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The Opus Agent is your go-to tool for AI-powered automation. Describe what you want in plain English, and it generates a step-by-step blueprint to accomplish the task. You can refine the blueprint, adjust the steps, or convert it to code for full control.

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Key Capabilities

Natural Language to Blueprint

Describe what you want to do, and Opus generates a step-by-step blueprint with the inputs and outputs it needs.

Iterative Refinement

Edit the generated steps, add new ones, remove what you don’t need, or regenerate with updated instructions.

Auto-Linked Variables

The Builder automatically connects outputs from previous tasks to inputs by matching names and types.

Switch to Code

Convert any blueprint to Python code for full control while keeping the same inputs and outputs.

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How The Blueprint Works

When you describe your task in the central description field, Opus Agent generates a blueprint—a series of Required Steps that break down your request into smaller actions. Each step has:
  • Objective: What the step aims to accomplish
  • Description: How the step will be carried out
After generation, you can:
  • Edit steps manually to fine-tune the logic
  • Add new steps if something is missing
  • Delete steps you don’t need
  • Regenerate by adding more instructions and clicking Generate again
The blueprint also auto-generates the Input and Output variables based on what your task needs. You can always adjust these manually.

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Operating Modes

Opus Agent offers two modes:
The fastest way to get started with AI tasks.How it works:
  1. Describe your goal in natural language
  2. The system generates a multi-step blueprint
  3. Input and output variables are auto-inferred
  4. Tweak the blueprint as many times as you need—your inputs and outputs stay intact based on your described task
Best for:
  • Content generation
  • Data extraction
  • Summarization and classification
  • Reliable processing that behaves as expected, even at enterprise scale

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Using Opus Agent

1

Add the task

Click the + button or drag an Opus Agent task into the builder.
2

Describe what you want

In the central description field, write what you want the task to do. For example: “Extract the customer name, invoice date, and total amount from the document.”
3

Generate the blueprint

Click Generate. Opus creates a blueprint with Required Steps, each having an Objective and Description. It also auto-generates the Input and Output variables.
4

Refine if needed

Review the steps. Edit, add, or delete them as needed. You can also add more instructions and click Generate again to update the blueprint.
5

Link variables

Use Auto Link to automatically connect inputs to matching outputs from previous tasks. You can also link them manually.
6

Preview and test

Run a preview with sample data to make sure outputs look right before activating the workflow.

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Best Practices

Define your input and output variable types at the start of your design.
  • Finalize output structures before building downstream logic
  • Regenerate the blueprint if IO changes materially
  • Test with representative data in preview mode
When downstream logic will branch or serialize data, use JSON mapped to Object variables:
  • Explicit schemas reduce parsing errors
  • Structured outputs are easier to validate
  • Type discipline improves workflow reliability
Always preview tasks with representative data before activation:
  • Check that output types render as expected
  • Verify variable mappings are correct
  • Test edge cases (empty inputs, long content)
The Switch to Advanced button converts your blueprint into an Advanced Task:
  1. Click Switch to Advanced
  2. Click Build to generate the full code from your blueprint
  3. Run the task using the built code
This gives you full control while preserving your inputs and outputs.

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Common Use Cases

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Document Summarization

Extract key points from lengthy documents: 
Input: document_text (Text)
Output: summary (Text), key_points (List)

Prompt: "Summarize the following document, extracting the
main thesis and up to 5 key points. Return as structured JSON."

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Data Extraction

Pull structured fields from unstructured text: 
Input: raw_text (Text)
Output: extracted_data (Object)

Prompt: "Extract the following fields from the text:
name, date, amount, category. Return as JSON matching
the output schema exactly."

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Classification

Categorize content into predefined buckets: 
Input: content (Text), categories (List)
Output: classification (Text), confidence (Number)

Prompt: "Classify the content into one of the provided
categories. Return the category name and a confidence
score between 0 and 1."

Custom Agent

Direct control over LLM selection and prompting.

Agentic Review

AI-powered review to validate and repair outputs.

Opus Code

Custom Python logic for deterministic transformations.

Builder Guide

Learn workflow design fundamentals.