• When we first built Dash, it looked like most enterprise search systems: a traditional RAG pipeline that combined semantic and keyword search across indexed documents. • It worked well for retrieving information and generating concise answers. • But as teams began using Dash for more than just finding content-for example, asking it to interpret, summarize, and even act on what it found-we realized that retrieval alone wasn’t enough. • The natural progression from “what is the status of the identity project” to “open the editor and write an executive summary of the projects that I own” required Dash to evolve from a search system into an agentic AI. • That shift introduced a new kind of engineering challenge: deciding what information and tools the model actually needs to see to reason and act effectively. • This has been popularized as context engineering, the process of structuring, filtering, and delivering just the right context at the right time so the model can plan intelligently without getting overwhelmed.
Article Summaries:
- Dash has evolved from a traditional retrieval‑augmented search system into an agentic AI that can plan, reason, and act on user requests. To support this shift, the team introduced “context engineering,” structuring and filtering the information and tools fed to the model so it can make intelligent decisions without overload. The addition of many tools initially caused analysis paralysis and “context rot,” degrading accuracy and performance. By limiting the number of tool definitions, filtering context to only what’s relevant, and creating specialized agents for specific tasks, Dash now delivers faster, more accurate results while keeping token usage and cost in check.
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