Context Engineering Predicts AI Agent Failures More Than Agent Design
The paper shows that weak context—poor instructions, tools, memory, guardrails, and untrusted inputs—drives AI agent failures more than the agents themselves. Using the open‑source ProofAgent‑Harness, the authors isolate seven context quality criteria that reliably forecast hallucinations, misuse, and manipulation, making context a pre‑flight reliability metric.