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Vibe Check

Automation Guides

Learn how to build robust, production-ready agent workflows using vibe-check’s automation features.

Available Guides

Section titled “Available Guides”

Overview

Section titled “Overview”

Production agent automation requires careful orchestration, error handling, and state management. These guides cover:

  • Multi-stage workflows - Chain agents together with cumulative context
  • Iteration patterns - Retry, loop, and convergence strategies
  • Error resilience - Recovery, fallbacks, and graceful degradation
  • State management - Context sharing across workflow stages

Guide Details

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Building Workflows

Section titled “Building Workflows”

Create sophisticated multi-stage workflows that orchestrate multiple agents.

You’ll learn:

  • Defining workflow stages with wf.stage()
  • Passing cumulative context between stages
  • Accessing cross-stage data (files, tools, timeline)
  • Composing complex pipelines

Use cases:

  • Multi-step deployment pipelines
  • Code generation → review → fix workflows
  • Data processing pipelines
  • Progressive refinement automation

Key concepts:

  • Stage definitions and ordering
  • Cumulative context accumulation
  • Cross-stage data access
  • Workflow composition

Loop Patterns

Section titled “Loop Patterns”

Implement retry logic, iterative refinement, and convergence testing.

You’ll learn:

  • Using wf.until() for conditional loops
  • Implementing retry strategies
  • Testing for convergence
  • Iterative refinement patterns

Use cases:

  • Retry failed operations with backoff
  • Iterative code improvement
  • Converge to quality thresholds
  • Progressive enhancement workflows

Key concepts:

  • Condition checking
  • Loop termination strategies
  • Max iteration limits
  • State tracking across iterations

Error Handling

Section titled “Error Handling”

Build resilient workflows that handle failures gracefully.

You’ll learn:

  • Error detection and classification
  • Recovery strategies
  • Fallback mechanisms
  • Graceful degradation patterns

Use cases:

  • Fallback to simpler models on failure
  • Retry with different prompts
  • Skip failed stages with warnings
  • Aggregate partial results

Key concepts:

  • Try-catch patterns for stages
  • Error classification
  • Recovery decision trees
  • State preservation on failure
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Tutorials

Section titled “Tutorials”

API Reference

Section titled “API Reference”

Concepts

Section titled “Concepts”
Section titled “Other Guides”