agent-architecture

You can build a multi-agent system using orchestration patterns like Multi-Agent Router or Multi-Agent Parallel to coordinate autonomous agents. This framework specifies agent roles, router logic, and output structuring to manage complex workflows.

Effective orchestration patterns for autonomous AI agents include Single-Turn, ReAct, Chain-of-Thought, Multi-Agent Router, Multi-Agent Parallel, and Plan-and-Execute. These patterns define how agents process logic and route tasks.

You implement memory strategies and guardrails by defining tool interface schemas and safety specifications within your agent blueprint. This ensures scalable and safe deployments by controlling agent behavior and context retention.

You model token costs for a multi-agent system by applying the framework's token-cost modeling specifications to your orchestration patterns. This ensures scalable deployments by tracking and optimizing token usage across agent interactions.

Yes, this AI agent framework supports hybrid human-AI workflows. It provides structured patterns for planning, tool use, and safety considerations tailored for projects integrating human oversight with autonomous agents.

Use a Plan-and-Execute pattern for complex tasks requiring upfront structuring, and a ReAct pattern for iterative reasoning and tool use. The framework helps distinguish when to apply each orchestration pattern based on task demands.