Dca

Skill: Dynamic Agent Composition (DAC)

Version: 1.0 Author: Manus AI

1. Description

This skill implements Dynamic Agent Composition (DAC), a sophisticated method for assembling an optimal team of specialist AI agents for any given task. Instead of relying on a fixed, monolithic team, DAC analyzes the specific requirements of a project and dynamically selects the best agents from a pool of available experts. Each agent has a profile advertising its unique capabilities (e.g., 'react-development', 'database-schema-design', 'api-security').

This skill is a direct implementation of the fourth of the 10 breakthrough LLM innovations.

Key Features:

• Capability-Based Selection: Composes teams based on the specific skills needed for a task, not on pre-defined roles.
• Optimal Team Assembly: Uses an efficient algorithm to select the smallest possible team that covers all required capabilities.
• Extensible Agent Pool: Allows new agents with new capabilities to be registered at any time, making the system highly adaptable.
• Resource-Efficient: Ensures that only the necessary agents are activated for a task, optimizing computational resource usage.

2. How to Use

2.1. Installation

This skill is a self-contained Python module. To use it, import the AgentComposer and AgentProfile classes.

from skills.dac.src.dac_engine import AgentComposer, AgentProfile

2.2. Defining and Registering Agents

First, define the profiles for your available specialist agents. Then, register them with the AgentComposer.

# 1. Instantiate the composer
dac = AgentComposer()

# 2. Define agent profiles
frontend_agent = AgentProfile(
    agent_id="frontend-dev-01",
    name="React Specialist",
    description="Expert in React and modern frontend frameworks.",
    capabilities={"react", "typescript", "css-in-js"}
)

backend_agent = AgentProfile(
    agent_id="backend-dev-01",
    name="Node.js Specialist",
    description="Expert in building scalable Node.js APIs.",
    capabilities={"nodejs", "express", "api-design"}
)

# 3. Register agents
dac.register_agent(frontend_agent)
dac.register_agent(backend_agent)

2.3. Composing a Team

Define the set of capabilities required for your project. The compose_team method will then select the best agents for the job.

project_requirements = {"react", "api-design", "typescript"}

composed_team = dac.compose_team(project_requirements)

print(f"Composed a team of {len(composed_team)} agents:")
for agent in composed_team:
    print(f"- {agent.name} (covers: {agent.capabilities})")

2.4. Verifying Capability Coverage

After composing a team, you can check if any required capabilities were not covered by the available agents.

uncovered = dac.get_uncovered_capabilities(project_requirements, composed_team)

if uncovered:
    print(f"\nWarning: The following capabilities could not be covered: {uncovered}")
else:
    print("\nSuccess: All required capabilities are covered by the team.")

3. Development Roadmap

DAC is key to making AI development teams flexible and efficient. Future development will focus on adding more intelligence to the composition process.

• v1.1: Capability Weighting:

  • Goal: Allow requirements to specify not just the need for a capability, but also the desired level of expertise (e.g., react:expert). The composer will then prioritize agents with a matching skill level.
  • Timeline: 2 weeks

• v1.2: Cost-Based Composition:

  • Goal: Assign a computational "cost" to each agent. The composer will then be able to assemble a team that not only covers the required capabilities but also does so within a specified budget.
  • Timeline: 4 weeks

• v1.3: Dynamic Re-composition:

  • Goal: Enable the system to re-compose the team mid-task if new requirements are discovered. For example, if a project suddenly requires a database, the composer can add a database specialist to the active team.
  • Timeline: 5 weeks

• v2.0: Predictive Composition:

  • Goal: Integrate with the Predictive Task Decomposition (PTD) skill. The composer will analyze the task graph from PTD to predict which capabilities will be needed in the future and pre-emptively assemble the right team.
  • Timeline: 8 weeks