ACM CAIS 2026

- The corpus repeatedly cites a workshop organized by researchers from UC Berkeley, Stanford, CMU, Databricks, Google, and Bespoke Labs. - Focus areas include autonomous AI systems for search, optimization, and scientific discovery. - Invited speakers mentioned in the corpus include Ion Stoica, Graham Neubig, Azalia Mirhoseini, Joseph Gonzalez, and James Zou.

- Official site lists keynote speakers including Andy Konwinski, Thariq Shihipar, and Percy Liang, reinforcing the event's practical orientation toward agentic coding, open research, and benchmark-driven engineering.

- A Berkeley/MIT team presented an LLM-based optimization system that frames diverse problems as iteratively improving a text artifact evaluated by a scoring function. - Corpus-reported outcomes include nearly tripling Gemini Flash's ARC-AGI accuracy, cutting cloud scheduling costs 40%, and matching AlphaEvolve on circle packing.

- **Research-to-product pipeline:** CAIS research maps directly onto enterprise agent pain points: optimization, evaluation, architecture, safety, and reproducibility. - **Agent engineering discipline:** The field is moving from demos to repeatable blueprints, benchmarks, and systems papers. - **Open ecosystem:** Participation from universities, Databricks, Google, Anthropic-adjacent practitioners, and open-source communities suggests no single vendor owns the agent stack. - **Benchmark competition:** Terminal-Bench, ARC-AGI, and optimization tasks become strategic proxies for agent utility.

- MIT researchers presented Tressoir, a system for designing and evolving multi-agent architectures, prompts, tools, and knowledge through human-readable “Interpretable Blueprints.” - The goal is reproducible, systematic construction of multi-agent systems instead of ad hoc prompt chains.