Computer AI Agents are software programs that can see what’s on your screen, understand it, and take action. Unlike traditional automation that requires specific API connections, these agents navigate web browsers, fill forms, and extract data from any interface by seeing and interpreting visual elements.
Think of them as automation that works like a human would - clicking buttons, typing text, reading what’s displayed - but without needing custom code for every application.
For conversational AI that works with text and documents rather than screens, see our BYO AI integration that connects ChatGPT, Claude, or Copilot subscriptions.
How workflow orchestration works with AI agents
Tallyfy can provide structure around AI agent execution. The workflow management system provides step-by-step instructions and defines inputs and outputs, while the AI agent handles the screen-based tasks. This separation gives you transparency into what the agent is doing and a framework for managing these automated steps as part of broader business processes.
Core capabilities of computer AI agents
These agents combine large language models with computer vision to interact with applications through their user interface:
Visual Perception: Can identify and interpret text, buttons, forms, and other UI elements on screen
Natural Language Instructions: Accept goals in plain English rather than requiring scripted code
Mouse and Keyboard Control: Execute clicks, typing, scrolling, and navigation actions
UI Adaptation: Can often handle interface changes that would break traditional RPA scripts
Start with simple tasks
AI agents work best with straightforward, repetitive tasks like filling specific form fields with known values. Complex, goal-driven work requiring significant decision-making can produce inconsistent results and high costs. Start small and expand gradually based on results.
Integration pattern with workflow management
What to notice:
Workflow system provides structured inputs (instructions, data, criteria) that guide the AI agent
Agent loops through perceive-understand-execute cycles until task completion
Outputs can be captured back for tracking and further processing
How this pattern works:
Define the process: Document your business process, identifying which steps humans perform and which could be handled by AI agents
Assign agent tasks: Steps involving web navigation, data extraction, or form filling can be assigned to AI agents
Provide instructions: The workflow system sends instructions and any needed data from previous steps
Monitor execution: Agent actions are logged for transparency and troubleshooting
Capture outputs: Results return to the workflow system for next steps
Refine over time: Adjust instructions based on results to improve reliability
Potential benefits and considerations
When successfully implemented, computer AI agents may provide:
Broader automation scope: Can work with applications that lack APIs or integration options
Reduced manual effort: Handles repetitive screen-based tasks that previously required human attention
UI resilience: Some ability to adapt when interfaces change, though not guaranteed
Process visibility: When orchestrated through workflow management, actions can be logged and tracked
Important limitations to understand:
Reliability varies: Success rates depend on task complexity, website structure, and vendor capabilities
Costs can scale quickly: Many vendors charge per task or execution time
Not deterministic: Unlike traditional code, agents may behave inconsistently
Still emerging: Vendor capabilities, pricing, and availability continue to evolve
The related articles below cover specific vendors and comparisons to help you evaluate if these tools fit your use case.
This complete guide explains how RPA handles structured repetitive tasks through rule-based automation while Computer AI Agents use artificial intelligence to adaptively use dynamic web environments and unstructured data with Tallyfy orchestrating both automation types within unified business processes.
OpenAI provides agent capabilities through tools like the Responses API and Agents SDK that integrate with Tallyfy to automate web interactions and document processing tasks by triggering webhooks and capturing structured outputs while routing failures to human review through conditional logic.
Skyvern is an open-source browser automation tool that uses LLMs and computer vision to execute web-based workflows through a multi-agent architecture and can integrate with Tallyfy via webhooks or middleware platforms to handle tasks like invoice management and form submissions while adapting to website changes in real-time.
Local Computer Use Agents run entirely on your own hardware to provide AI-driven automation with complete privacy and zero latency while Tallyfy orchestrates these agents through structured workflows that start with small focused tasks using efficient Small Language Models in the 270M-32B parameter range that handle mundane business automation like form filling and data extraction on standard laptops without expensive cloud API costs or data sovereignty concerns.
