Agent Development

What Are Station Agents?

Station agents are AI-powered automation tools that:

Execute multi-step tasks autonomously
Access tools and APIs through MCP servers
Run on your infrastructure with your credentials

Unlike ChatGPT or Claude, Station agents have direct access to your tools (AWS, databases, filesystems) and can execute complex workflows without copy-pasting commands.

Dotprompt Format

Every agent is a .prompt file with YAML frontmatter and a template body:

---
metadata:
  name: "Cost Analyzer"
  description: "Analyzes AWS costs and finds savings"
  tags: ["finops", "aws"]
model: gpt-4o-mini
max_steps: 8
tools:
  - "__get_cost_and_usage"
  - "__list_cost_allocation_tags"
---

{{role "system"}}
You are a FinOps analyst specializing in AWS cost analysis.

Your process:
1. Query AWS Cost Explorer for the past 30 days
2. Identify cost spikes exceeding 20% baseline
3. Break down costs by service
4. Provide actionable recommendations

{{role "user"}}
{{userInput}}

Frontmatter Reference

Field	Type	Required	Description
`metadata.name`	string	Yes	Agent display name
`metadata.description`	string	Yes	What the agent does
`metadata.tags`	array	No	Categories for search
`model`	string	Yes	AI model to use
`max_steps`	integer	Yes	Maximum tool calls (5-15 typical)
`tools`	array	Yes	MCP tools the agent can use

Model Selection

Model	Use Case	Speed	Cost
`gpt-4o-mini`	Simple tasks, file ops	Fast	Low
`gpt-4o`	Complex reasoning	Medium	Medium
`claude-3-5-sonnet-latest`	Code analysis, security	Medium	Medium

Creating Agents

CLI
File

# Interactive creation
stn agent create

# With flags
stn agent create --name "my-agent" --model gpt-4o-mini

Create a .prompt file in your environment’s agents directory:

~/.config/station/environments/default/agents/my-agent.prompt

Running Agents

# Run an agent
stn agent run "Cost Analyzer" "Analyze last month's AWS spending"

# With real-time output
stn agent run "Cost Analyzer" "Find cost savings" --tail

Discovering Tools

List available MCP tools for your agents:

# All tools in current environment
stn mcp tools

# Search for specific tools
stn mcp tools | grep aws

Coding Backends

Agents can be enhanced with coding capabilities that allow them to read, write, and modify files.

OpenCode Backend

Enable AI coding with filesystem access via OpenCode:

---
metadata:
  name: "Code Assistant"
  description: "AI coding assistant"
model: openai/gpt-4o
coding:
  enabled: true
---

You are an expert software engineer. Use coding tools to implement features.

This gives the agent these tools:

Tool	Description
`coding_open`	Start a coding session in a directory
`code`	Execute coding tasks (read/write/refactor)
`coding_close`	End the session
`coding_commit`	Commit changes to git
`coding_push`	Push to remote repository

The transport mechanism (HTTP or NATS) is configured at the Station level, not per-agent. This makes agent definitions portable across different deployments. See OpenCode Backend for backend configuration.

Coding Frontmatter Options

Field	Type	Required	Description
`coding.enabled`	boolean	Yes	Enable coding tools for this agent
`coding.workspace_path`	string	No	Default workspace directory

OpenCode Backend

Full guide to OpenCode integration, HTTP vs NATS backends, and container mode

Sandbox Backend

For isolated code execution in containers (different from OpenCode), use the sandbox backend:

---
model: openai/gpt-4o
sandbox:
  mode: code
  session: workflow
---

Sandbox Execution

Run code safely in isolated Docker containers

Notifications

Enable agents to send webhook notifications (ntfy, Slack, or any webhook endpoint):

---
model: anthropic/claude-sonnet-4-20250514
notify: true
---
You are a monitoring agent. Send notifications when tasks complete or issues arise.

This gives the agent the notify tool with parameters:

Parameter	Description
`message`	Notification content (required)
`title`	Subject line (optional)
`priority`	`min`, `low`, `default`, `high`, `urgent`
`tags`	Emoji tags like `["warning", "white_check_mark"]`

Notifications

Full guide to webhook configuration and formats

Next Steps

Notifications

Send webhook notifications from agents

MCP Tools

Connect agents to external services

Multi-Agent Teams

Build hierarchical agent teams

Workflows

Orchestrate multi-step agent tasks

Evaluation

Test and score your agents

Get Started

Station CLI

Building Agents

Station Lattice

Deployment

Advanced

What Are Station Agents?

Dotprompt Format

Frontmatter Reference

Model Selection

Creating Agents

Running Agents

Discovering Tools

Coding Backends

OpenCode Backend

Coding Frontmatter Options

OpenCode Backend

Sandbox Backend

Sandbox Execution

Notifications

Notifications

Next Steps

Notifications

MCP Tools

Multi-Agent Teams

Workflows

Evaluation

​What Are Station Agents?

​Dotprompt Format

​Frontmatter Reference

​Model Selection

​Creating Agents

​Running Agents

​Discovering Tools

​Coding Backends

​OpenCode Backend

​Coding Frontmatter Options

OpenCode Backend

​Sandbox Backend

Sandbox Execution

​Notifications

Notifications

​Next Steps

Notifications

MCP Tools

Multi-Agent Teams

Workflows

Evaluation

What Are Station Agents?

Dotprompt Format

Frontmatter Reference

Model Selection

Creating Agents

Running Agents

Discovering Tools

Coding Backends

OpenCode Backend

Coding Frontmatter Options

Sandbox Backend

Notifications

Next Steps