astrion-website/content/en/07-agent-capabilities.md

Agent Capabilities

Astrion's agent capabilities fall into five areas: sub agents (delegates dispatched by the main agent), multi-agent conversation (a team of roles), workflows (predefined process templates), Skills (specialized capability packs), and MCP (external tool integration). This chapter covers how to use and configure each one.

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1. Sub Agents

What It Is

A sub agent is an independent executor dispatched by the main agent within the same process: it has its own context, its own model configuration, and an independent lifecycle. The main agent uses it to process mutually independent tasks in parallel — typical scenarios: researching three technical approaches at once, or organizing documentation while tests run.

What it is not for: sub agents cannot communicate with each other and cannot see the main conversation history, so collaborative tasks (where A's output is B's input) should be executed sequentially or handed to a single sub agent. Code writing/editing tasks are by default handled directly by the main agent.

How to Use

Usually you don't need to operate directly — tell the main agent "do X and Y for me at the same time" and it will decide on its own whether to split the work into sub agents. Two ways of running:

• Foreground (blocking): the main agent pauses and waits for it to finish; suited for fast tasks or scenarios where subsequent steps depend on the result;
• Background: the main agent keeps working or chats with you; when the sub agent finishes, the system notifies you automatically.

You can watch each sub agent's status and output in real time via the "Sub Agents" pane in the Quick Dock on the right side of the conversation area (see "Quick Dock").

Lifecycle & Limits

• States: running → idle (context is preserved, can continue the conversation) / terminal states (completed / failed / timed out / terminated);
• Default concurrency limit: 5 (SUB_AGENT_MAX_ACTIVE);
• Default timeout: 180 seconds; you can specify a longer timeout for an individual task when creating it (1800+ seconds recommended for large-scale analyses);
• Outputs are conventionally placed under sub_agent_results/ in the workspace.

Configuration (Model Library)

Sub agents use a separate model library, sub_agent_models.json; see Section 6 of "Quick Start" for configuration. When creating an individual task, the main agent can specify: the model entry, thinking mode, timeout, max rounds, and more. If not specified, the default_model entry is used.

2. Multi-Agent Conversation

What It Is

A conversation type (chosen at creation time and immutable): the main agent is fixed as Team Leader, responsible for understanding your requirements, breaking down tasks, creating and directing a group of role-assigned sub agents, and consolidating their outputs.

Role System

5 preset roles:

Role	Responsibility
Full-Stack Engineer	Frontend/backend implementation, API design, debugging and integration
UI Operator	UI operations and visual verification
Code Reviewer	Code review
Researcher	Research and information gathering
Brainstormer	Brainstorming and idea exploration

A role = role ID + instance number, with display names like Full-Stack Engineer_1; numbers increment within each role.

Custom roles: Personal Space has a role editor (role list → create/edit). A role definition is a Markdown file with metadata:

---
id: full-stack-engineer      # Role ID
name: Full-Stack Engineer    # Display name
description: One-line role summary  # Team Leader assigns tasks based on this
model: ""                    # Pin a model (empty = sub-agent model library default)
thinking_mode: thinking      # fast / thinking
---

(The body is the role's system prompt: responsibilities, working principles, constraints...)

Communication Mechanics (What You Need to Know)

• Team Leader sends messages to sub agents in two ways: assigning work (non-blocking, keeps doing other things) and asking (blocking, waits for one round of reply);
• Sub agents can ask/answer each other, but all inter-sub-agent communication is reported to the Team Leader in sync;
• Every round of a sub agent's output is reported to the Team Leader in real time and shown in the conversation flow; you can jump in at any time to correct course.

When to Use Multi-Agent

Good fit: a small project that needs "a coder + a code reviewer + a UI runner" working in division of labor; not a fit: tasks a single thread can finish on its own (it only adds coordination overhead).

3. Workflows

What It Is

Write a predefined process (stages, review gates, branches, ending conditions) into a WORKFLOW.md file and save it as a template; once activated, the agent advances strictly stage by stage, reporting to you or a review agent after each stage, and only moves to the next stage after the review passes.

