Generic AI can summarize documents and answer simple questions. But it fails at complex, specialized work in industries like aerospace, semiconductors, manufacturing, and logistics. The core issue isn’t models, it’s the context or scaffolding around them When enterprises try to build expert AI, they face a hard tradeoff: Build it yourself: Fully customizable, but requires scarce AI expertise, months of development, and constant optimization. Buy off-the-shelf: Fast to deploy, but inflexible. Hard to customize and doesn’t scale across use cases. We took a different approach: a platform approach with a unified context layer specialized for domain-specific tasks. Today, we launched Agent Composer, with orchestration capabilities that enable: Multi-step reasoning (decompose problems, iterate solutions, revise outputs) Multi-tool coordination (docs, logs, web search, APIs in the same workflow) Hybrid agentic behavior (dynamic agent steps + static workflow control) It works: Advanced manufacturing: root cause analysis from 8 hours to 20 minutes Global consulting firm: research from hours to seconds Tech-enabled 3PL: 60x faster issue resolution Test equipment: code generation in minutes instead of days Spending time on the integrating context with AI worked for us on Enterprise AI problems. To get more details about our approach, check out the blog post: https://contextual.ai/blog/introducing-agent-composer submitted by /u/rshah4 [link] [comments]