Computer Ai Agents > RPA vs. computer AI agents
Computer AI agents
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.
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.
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.
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
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.
Vendors > OpenAI agent capabilities
Computer Ai Agents > Local computer use agents
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