Define workflows for agents/models/middleware in BoxLang development.
npx skills add https://github.com/ortus-boxlang/skills --skill bx-ai-agents
Please help me install this Agent Skill. Skill: bx-ai-agents Source: https://github.com/ortus-boxlang/skills/tree/main/boxlang-modules/bx-ai/bx-ai-agents Command: npx skills add https://github.com/ortus-boxlang/skills --skill bx-ai-agents
Building and orchestrating autonomous AI agents requires integrating models, tools, skills, memory, and inter-agent communication. This skill provides a unified pattern to configure and run aiAgent() with optional components like MCP servers and middleware.
Create a basic agent with a name, description, and instructions to begin using aiAgent().
High-intent search queries and answers about installing and using this skill.
You orchestrate AI agents in BoxLang by configuring the aiAgent() function with an aiModel, memory, tools, and middleware. This pattern allows you to define agent workflows and manage components for repeatable autonomous tasks.
Multi-agent hierarchy orchestration coordinates multiple AI agents where parent agents delegate tasks to sub-agents. You need it when a single agent cannot handle complex workflows, allowing specialized agents to hand off tasks across synchronous and asynchronous streams.
You assemble an AI agent by defining its name, description, model, and instructions, then optionally attaching MCP servers and middleware. This composition ensures deterministic behavior and safe execution across streaming and asynchronous scenarios.
Yes, you can manage always-on and lazy-loaded skills across multi-agent hierarchies. This orchestration pattern scales your AI agents by ensuring specific skills are available continuously while others load only when triggered.
Yes, the AI agent orchestration pattern supports streaming scenarios. You can apply it within BoxLang agent development across both synchronous and asynchronous tasks to ensure deterministic behavior during live execution.
You need middleware for safe AI agent execution to enforce deterministic behavior and guardrails around model interactions. It intercepts processes between the aiModel, tools, and memory to prevent unsafe autonomous actions.
ortus-boxlangChain aiMessage(), aiModel(), and aiTransform() steps into deterministic BoxLang pipelines.
proofoftomCoordinate autonomous agents via A2A protocol with Agent Card discovery and task lifecycles.
nadicodeaiConnect to MCP servers and auto-register their tools in Argo Agent.
ehfazrezwanMap custom AI agent event formats to NeuralScape conversation schemas.
