The hidden cost of context switching in AI workflows
Multi-step AI workflows lose information at every boundary. The handoff between steps is where accuracy degrades, latency compounds, and cost accumulates. Most teams do not measure it.
The token budget problem: why your production agents run out of room
Context windows are finite. Production workloads are not. Here is what actually breaks when an agent exhausts its budget, and three patterns that prevent it.
Why long-running AI agents fail silently
Agents degrade before they break. Context pressure accumulates, attention patterns shift, and outputs drift, all without an error code. Here's how to detect it and what to do about it.
Most multi-agent systems are sequential pipelines wearing a costume
Most 'multi-agent' systems are sequential pipelines with role-play prompts. Three diagnostic questions to tell the difference.
Beyond Chatbots: Multi-Agent Architecture Patterns for Production
Single-model AI hits a ceiling fast. Here are the architecture patterns we use to build multi-agent systems that coordinate hundreds of specialized agents in production.