a8n
AI-native workflow automation platform combining a visual React Flow DAG editor, autonomous LangGraph agents, durable Inngest execution, multi-provider AI nodes, secure credential management, and standardized MCP Apps for AI-driven automation.
a8n is an AI-native workflow automation platform built around a visual DAG editor, autonomous LangGraph agents, durable Inngest execution, and standardized MCP Apps. Humans can design workflows visually while AI agents can generate, debug, approve, and execute the same workflows programmatically through MCP.
“Humans design workflows visually. AI agents build, debug, and operate them programmatically. One execution engine connects both worlds.”
SYSTEM
ARCHITECTURE
Engineered for production durability, type safety, and scalable domain isolation. Every module operates with strict boundaries and predictable failure handling.
MODULAR DOMAIN ISOLATION — BUILT FOR ZERO-DOWNTIME DEPLOYMENTS, TYPE-SAFE CONTRACTS, AND FAIL-SAFE EXCEPTION BOUNDARIES.
PROJECT
IMPACT
Production performance measured under live traffic load, latency stress profiles, and automated system profiling.
ALL OUTCOMES ARE EMPIRICALLY VERIFIED IN PRODUCTION ENVIRONMENTS — MEASURED UNDER HIGH CONCURRENCY, PEAK LATENCY PROFILES, AND AUTOMATED SYSTEM HEALTH AUDITS.
MCP Tools
Workflow Nodes
Realtime Channels
MCP App Widgets
KEY
CAPABILITIES
Core capabilities engineered for high-scale reliability, intuitive operator workflows, and real-time production execution.
PRODUCTION-TESTED SYSTEM FEATURES — ARCHITECTED FOR LOW-LATENCY INTERACTION, DETERMINISTIC STATE HANDLING, AND COMPREHENSIVE TELEMETRY.
Visual DAG Editor
A React Flow powered workflow canvas lets users visually compose automation pipelines using custom trigger, AI, HTTP, and integration nodes. DAGs are validated and topologically sorted before execution.
Autonomous LangGraph Agent
The built-in agent transforms natural-language goals into workflow drafts, diagnoses failed executions, suggests repairs, manages credentials, and uses dual memory for contextual assistance.
Standardized MCP Apps
a8n implements the official ext-apps SDK to turn MCP tool outputs into interactive micro-frontends. Widgets support streaming input, validation states, server-tool calls, execution timelines, and workflow approval flows.
Durable Workflow Execution
Inngest handles event-driven execution with retries, step isolation, failure recovery, context propagation, and realtime execution updates.
Multi-Provider AI Automation
AI workflow nodes support OpenAI, Anthropic, and Google Gemini through the Vercel AI SDK, allowing workflows to combine different model providers while keeping credentials securely scoped.
AI Safety & Human-in-the-Loop
Sensitive agent actions are protected through prompt-injection detection, semantic safety checks, secret redaction, risk-aware policies, and explicit user approval before destructive or high-impact operations.
SYSTEM
MODULES
Logical grouping of platform capabilities and execution primitives by architectural domain.
ISOLATED DOMAIN SPECIFICATIONS — MODULAR ARCHITECTURAL LAYERS DELIVERING HIGH THROUGHPUT AND RESILIENT FAULT TOLERANCE.
VISUAL
SHOWCASE
A detailed look at the interface, workflows, and execution environments engineered for this system.
PLATFORM PREVIEWS — PRODUCTION DEPLOYMENT CAPTURES AND INTERFACE WALKTHROUGHS.
DEVELOPMENT
PROCESS
A rigorous, phased engineering lifecycle designed to transform architectural requirements into scalable, production-ready systems.
DISCIPLINED SYSTEM METHODOLOGY — ARCHITECTED FOR END-TO-END OBSERVABILITY, DETERMINISTIC REVIEWS, AND ZERO-REGRESSION RELEASE CYCLES.
TECHNOLOGY
STACK
An engineered architectural map outlining foundational nodes, runtime environments, and type-safe deployment frameworks.
DETERMINISTIC ARCHITECTURE STACK — PROFILED FOR PRODUCTION LATENCY, STRICT TYPE SAFETY, AND HIGH-CONCURRENCY SCALABILITY.