By the time we are experiencing the Singularity, it’s too late to stop it.
Multi-Agent Systems (MAS)
As soon as AI Agents can reason and take action, cooperation between Agents becomes straightforward. Talking to another Agent is merely one of the actions at a disposal of any AI agent.
The report from the World Economic Forum highlights that in the near future, we will witness the evolution toward multi-agent systems (MAS), where multiple AI agents collaborate to achieve complex goals. Multi-agent AI systems offer scalability and resilience, allowing organizations to tackle challenges beyond the capacity of single agents.
Cooperating Agents
As soon as AI Agents can reason and take action, cooperation between Agents becomes straightforward. Talking to another Agent is merely one of the actions at a disposal of any AI agent.
Agents in Singularitycrew are built using the ReAct framework. This framework enables the chain of thought, observation and action. This chain makes AI Agent understand the changing environment, perform dynamic reasoning, and quickly adapt its acting plan based on external information.
Task Delegation
Special protocol enables all agents to be aware of their teammates. When an agent recognizes another agent as better suited for a task, it can delegate the task accordingly.
A squad of agents is led by a Squad Manager who accepts all tasks, identifies the most suitable agent for each task and delegates it to the most competent team member.
Agent Communication
Special protocol allows agents to communicate with each other, sending messages to trigger tasks with specific inputs.
Communication protocol is a structured set of rules and standards that enables AI agents to exchange information, coordinate actions, and delegate tasks efficiently. It ensures seamless interaction by defining how messages are formatted, transmitted, and processed, allowing agents to collaborate, share knowledge, and trigger tasks with specific inputs.
Example: AI Agent Communication in Market Research & Analysis.
Internet Research Agent (Agent 1) → Gathers data from the web.
Marketing Analyst Agent (Agent 2) → Analyzes the data and provides insights.
Internet Research Agent (Agent 1) searches the internet for recent articles, reports, and social media discussions on marketing trends. Agent 1 Extracts key findings and summarizes the top 3 emerging trends.
Marketing Analyst Agent (Agent 2) processes the input from Agent 1 and creates a recommended marketing strategy based on the insights.
Multi-Agent Cooperation
Real-world business problems are complex projects composed of multiple tasks, each carried out by different specialists Agent. Collaboration, with each expert handling their specific responsibilities, ensures the successful resolution of these challenges.
A taskforce is a team of highly specialized AI agents capable of tackling complex real-world problems by executing multiple interconnected tasks through seamless communication.
Multi-level Collaboration
Integrating delegation and sequential cooperation enables multi-agent systems to effectively manage intricate business processes.
Delegation allows AI agents to assign specific tasks to other agents based on expertise or capacity, optimizing resource utilization. Sequential cooperation ensures that agents work collaboratively in a structured sequence, where the output of one task becomes the input for the next, maintaining process continuity and efficiency. This combination facilitates the modeling and execution of complex workflows within multi-agent frameworks.
Indirect Collaboration
communication via shared data
Agents can utilize a shared data repository that stores information accessible to all agents. This includes common domain knowledge (such as technical data, business descriptions, or game world narratives), collective objectives and rules, standardized operational procedures, and more. This shared repository serves as a communication medium to simultaneously reach all agents: when one agent updates the database, all other agents incorporate this update into their operations.
Decision Making
Singularitycrew integrates key decision-making patterns for AI agents, ensuring the efficient operation of multi-agent systems within complex business environments.
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Democratic Decision-Making: This approach involves AI agents collaborating to reach a consensus, often through mechanisms like voting or averaging individual preferences. Each agent contributes equally to the decision process, ensuring that the collective outcome reflects the majority or aggregate viewpoint.
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Hierarchical Decision-Making: In this structure, AI agents are organized in a hierarchy where decisions flow from higher-level agents to subordinate ones. Higher-tier agents possess broader oversight and make strategic decisions, while lower-tier agents handle specific tasks or implement directives. This pattern is efficient for managing complex systems with diverse functionalities, as it allows for clear delegation and control.
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Sequential decision-making: When AI agents execute a series of tasks where each task's output serves as the input for the next, and each agent independently makes decisions within its assigned task. In this framework, agents operate in a pipeline-like structure, with each agent's decision influencing subsequent actions in the sequence.
Beyond a Single Team
We now have all the essential foundations for advanced cooperation between AI agent teams.
