strands-agents/agent-sop

Natural language workflows that enable AI agents to perform complex, multi-step tasks with consistency and reliability.

What it solves

Agent SOPs (Standard Operating Procedures) solve the problem of inconsistency and unreliability when AI agents perform complex, multi-step tasks. Instead of relying on single-use prompts, it provides a standardized, markdown-based framework to create reusable and shareable workflows that ensure agents follow specific constraints and objectives across different AI systems.

How it works

The project uses a standardized markdown format to define workflows. These SOPs include clear objectives, parameterized inputs for flexibility, and step-by-step instructions using RFC 2119 constraints (MUST, SHOULD, MAY) to provide precise control over agent behavior.

These SOPs can be distributed and integrated into various AI tools via:

  • MCP Server: Exposes SOPs as prompts that AI assistants can discover and use on-demand.
  • Cursor IDE: Converts SOPs into parameterized commands (using the / prefix) that prompt users for input.
  • Agent Skills: Generates SKILL.md files for platforms like Claude, enabling "progressive disclosure of context" where agents load only the relevant workflow for the task.
  • Python SDK: Allows developers to integrate SOPs directly into agentic workflows.

Who it’s for

It is designed for developers and teams who want to standardize their AI agent workflows, particularly for software development tasks like codebase analysis, prompt-driven development (PDD), and test-driven development (TDD) implementation.

Highlights

  • Standardized Constraints: Uses RFC 2119 keywords to ensure reliable execution without rigid scripting.
  • Multi-modal Distribution: Supports MCP, Cursor commands, and Agent Skills (Claude).
  • Parameterized Workflows: Transforms static prompts into flexible templates.
  • Pre-built SOPs: Includes ready-to-use workflows for codebase summary, PDD, code task generation, and automated evaluation.
  • Context Efficiency: Reduces context window bloat by allowing agents to load only the necessary SOP skill when needed.

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