agentic-systems

Autonomous agents use design patterns like ReAct and Plan-and-Execute loops to manage decision and action cycles. These patterns structure how agents reason about tasks, select tools, and evaluate outcomes during autonomous operation.

Multi-agent systems can be structured using Orchestrator/Worker or Peer-to-Peer architectures. These patterns enable scalable collaboration by defining clear roles for task delegation, inter-agent communication, and coordinated execution across multiple autonomous agents.

Human-in-the-loop (HITL) workflows require safety guards, explicit iteration caps, and error handling protocols. These safeguards ensure reliable governance by allowing human oversight of autonomous decisions, safe tool use, and inter-agent trust in production environments.

Yes, MCP protocols can be applied for tool integration in multi-agent systems. Tool design and memory systems work with MCP to standardize how autonomous agents access external capabilities, manage context, and execute actions safely.

Observability in autonomous agent loops tracks decision cycles, tool execution, and inter-agent communication. It provides visibility into ReAct patterns, memory usage, and error handling to maintain reliability and debug production agent workflows.

Use Orchestrator/Worker architectures when you need centralized task delegation and control, and Peer-to-Peer when agents need equal autonomy for direct collaboration. Both support scalable multi-agent systems with different governance and communication models.