daa-agent

Create and adapt Dynamic Agentic Architecture agents that learn from interactions.

70.1k|8.4k|Updated Jun 2, 2025
One-click install
npx skills add https://github.com/ruvnet/claude-flow --skill daa-agent
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: daa-agent
Source: https://github.com/ruvnet/claude-flow/tree/main/plugins/ruflo-daa/skills/daa-agent
Command: npx skills add https://github.com/ruvnet/claude-flow --skill daa-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Static agents keep fixed behavior regardless of outcomes, so they never improve with use. This Skill creates Dynamic Agentic Architecture (DAA) agents that adapt their behavior based on performance metrics, learn continuously from interactions, and share knowledge with other agents.

Core Features & Use Cases

  • Adaptive Agent Creation: Create DAA agents with initial configuration and learning parameters via the ruflo DAA MCP tools.
  • Learning & Performance Monitoring: Track adaptation progress with learning status and inspect efficiency and accuracy through performance metrics.
  • Manual Adaptation & Knowledge Sharing: Trigger adaptation based on feedback and propagate learnings across agents.
  • Use Case: Deploy a support-triage agent that starts with a baseline configuration, monitors its accuracy metrics over time, adapts its routing behavior based on feedback, and shares what it learned with sibling agents handling related queues.

Quick Start

Create a DAA agent with learning enabled, then check its learning status and performance metrics to see how it adapts over time.

Frequently Asked Questions about daa-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create an adaptive AI agent that learns over time?

Create a DAA agent by calling the daa_agent_create tool with an initial configuration and learning parameters. The agent then adapts its behavior based on performance metrics and learns continuously from interactions.

What is the difference between a DAA agent and a static agent?

A static agent has fixed configuration, no learning, isolated knowledge, and constant performance. A DAA agent adapts behavior over time, learns continuously from interactions, shares knowledge across agents, and improves performance with use.

How do I monitor a DAA agent's learning progress?

Call daa_learning_status to see adaptation progress and daa_performance_metrics to inspect efficiency and accuracy metrics. These two tools together show whether the agent is improving as expected.

Can DAA agents share knowledge with other agents?

Yes, the daa_knowledge_share tool propagates learnings from one agent to other agents. This lets improvements discovered by one agent benefit the wider agent fleet instead of remaining isolated.

When should I trigger manual adaptation instead of waiting?

Trigger manual adaptation with daa_agent_adapt when you have explicit feedback that should change agent behavior immediately, rather than waiting for gradual learning from interactions to converge.