📄️ run
The run step allows you to execute arbitrary shell commands directly within your task. This allows you to integrate with existing command-line tools, scripts, or performing system-level operations that don't require the intelligence of an AI agent.
📄️ prompt
Use a prompt step when you need a single-turn response from the fast query LLM without spinning up a full agent. The step renders your message with the task context, sends it to the configured simple prompt model, and stores the model's reply so later steps can reference it.
📄️ agent
The agent step is a core component in Bosun tasks, allowing you to leverage AI agents to perform complex, intelligent actions on your codebase. An agent step defines a specific task for an AI agent, providing it with instructions, a role, and constraints to guide its behavior.
📄️ structured-prompt
structured_prompt prompts the LLM for a JSON-formatted answer that matches a schema. Bosun renders your prompt with the task context, requests a structured response, validates the result, and makes it available to downstream steps.
📄️ feedback
Use a feedback step when you want a workflow to pause and wait for a human decision or edit before continuing. The step seeds an initial value, validates it against a schema (either inferred or explicit), and then blocks the task until feedback is submitted.
📄️ for-each
The for_each step in Bosun allows you to iterate over a list of items, executing a sub-step (which can contain agent or run steps) for each item. This is effective for automating repetitive tasks across multiple files, configurations, or data entries.