In a world full of autonomous agents, the real value is not another agent. The real value is the system that can orchestrate, govern, execute, and continuously improve them. The current AI landscape is full of impressive agent platforms: OpenClaw, MaltBot, Agent Zero, and many … …
What if AI agents didn’t just execute tasks—but designed and constructed the systems they need to complete them? And what if every capability they have—calling APIs, running shell commands, installing software on remote machines—was channeled through secure, auditable pathways that you configure? AgenticOS makes both … …
Agent transitions represent the most powerful building block in AgetnticOS. Unlike LLM transitions that process prompts and return responses, Agent transitions execute autonomous AI agents that can make decisions, call tools, and produce entirely new data structures. Key Innovation: Agent transitions have optional presets (agents … …
LLM (Large Language Model) transitions bring the power of AI directly into your agentic processes. They enable natural language processing, text generation, sentiment analysis, entity extraction, and intelligent routing – all orchestrated through simple inscriptions. Key Innovation: LLM transitions use a two-field emit pattern where … …
Command transitions execute shell commands and filesystem operations via the agentic-net-executor service. They enable your Petri nets to interact with the operating system – running scripts, managing files, invoking CLI tools, and integrating with external processes. Command transitions dispatch work to the executor service and … …
Template interpolation is how AgetnticOS transitions access and transform token data. Using ${…} syntax, you can reference input token fields, metadata, and build dynamic outputs in Map templates, HTTP bodies, LLM prompts, and more. Templates use hierarchical paths to access token data and metadata: Understanding … …
HTTP transitions connect your agentic processes to the outside world. They make REST API calls, process responses, handle authentication, implement retry logic, and route results based on HTTP status codes or response content. HTTP transitions call external APIs and emit parsed response data to success … …
ArcQL (Arc Query Language) is AgetnticOS’s query language for selecting tokens from places. Every transition preset uses ArcQL to specify which tokens to consume, making it fundamental to understanding how tokens agentic through your Petri nets. Key Rule: All paths must start with $ (the … …
The Pass transition is the foundational routing primitive in AgetnticOS Petri nets. It examines token data and decides where each token should go – without transforming it, without calling external systems, without side effects. Think of it as a railway switch operator: tokens arrive, get … …
While Pass transitions route tokens unchanged, Map transitions transform token data through templates. They’re the workhorses of data reshaping – adding fields, removing fields, restructuring, enriching, and preparing data for downstream processing. Map transitions take input token data, apply a template transformation, and emit the … …
