> ## Documentation Index > Fetch the complete documentation index at: https://docs.bagofwords.com/llms.txt > Use this file to discover all available pages before exploring further. # Bag of words > Governed AI agents for any model and any data. Bag of words gives teams a governed **agentic analytics** system: use the LLMs you choose, connect the data you already have, and let agents investigate, validate, and explain work that ranges from a quick question to a deep root-cause analysis. An executive dashboard artifact beside the prompt, validation steps, sharing, and schedule that created it Agents can work across databases, services, APIs, tables, semantic objects, documents, and files. They do not just produce a one-shot answer: they plan, use tools in parallel, inspect evidence, reflect on what is missing, and repair their approach when needed—all within the access and policy boundaries you define. Sign in, create your workspace, and ask a first question. Use a catalog connector, a custom MCP server, or a custom API. Shape agents with data, tools, files, instructions, and evals. ## From question to defensible result Bag of words runs a governed agentic loop. An agent plans the investigation, explores independent evidence in parallel, calls only the data and tools it is allowed to use, validates what it finds, and reflects or repairs when the evidence is incomplete. The same system can answer a focused business question or coordinate a challenging financial analysis or production root-cause investigation. A current Chat-mode root-cause analysis with parallel tool work, an investigation note, and a Mermaid incident map Its **Knowledge Harness** is the context that makes the loop useful: scoped connections, tables and semantic objects, files, tools, instructions, identity, permissions, and prior evidence. Evals and feedback close the self-improvement loop, so teams can review and promote better instructions rather than giving an agent unchecked authority to change its own rules. ## What you can build * **Business analysis:** revenue and financial analysis, operating reviews, and self-serve exploration. * **Technical investigation:** production root-cause analysis across observability systems, services, and databases. * **Knowledge work:** research and synthesis across structured records and unstructured files. * **Repeatable workflows:** training agents with scoped instructions and evaluations, then tracking quality, usage, and cost. Learn Chat mode, Training mode, and the analysis workflow. Inspect runs, traces, quality, usage, and cost. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.