Choose a Workflow Pattern
Select an agentic architecture that fits your use case. Each pattern defines how your AI agent reasons, acts, and iterates.
Start from a Template
Pre-built workflows for common use cases — Customer Support, Content Creation, Data Analysis, and more.
Neuro-Symbolic AI combines neural networks (pattern recognition) with symbolic reasoning (logic rules). Modern agentic patterns leverage this hybrid approach — the LLM "thinks" while structured tools and guardrails provide deterministic logic.
Configure Context Engineering
Define how your agent perceives and manages information. Context engineering is the foundation of reliable AI systems.
Context Engineering is the art of structuring what information an AI agent sees, when it sees it, and how it's formatted. Unlike static prompting, context engineering dynamically manages the agent's "working memory" across multiple turns and tool calls. Proper context engineering can reduce hallucinations by up to 90%.
Vector DB
Web Search
External API
Documents
SQL/NoSQL DB
Code Repository
Define Agent Role & Tools
Specify what your agent does, which tools it can access, and how it reasons through tasks.
Temporal Reasoning enables agents to understand time-dependent relationships — "what happened before", "what's happening now", and "what should happen next." This is critical for multi-step workflows where action order matters, such as data pipelines, approval chains, or iterative analysis loops.
Web Search
Code Execution
File I/O
API Call
Calculator
DB Query
Image Gen
Email
Set Governance & Guardrails
Define safety boundaries, compliance rules, and quality validation. Prompt governance is essential for production AI systems.
Prompt Governance is the organizational framework for managing, versioning, and auditing AI prompts at scale. It includes access control (who can edit prompts), version tracking (what changed and when), compliance mapping (which regulations apply), and quality gates (automated checks before deployment). Without governance, prompt drift can cause unpredictable behavior and compliance violations.
Hallucination Detection
Verify claims against source data before outputting
PII Redaction
Auto-detect and mask personal identifiable information
Content Safety Filter
Block harmful, biased, or inappropriate outputs
Output Validation Schema
Enforce structured output format with JSON Schema
GDPR
HIPAA
SOX
ISO 27001
EU AI Act
None
90% Cost Reduction Tip: Use smaller models (GPT-4o-mini, Claude Haiku) for reasoning steps and reserve powerful models (GPT-4o, Claude Opus) only for final validation. Implement caching for repeated queries and set strict token budgets per agent step. Most agentic workflows waste tokens on verbose intermediate reasoning that can be compressed.
Cost Estimator & Optimization
Estimate your workflow's real-world costs before deploying. Avoid surprise API bills.
Daily Cost
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Monthly Cost
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Yearly Cost
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Cost Breakdown
Test & Preview Your Workflow
Run simulated tests against your workflow configuration. Identify issues before deploying to production.
Sandbox Testing simulates your workflow execution using mock data and validates the configuration structure, guardrail compatibility, tool availability, and expected token usage. No actual API calls are made — this is a dry-run validation.
Test Queries
Simulated dry-run — no API calls made
Test Results
Your Workflow is Ready
Review the visual flow, save a version, then copy the config in your preferred format.
Version History
No saved versions yet. Click "Save Version" to create one.