--- title: "Generative and agentic AI" description: "Generative AI that answers with the source attached, and agents that complete the work inside your systems of record. Every answer has a citation. Every consequential action has an approver." canonical: "https://bespinus.github.io/bgus-ai-landingpage/agentic-ai/" pillar: "agentic-ai" updated: "2026-10-01" --- # AI that finishes the work. Generative AI that answers with the source attached, and agents that complete the work inside your systems of record. Every answer has a citation. Every consequential action has an approver. ## Most of the work still runs on people reading. What teams ask us to automate is mostly document and knowledge work. ### Documents and data rooms Statements, contracts, and deal files turned into records in your system, with a citation on every field and a list of what is missing. Blanks over guesses, and exceptions reach a person with a reason. ### Knowledge and search Years of expertise spread across documents, transcripts, and spreadsheets, made searchable with permissions respected and the source attached to every answer. ### Workflows and bespoke applications Agents built into the process itself, from qualifying a lead to assembling a quote. Often that means replacing SaaS seats with software designed around how you actually work. ## Trust comes from the controls around the model Trust is not a property of the model. It is built from five unglamorous things around it. ### Evaluation before exposure An evaluation harness against real historical cases, run before anything goes in front of anyone. ### Explicit tool boundaries Least-privilege access to systems of record, sized to the cost of the agent being wrong. ### Audit-grade tracing Full trace and decision logging built for audit rather than for debugging. ### Approval gates that are placed, not sprinkled Human approval where the cost of being wrong is highest, and nowhere else. ### Enforced budgets Cost and latency limits applied at the router, set by Tokenomics before the build rather than discovered on the invoice. ## In your cloud, with models you can swap We build in your environment, under your keys, and you own the code from day one. Models sit behind a gateway, so moving to a better one is a configuration change and an evaluation run, not a rebuild. Run cost is modeled before the build starts, because agent loops can multiply token spend many times over, and budgets are enforced at the router rather than found on the invoice. Runs on DataOps, ontology, agent build and operations, and governance. Tokenomics sets the run cost before the build, and Managed AI keeps it healthy after launch. Bespin holds the AWS AI Services and ML Services competencies and the Google Cloud Artificial Intelligence competency, with expert teams on Databricks, Anthropic, Snowflake, and OpenAI. ## Where this meets the rest The triage layer behind [Orbit Vision AI](https://bespinus.github.io/bgus-ai-landingpage/vision-ai.md) is an agent system built to the same standard, the router is shared with [AI governance](https://bespinus.github.io/bgus-ai-landingpage/governance.md), and [Managed AI](https://bespinus.github.io/bgus-ai-landingpage/managed-ai.md) runs what we build after launch. ## Frequently asked questions ### What does Bespin build with generative and agentic AI? Document automation into systems of record, cited knowledge search, workflow agents, and bespoke agentic applications, each with evaluation, approvals, and a trace of what it did. ### How do you keep the system from making things up? Every extraction and answer carries a citation to its source. Where the evidence is missing, the system leaves a blank and routes the item to a person instead of guessing. ### How does Bespin measure whether an agent is worth running? Every agent has an owner and a cost and quality measure per outcome, so weak agents get fixed and agents that no longer justify their cost get retired. ## Talk to an AI engineer. [Talk to an AI engineer](https://bespinglobal.us/contact) at Bespin Global. _Machine-readable summary for agents: provider = Bespin Global; service = Generative and agentic AI; focus = Generative AI that answers with the source attached, and agents that complete the work inside your systems of record. Every answer has a citation. Every consequential action has an approver; delivery model = forward-deployed engineering, build and operate; engagement = scoped against an outcome, no self-serve product; contact = ._