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Workflows as Apps

OVERVIEW

Traditionally, workflows were designed as backend infrastructure—used primarily for data movement, system integration, and complex automation behind the scenes. Their primary role was to orchestrate.

As workflows move closer to everyday business operations, their role is expanding. Today, workflows are increasingly used by non-technical knowledge workers who rely on automation to complete routine, outcome-driven tasks as part of their daily work.

MY ROLE

Lead the project & designed multiple app interfaces for different output operators on workflow tool.

TIMELINE

Jan 2025- Jun 2025

DOMAIN

AI & ML

Introducing Workflow Apps

As workflows shift from backend infrastructure to everyday operational tools, they require a new interface model—one that prioritizes outcomes over configuration.

Workflow Apps provide this missing layer.

Instead of exposing logic, conditions, or data flow, apps present workflows as simple, task-oriented experiences. Each app is designed around a specific outcome, allowing knowledge workers to trigger, monitor, and reuse automation without understanding how it is built.

As workflows shift from backend infrastructure to everyday operational tools, they require a new interface model—one that prioritizes outcomes over configuration.Workflow Apps provide this missing layer.

Instead of exposing logic, conditions, or data flow, apps present workflows as simple, task-oriented experiences. Each app is designed around a specific outcome, allowing knowledge workers to trigger, monitor, and reuse automation without understanding how it is built.

As workflows shift from backend infrastructure to everyday operational tools, they require a new interface model—one that prioritizes outcomes over configuration.Workflow Apps provide this missing layer.

Instead of exposing logic, conditions, or data flow, apps present workflows as simple, task-oriented experiences. Each app is designed around a specific outcome, allowing knowledge workers to trigger, monitor, and reuse automation without understanding how it is built.

How Apps Are Generated

Workflow apps are not manually designed or assembled. They are generated automatically once a workflow defines a clear Output Operator.

The Output Operator acts as a contract between the workflow and the end user. It specifies:

• The structure of the output
• The interaction model required to consume it
• The guarantees the system can provide (format, reliability, repeatability)

Workflow apps are not manually designed or assembled. They are generated automatically once a workflow defines a clear Output Operator.

The Output Operator acts as a contract between the workflow and the end user. It specifies:

• The structure of the output
• The interaction model required to consume it
• The guarantees the system can provide (format, reliability, repeatability)

Workflow apps are not manually designed or assembled. They are generated automatically once a workflow defines a clear Output Operator.

The Output Operator acts as a contract between the workflow and the end user. It specifies:

• The structure of the output
• The interaction model required to consume it
• The guarantees the system can provide (format, reliability, repeatability)

Types of Output Operators:

These behaviors led to the following system-level decisions:

These behaviors led to the following system-level decisions:

Tabluar output Operator

Tables with sorting, filtering, and pagination

Long-Form Text Operator

Structured text with sections

Search App Operator

Retrieve and rank information across sources.

Q&A Chat app Operator

Enable iterative questioning and exploration.

Design Principles & Guardrails

1. Outcome Over Configuration

Apps expose only what is necessary to achieve an outcome. Workflow logic, conditions, and orchestration remain hidden by default.

2. Safe Interaction by Default

Knowledge workers can Trigger workflows, Provide inputs, Consume results. They cannot Modify logic, Break dependencies, Affect system integrity.

  1. Clear Ownership Boundaries

Builders own logic, structure, and evolution of workflows. Knowledge workers own execution and outcomes. This separation prevents accidental misuse while enabling scale.

  1. Reuse Over Reinvention

Apps are designed to be Reusable, Shareable, Reliable over time. Encouraging workflows to function as internal tools, not one-off automations.

  1. Tabular Output Apps

Structured data, made usable

Tabular Output Apps surface workflow results as clean, scannable tables—optimized for review, filtering, and export. By presenting structured data in a familiar format, they allow knowledge workers to quickly interpret results and act on them without understanding how the data was generated.

Interface generated via Tabular Output Operator

  1. Long-Form Text Apps

Complex reasoning, delivered as readable output

Long-Form Text Apps transform multi-step workflows into coherent written outputs. They present structured narratives that users can read, review, and reuse—removing the need for prompt engineering or manual synthesis while preserving clarity and intent.

Interface generated via Long-Form text Output Operator

  1. Search Apps

Ask once, explore across systems

Search Apps convert workflows into fast, query-driven experiences. Users enter a single input and receive ranked, relevant results aggregated from connected sources—making discovery intuitive while keeping retrieval logic hidden.

Interface generated via Search App Output Operator

  1. Q&A Chat Apps

Explore answers through conversation

Q&A Chat Apps enable iterative interaction with workflows through a conversational interface. They maintain context across turns, allowing users to ask follow-up questions and refine understanding without navigating configuration or logic.

Interface generated via Q&A Chat App Output Operator

IMPACTS

Apps generated through the workflow builder enabled the MarkovML team to create targeted, vertical solutions for Marketing and Sales, empowering knowledge workers to achieve outcomes without engaging with underlying complexity.

LEARNINGS

Vertical solution is the future. Nature of AI is such that, it's way more powerful and effective when applied to specific context.

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