AI agents should be built as explicit state machines rather than monolithic prompts, according to a new analysis of agent architecture failures. A single prompt that handles routing, extraction, tool …
PII scrubbing must occur before prompt construction, not after model output, because large language models cannot distinguish private data from untrusted instructions at the token level. Deterministic…
The client-facing dashboard is dead for outcome-based AI services, replaced by an email-first "Acknowledgment Loop" that measures trust and engagement through client replies. Pull-based dashboards bur…
A 70-billion parameter model can cost less per useful token than an 8-billion parameter model in production inference, because the real cost driver is GPU utilization rather than parameter count. Team…
Adding more context to an LLM prompt often degrades performance rather than improving it, as the context window functions as volatile working memory rather than reliable storage. Past a certain thresh…
A new event-driven operator pattern for AI agents replaces polling loops with message-based wake-up, deterministic processing, and durable state checkpointing, eliminating three structural failure mod…
Seven AI models were each given the same prompt in blind OpenCode sessions and asked to rank all seven, including themselves, on fixed factors. The mistakes — silent model substitutions, hallucinated …
In multi-step AI pipelines, a dangerous failure occurs when a bad intermediate result becomes trusted state for the next step, not just when a single model output is wrong. Deterministic validators pl…