multi-agent

ReAct agents accumulate context linearly until they run out of space, then forcibly compact. This leads to expensive LLM calls, slower execution, and context poisoning. Playbooks uses stack-based context management to continuously prune context as playbooks return, keeping only what's relevant. The result is faster, cheaper, and more reliable agent execution - especially for complex workflows.

Hard timeouts force binary decisions - wait or abort. But real intelligence requires nuanced judgment. Adaptive waiting lets agents decide intelligently whether to keep waiting, take alternative action, or escalate, based on context and expectations. Here's how Playbooks brings human-like patience to multi-agent systems.

A funny story about building a world domination program with Playbooks AI.