Enterprise AI Agent Development

Agentic workflows that use your tools and data, run on models inside your perimeter, and keep a human approval step and an audit log for every action.

Most agent demos work once, on a clean example, with a model API on the other end. Production is harder. The agent has to call your systems with the right permissions, read documents that are messy and sensitive, recover when a tool fails, and leave a record that a reviewer can follow later. Our AI agents practice builds that version: agentic workflows that run on your infrastructure, are measured on your tasks and are owned by your team.

What we build

• Agentic workflows with tool calling that connect a model to your internal APIs, databases and search, each tool with its own scoped permissions.
• Retrieval over internal data, so agents ground their work in your own documents and record which sources they used.
• Document-processing agents that extract, classify, compare and summarize contracts, reports and forms, then route the result to the right reviewer.
• Multi-agent orchestration for longer tasks, where specialized agents handle planning, research and checking, and a coordinator tracks state between them.
• Evaluation harnesses that score agents on realistic tasks, including tool use and failure recovery, before and after every change.
• Human-in-the-loop approval for actions that matter, with clear review screens and the ability to correct the agent before anything is committed.
• Governance and audit logging that records every prompt, tool call, output and model version inside your environment.

Agents that run inside your perimeter

An agent sees more of your data than a chatbot does, because it reads documents and queries systems on its own. For regulated organizations that makes the deployment model the first decision. We build agents on open-weight models served on your hardware or in an in-country data center, the same sovereign AI approach described in LLM Engineering.

Agents also make many model calls per task, so inference speed and cost decide whether a workflow is usable. With Avian.io and NVIDIA, our team set a DeepSeek R1 inference world record: 303 tokens per second in FP4 on NVIDIA Blackwell, verified by Artificial Analysis. In financial deployments we have run inference at P99 latency under 50 ms. We apply that work to the models behind your agents.

For Believe, we built a multi-agent LLM pipeline in a LangGraph state graph that reads full music-label contracts in French, English and German, classifies the deal, extracts counterparts and royalty terms into a typed schema and checks each contract against 70 operating rules. Contract type classification went from 40% to 98.2%, with field-level extraction at 91-93% concordance against reviewer ground truth, and the review application is in production with four business reviewers. See the Believe contract review case study.

The team has also built on-prem LLM tooling and document intelligence inside a tier-1 banking perimeter at JP Morgan, where that work reduced manual review by 60%. That experience shapes how we design agents for Banking & Finance and the wider Finance AI practice.

How an engagement runs

An agent project moves through four phases, and your engineers and the people who will review the agent's output take part in each one.

1. Scoping. We map one workflow step by step: who does it today, which systems they touch, which decisions need a person and what a correct result looks like. From that we write the task set the agent will be scored on.
2. Research. We build a first agent against that task set, compare models and tool designs, and study where it fails. The failures tell us which steps need better retrieval, tighter tools or a human check.
3. Production. We connect the agent to your identity, permissions and logging, add approval steps and rate limits, and test it on real volumes with your reviewers in the loop.
4. Handover. You receive the code, prompts, tool definitions, evaluation suite and runbooks. We work alongside your team until they can add tools, swap models and rerun the evaluations without us.

Deployment and governance considerations

• Least privilege. Each agent can only call the tools and see the data its task requires, using the same access controls your staff already have.
• Approval where it counts. Reading and drafting can be automatic. Sending, paying, filing or changing records goes through a person by default.
• Replayable logs. Every step is logged inside your infrastructure with inputs, outputs and model versions, so a reviewer can reconstruct any run.
• Measured before it ships. Changes to prompts, tools or models are checked against the evaluation suite, and regressions block the release.
• Open components. We favor open-weight models and open-source frameworks where they fit, so the system keeps running without a single vendor.

If you have a workflow in mind, or want help choosing the first one, contact us.