How to Use

• Activate: + menu → "Workflow", or type /workflow to choose;
• Progression: after each stage, the AI reports automatically and moves to the next stage (when the stage includes a review gate, the review agent checks it; a rejection is sent back to the previous stage with remediation feedback);
• Branches: at a branch point, the AI presents a menu of paths for you to decide;
• Exit / check progress: ask the AI to exit the workflow or report current progress at any time;
• Only one workflow can be active in a conversation at a time.

Built-in Workflows

Workflow	Purpose
bug-fix-triage	Defect triage and fix process
code-review-pipeline	Code review pipeline
feature-development	Full feature development process
research-report	Research report generation process

Custom Workflows

Write one following the WORKFLOW.md format of the built-in workflows and drop it into the workflow library directory. It's recommended to just ask the AI in natural language, e.g. "use the workflow-authoring skill to write me an xx workflow" — the system has built-in authoring guidelines and format validation, and the finished workflow is archived automatically and ready to activate.

Workflow reviews are performed by the workflow review agent (workflow_review); see "Review Agents" below for configuration.

4. Skills (Skill Packs)

What It Is

A Skill = a folder + a SKILL.md file (a skill specification with metadata). It captures the experience of "how to do a certain type of task"; when the AI encounters a matching scenario, it reads the skill before acting — like giving the AI an on-the-job training manual.

Built-in Skills (12)

agent-build-standard (Agent architecture guide), agents-md-writer (writing AGENTS.md), docx (Word documents), pptx (PPT), frontend-design (frontend design), ui-aesthetic-design (UI aesthetics), skill-creator (creating new Skills), workflow-authoring (writing workflows), run-command-guide (command execution guidelines), terminal-guide (terminal usage guidelines), sub-agent-guide (sub agent guidelines), mcp-tool-config (MCP self-service configuration).

How to Use

• Insert a reference: type // or use the + menu → "Select AgentSkill" to insert a skill reference into your message;
• Enable/disable: manage the enabled list on the "Tools & Skills" page in Personal Space;
• Strict-guard toggles (all off by default): you can require "read terminal-guide before using the terminal", "read the guidelines before using run_command in foreground/background mode", and "read sub-agent-guide before dispatching a sub agent" — useful during the learning phase to prevent misuse, and can be turned off once you're experienced;
• Skill suggestions (off by default): dynamically suggests potentially relevant skills based on the current task.

Custom Skills

Just tell the AI "capture today's process as a skill" and it will create, validate, and archive it following the skill-creator guidelines, ready to reuse afterwards. User skills are stored in .astrion/skills/ in the workspace.

5. MCP Tool Extensions

Connect external tool services (databases, browser automation, third-party SaaS...) via Model Context Protocol; after connecting, new tools of the form mcp__service name__tool name appear in the AI's tool list.

• Config file: <data root>/<mode>/data/mcp_servers.json (can be overridden with MCP_SERVERS_FILE);
• Master switch: MCP_TOOLS_ENABLED (on by default);
• Protocol version 2025-06-18, default 25-second timeout for tool discovery/invocation;
• Host mode feature: you can directly ask the AI to "configure xxx MCP service for me" — the built-in mcp-tool-config skill guides it to write the config and make it take effect on its own.

6. Review Agents (Three)

Three independent AIs that check key milestones on your behalf, all configured on the "Review Agents" page in Personal Space; they reuse the sub agent model library:

Review Agent	When It Steps In
auto_approval Auto-approval	Under the auto_approval permission mode, auto-approves when a command is about to write outside the sandbox or triggers a permission denial
goal_review Goal review	In goal mode, evaluates whether each round of work achieved the goal
workflow_review Workflow review	At workflow review gates, decides whether to pass or reject

Each can be configured with: model (leave empty for the model library default), thinking (thinking mode), timeout_seconds, max_rounds, max_command_timeout.

Tip: for review agents, choose cheap but stable models — they are called frequently with fixed task patterns, so flagship models are unnecessary.

7. Combinatorial Examples

• Multi-agent + workflow: after activating the feature-development workflow, the Team Leader directs the role team through stage-by-stage delivery per the process;
• Sub agents + Skills: insert //frontend-design before a research task, and the sub agent works with the design guidelines in mind;
• MCP + Quick Dock: when a browser-automation MCP runs a long task, watch real-time progress in the "Background Commands" pane.