OpenAI provides agent capabilities through several tools: the Responses API, Agents SDK, and Computer Use (CUA) model. These work with Tallyfy to automate workflow tasks that involve web interactions, document processing, and data extraction.
OpenAI’s Operator (launched January 23, 2025) demonstrated browser automation using the Computer-Using Agent (CUA) model built on GPT-4o. The CUA model scored 38.1% on the OSWorld benchmark and 58.1% on WebArena - showing current limitations for complex automation.
More recently, OpenAI released the Responses API and open-source Agents SDK, which provide developers with tools for building AI agents that combine multiple capabilities including web search, file processing, and computer use.
Important guidance for AI agent tasks
Your step-by-step instructions for the AI agent to perform work go into the Tallyfy task description. Start with short, bite-size and easy tasks that are just mundane and tedious. Do not try and ask an AI agent to do huge, complex decision-driven jobs that are goal-driven - they are prone to indeterministic behavior, hallucination, and it can get very expensive quickly.
Tallyfy orchestrates OpenAI agent capabilities by triggering tasks, providing structured inputs, and capturing outputs. The integration uses webhooks or API calls to connect Tallyfy processes with OpenAI’s agent tools.
What to notice:
OpenAI’s agent platform includes several components that work together:
Responses API: Combines chat capabilities with multi-tool support, allowing agents to use web search, file search, and computer use within a single API call.
Agents SDK: Open-source toolkit for building single-agent and multi-agent workflows. Works with OpenAI models and competitor models from Anthropic and Google.
Computer Use (CUA): Browser automation powered by the CUA model. Captures screenshots, identifies UI elements, and simulates mouse/keyboard actions. Current performance is 38.1% on OSWorld benchmark.
Web Search: Integration with GPT-4o search providing factual accuracy of 90% on SimpleQA benchmark. Includes citations to original sources.
File Search: Document retrieval from large document sets with metadata filtering support.
Tallyfy provides structure for OpenAI agent automation:
Trackable execution: Every agent action happens within a Tallyfy process with full audit trail.
Error recovery: When agents encounter issues, Tallyfy’s conditional logic can route tasks to humans for completion.
Incremental approach: Start with simple tasks like form filling or data extraction. Monitor results. Expand gradually to more complex workflows.
MCP support: With Model Context Protocol servers, OpenAI agents can interact with Tallyfy using natural language commands like “complete the next task in customer onboarding.”
Operator uses the Computer-Using Agent (CUA) model built on GPT-4o architecture:
Visual perception: Captures screenshots of web pages and identifies UI elements like buttons, text fields, and links.
Action execution: Simulates mouse clicks and keyboard input to interact with websites.
Safety controls: Pauses for user approval before sensitive actions like payments or credential entry. Includes fine-tuning to resist prompt injection attacks.
Performance benchmarks:
Best suited for:
Limitations: Struggles with complex interfaces, multi-page workflows requiring sustained context, and websites with anti-bot measures.
ChatGPT subscription plans:
API access: Available through Responses API and Agents SDK
Geographic availability: United States, Canada, UK, Australia
Note: Features and availability change frequently. Check OpenAI’s official documentation for current status.
Identify suitable tasks Look for Tallyfy tasks that involve web interactions - online ordering, booking appointments, data extraction from public websites.
Write clear instructions Create specific natural language instructions in task descriptions. Include exact URLs, button names, and expected outputs.
Choose integration method
Test incrementally Start with simple, low-risk tasks. Monitor success rates. Adjust instructions based on results before expanding to more complex workflows.
Implement error handling Use Tallyfy’s conditional logic to route failed or partially completed tasks to human review.
Scenario: Automate restaurant reservations for client meetings.
Tallyfy task configuration:
Agent execution:
Result handling:
Responses API: Send task instructions and data directly to OpenAI’s API. Agent uses available tools (web search, computer use, file search) to complete the task.
Agents SDK: Build custom workflow orchestration using the open-source SDK. Supports multi-agent workflows and mixed model usage.
MCP server: Create a Model Context Protocol server that exposes Tallyfy operations. OpenAI agents can then interact with Tallyfy using natural language.
Webhook architecture: Use message queues between Tallyfy and OpenAI to handle high-volume automation with retry logic and parallel processing.
Invoice data extraction:
Customer onboarding tasks:
Competitive research:
Task instructions: Write specific, clear instructions. Include exact URLs, button names, field labels, and expected outputs.
Good: “Navigate to acme.com/invoices, download PDFs from last 30 days, extract invoice numbers and amounts” Poor: “Get the invoices”
Security: Never store passwords in task descriptions. Use secure credential management. Enable user approval for sensitive actions.
Error handling: Configure Tallyfy conditional logic to route failed tasks to humans. Log agent actions for debugging.
Monitoring: Track success rates by task type. Adjust instructions based on failure patterns.
Current OpenAI API pricing (subject to change):
Check OpenAI’s official pricing page for current rates and subscription plan details.
Task fails repeatedly:
Integration not triggering:
Partial completion:
Current constraints:
Best practices:
Integrations > Computer AI agents
Computer Ai Agents > AI agent vendors
Mcp Server > Using Tallyfy MCP server with ChatGPT
Computer Ai Agents > Local computer use agents