Design strategies to avoid infinite loops, repetitive behaviors, or deadlocks in fully autonomous multi-agent environments.

Techniques for message passing, memory sharing, and protocol standardization between agents.

Implementing shared or centralized memory to synchronize context and reduce redundant computation.

Overview of how multiple LLM-powered agents can collaborate or compete to solve complex tasks.

Monitoring, logging, and diagnosing coordination issues and response inconsistencies in agent workflows.

Different agent roles like planner, executor, researcher, and critic in collaborative LLM systems.

Comparing topologies such as hub-spoke, peer-to-peer, and hierarchical agent communication models.

Approaches to benchmark multi-agent systems using task-specific metrics, collaboration success rates, and error analysis.

Using CrewAI to define roles, tools, goals, and communication protocols for agent collaboration.

Designing chat-based multi-agent systems using Microsoft AutoGen for task-solving and tool integration.

Dividing large problems into subtasks and delegating them among autonomous LLM agents.

Consensus building, voting mechanisms, and arbitration strategies between agents with overlapping goals.

Techniques to sandbox individual agents to avoid unexpected tool usage, harmful prompts, or data leaks.

Ethical challenges in delegating decision-making to autonomous agents, including bias, consent, and traceability.

Using tools like web search, calculators, APIs, and databases across agent workflows for enhanced reasoning.

Swift Lessons: Multi Agent Systems

Concepts