prioritizes speed, personalization, and low friction for individual users. Enterprise software prioritizes scale, control, and system integration, often sacrificing user experience. Agents like Operator must flex between these demands: adapting to ambiguity in consumer workflows, and ensuring precision across enterprise systems. The table below summarizes the key trade-offs.
Consumer vs Enterprise Taxonomy
Dimension
Consumer
Enterprise
Task Frequency
Low to Medium
High – Often repetitive
Variability
High – user preferences, goals, and edge cases vary widely
Medium – task structure is usually repeatable
Personalization
Critical – budget, preferences, brand loyalty
Less Critical – often templated for precision but may need role-based customization
Human-in-the-Loop (HITL)
Used for clarifying preferences or guiding behavior
Used for approvals, exceptions, or hand-offs
System Landscape
Primarily web apps, often with limited APIs
Mix of web, desktop, internal and external systems; APIs and non-API systems
Data Types
Structured (dates, prices) + unstructured (preferences)
Structured (tables, forms) + semi/unstructured (PDFs, emails, scanned docs)
Stakeholders
Individual user
Multiple personas: business users, IT, operations, leadership
Automation Challenges
Preference Understanding, Personalization, Understanding Variability
System interoperability, reliability, permissions, long-range orchestration
There are no rows in this table
Given the above framework, we took a simple approach to bucket the complexity of processes into a classic low, medium & high setup, since that will setup the base for our technical analysis:
L-M-H Framework
Key Criteria
Low Complexity
Medium Complexity
High Complexity
Systems Complexity
1 system
Applications with limited user interaction and minimal branching
1–3 systems
Moderate UI navigation and contextual input
3+ systems
Significant dependencies and multi-threaded flows
Number & type of actions
1 - 10 action
Mostly point-and-click, basic form fills, selections
10–20 actions
Some conditionality, data entry, comparisons, or cross-checking
20+ actions
File uploads, verifications, workflow approvals, external context management
Exception handling
Minimal
Retries or clarifications are straightforward
Medium;
May require clarification of preferences, alternative selections, or constraints
High;
System-level errors, verification failures, ambiguous or unstructured inputs that require escalation
Data types involved
Structured or lightly formatted (e.g., date, location, dropdown selection)
Structured and semi-structured
(e.g., price ranges, availability, user preferences)
Structured + unstructured
(e.g., PDFs, uploaded documents, ID verifications); more common in enterprise
Process readiness
Well-bounded and requires no orchestration
Elements may require sequencing or coordination across systems or timelines
Requires orchestration, contextual memory, or integration across multiple tools or personas
Level of risk
Low
Minimal financial or operational consequence if task is done incorrectly
Medium
May result in suboptimal outcomes or financial waste
High
Incorrect execution has financial, legal, or operational implications
There are no rows in this table
Analyzing Existing OOTB Use Cases for Operator
To ground our product and technical evaluation, we categorized OpenAI’s publicly demonstrated consumer use cases for Operator into the complexity framework outlined above. This helps establish a consistent basis to assess how well the current system performs across varying task types, and provides a point of comparison with other agentic products like Anthropic’s Computer Use and OpenAI’s own CUA model.
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