agentwise

agentwise is a powerful automation skill designed for Claude Code, enabling multi-agent orchestration that optimizes token usage by 15-30%. This skill features self-improving agents that adapt over time and automatic verification processes to ensure reliability and efficiency in your workflows. By leveraging agentwise, developers and AI practitioners can streamline complex tasks that involve multiple AI agents, enhancing productivity and reducing overhead. The key benefits of agentwise include significant token optimization, which can lead to cost savings in environments where token usage directly impacts performance and expenses. While the exact time savings are currently unknown, the efficiency gained from automating agent interactions and improving their performance can lead to faster project delivery and reduced manual intervention. This skill is particularly valuable for teams focused on AI automation and workflow automation, allowing them to maximize the potential of their AI agents. This skill is ideal for developers, product managers, and AI practitioners who are looking to enhance their AI-first workflows. It is especially relevant for those working in environments where multi-agent systems are commonplace, such as data engineering, machine learning, and software development. Practical use cases include automating data processing tasks, managing complex decision-making processes in real-time, and enhancing the responsiveness of AI-driven applications. Implementation of agentwise is categorized as intermediate, requiring approximately 30 minutes to set up. Users should be familiar with Claude Code and have a basic understanding of multi-agent systems to fully leverage its capabilities. By integrating agentwise into your existing workflows, you can create a more efficient and adaptive AI environment that aligns with modern automation needs.

["Define your agent roles and interaction protocols: List the agents (e.g., Planner, Executor, Verifier) and specify how they communicate (e.g., JSON messages, shared memory). Use a tool like `Claude Code` to scaffold the agents in separate files (e.g., `planner.py`, `executor.py`).","Implement self-improvement mechanisms: Add a feedback loop where agents log their performance metrics (e.g., token usage, task completion time) to a shared database (e.g., SQLite or Redis). Use these metrics to adjust agent behavior dynamically, such as refining the Planner’s task breakdown based on past errors.","Set up automatic verification: Define quality checks (e.g., code linting, docstring coverage) and integrate tools like `pylint`, `mypy`, or custom scripts. Configure the Verifier agent to run these checks after each task and flag failures for the Executor to address.","Monitor and iterate: Use Agentwise’s built-in dashboard (or a custom one) to track metrics like token savings and task completion rate. Run A/B tests by tweaking agent roles or protocols, and use the data to further optimize the system.","Deploy incrementally: Start with a small subset of tasks (e.g., 5% of your project) to validate the system. Gradually expand coverage as you confirm improvements in efficiency and quality. Use `Claude Code`’s `--dry-run` flag to test orchestration logic before full deployment."]