AI agents designed to operate inside the real business
Agents are only useful when they live inside a workflow, respect the controls around it, and move a number the business already tracks. That is the version we build.
The interesting question with AI agents is not what they can do in a demo. It is what they can do reliably, inside your systems, with your data, under your governance. That is a deployment problem, and it is the work we do.
We build agents that take real tasks end to end inside enterprise workflows: research, triage, drafting, structured tool calls, back office automation, sales enablement, customer support assist. They are scoped to the work that matters and instrumented so leadership can see what they are doing.
We pick the right level of autonomy for the task. Some workflows want a tightly scoped tool calling agent. Others want a co pilot with a human in the loop. The right answer comes from the workflow, not from the framework.
How we approach it
Scope to the workflow
We start from a specific high leverage workflow, define the inputs, outputs, and acceptable failure modes, and design the agent around that contract.
Tools and data, first class
Agents are only as good as the tools and data they can reach. We build the integration layer and the retrieval layer with the same care as the model layer.
Evaluation harness from day one
Every agent ships with offline and online evals tied to the workflow metric. Prompt and model changes are measured, not vibe checked.
Guardrails and human review
Permissions are scoped, side effects are bounded, and high risk actions route to a reviewer. The audit trail is complete.
What you get
- Production AI agents embedded inside specific high leverage workflows
- Integration with your existing tools, data, and identity
- Evaluation harness and dashboards tied to the metric that matters
- Governance, audit, and human review appropriate to the risk profile
- A path to expand to adjacent workflows once the first one is proven
Frequently asked
- What are AI agents?
- An AI agent is a system that can take goals, plan steps, call tools, and act inside other software to get work done. In an enterprise context it usually means a workflow that combines language models, retrieval, structured tool calls, and human review to handle a class of tasks end to end.
- Where do AI agents actually work well today?
- Bounded, high frequency workflows with clear inputs and outputs: intake and triage, structured research, sales and operations enablement, document processing, customer support assist, and back office automation. They work less well as open ended autonomous workers and more well as embedded participants in a specific workflow.
- How do you keep agents safe in production?
- Scoped tool permissions, deterministic guardrails on side effects, evaluation harnesses tied to the workflow's real metrics, human in the loop for high risk actions, and full audit logging. Safety is a property of the system around the agent, not the model.
- Do you build custom agents or use frameworks?
- Both, pragmatically. We use established components where they earn their keep and build custom where the workflow needs it. The objective is the deployment, not the framework choice.
- How does this differ from buying an off the shelf agent product?
- Off the shelf products optimize for a generic workflow. Bespoke agents optimize for your workflow, your data, and your constraints, which is usually where the real value lives. We build when the leverage justifies the build.