AI Agent Automation
System Architecture

Designed for Execution, Not Guesswork

AI Agent Automation is built as a deterministic execution engine with strict boundaries between orchestration, execution, and observation.

High-Level Execution Flow

This diagram represents the actual execution boundaries in the system. Every block is a real component. Every connection is a controlled data flow. You can drag nodes to explore the architecture.

React Flow

Unlike static diagrams, this canvas reflects how execution actually happens — with strict separation between orchestration, execution, scheduling, and persistence.

REST API Layer

Responsible only for validation, persistence, and orchestration. The API never executes workflow logic directly.

  • Creates workflows & tasks
  • Persists execution state
  • Triggers engine execution

Agent Runner

The central orchestrator. It executes workflow steps in strict order and enforces deterministic behavior.

  • Sequential step execution
  • Error propagation
  • Agent context isolation

Step Executor

Executes a single step with clearly defined inputs and outputs. Each step is sandboxed by type.

  • LLM calls
  • HTTP requests
  • Tool execution
  • Delay / timing steps

Scheduler

A cron-based trigger system that creates tasks without coupling execution logic to time.

  • Cron expressions
  • Background execution
  • No UI dependency

Persistence Layer

MongoDB stores immutable execution history alongside mutable workflow definitions.

  • Workflows
  • Tasks
  • Agents
  • Execution logs

Safety Boundaries

The system is designed so failures are isolated, observable, and recoverable.

  • No hidden retries
  • No implicit agent state
  • No cross-task leakage

Why These Boundaries Matter

Clear separation of concerns makes the system easier to debug, extend, audit, and trust. This is what allows AI automation to move from demos to production systems.