AI Agent Architectures

Context isolation prevents token degradation as AI systems handle longer, more complex tasks.

Teams must evaluate agent trajectories and tool calls, not just final outputs.

LLM agents fail predictably at tool selection, not randomly.

Persistent memory layers, not bigger context windows, solve agent degradation.

Shared context stores are the load-bearing component that makes or breaks multi-agent coordination.

ReAct adapts step-by-step while Plan-and-Execute locks in the full sequence upfront.

Prevent cascading failures by placing checkpoints where mistakes can't be quietly undone.