ACM Conference on AI and Agentic Systems (CAIS 2026)

As AI systems rapidly move from standalone models to sophisticated multi-component deployments, a new conference will address one of the field's most critical challenges: how to engineer AI systems that work in the real world. Rather than focusing solely on individual model capabilities, the conference centers on how AI components are composed, orchestrated, and evaluated as part of end-to-end systems. A core theme of CAIS is the difference between language-model scaffolds—temporary structures used to bootstrap new capabilities from today's models—and AI software systems, which must be durable, efficient, and dependable over time. The conference emphasizes evaluation methods that remain meaningful even as models improve, focusing on application-specific requirements such as latency, correctness, and verifiability. It will feature peer-reviewed research across four core areas: Architectural Patterns & Composition, System Optimization & Efficiency, Engineering & Operations, and Evaluation & Benchmarking.