Agent Architecture Patterns

Core architectural patterns for local AI agents include Reflection for iterative self-critique, Planning for task decomposition, Tool Use for external interaction, Multi-Agent for role collaboration, and Memory for state management across sessions.

Apply the reflection pattern by drafting an initial agent response, evaluating it critically for hallucinations, and refining the output through iterative self-critique before finalization to improve reasoning reliability.

Decompose complex goals into atomic tasks using the Planning pattern, which structures multi-agent workflows by defining deterministic operating flows with clear validation steps and separating planning from execution.

Yes, you can coordinate multiple local AI agents using the Multi-Agent pattern to assign specific roles, hand off data between agents, and aggregate individual outputs into a cohesive final result.

The best way to maintain context and state across local AI agent sessions is implementing the Memory pattern, which enables memory-aware context management to preserve information between interactions.

Separate planning, execution, and memory in local AI agent architectures when designing complex agent-based workflows or debugging reasoning chains to ensure modular pattern definitions and deterministic operating flows.