What is ACM CAIS 2026?

ACM CAIS 2026 is the inaugural ACM Conference on AI and Agentic Systems, held May 26-29 in San Jose, California. It is the first academic conference dedicated to agentic AI systems.

How many papers were accepted at CAIS 2026?

63 peer-reviewed research papers and 46 system demonstrations were accepted, plus 6 workshops and tutorials.

What topics does CAIS 2026 cover?

Architectural patterns and composition for multi-agent systems, system optimization and efficiency, engineering and operations of compound AI, and evaluation and benchmarking methodologies.

Who is organizing ACM CAIS 2026?

Co-chaired by Two Sigma Heather Miller. Sponsored by ACM (Association for Computing Machinery).

Why is CAIS 2026 significant for OpenClaw?

It signals that agentic AI has matured from a hobby/startup phenomenon into a recognized academic discipline, legitimizing the engineering challenges OpenClaw and its ecosystem face.

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ACM CAIS 2026: First Academic Conference on AI and Agentic Systems Opens in San Jose

Inaugural ACM conference dedicated to agentic AI, May 26-29 2026 in San Jose. 63 peer-reviewed papers, 46 system demos, 6 workshops. Co-chaired by Two Sigma Heather Miller. Covers multi-agent architecture, compound AI, evaluation, safety. Signals field maturation.

Product Idea from this Signal

A search engine that indexes agentic AI research papers, benchmarks, and safety evaluations so builders can ground their architecture decisions in peer-reviewed evidence

ACM just launched CAIS 2026, the first academic conference dedicated to agentic AI systems, with 63 papers and 46 demos covering multi-agent architecture, safety, and evaluation. Meanwhile academic security research like Prism is publishing formal verification approaches for agent runtimes. Builders making production architecture decisions (sandboxing strategy, multi-agent composition, tool-use safety) have no way to find relevant academic work without manually scanning arxiv and conference proceedings. This tool indexes agentic AI papers, extracts actionable findings, and surfaces them when a builder searches for their specific technical challenge.

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