Questions about AI in your operations.

An agentic workflow is software that starts when something happens in your systems, gathers the context it needs, and uses an AI model for the steps that need reading or judgement. It then takes a bounded action or asks a person to approve it.

Thresholds, permissions, and approval rules are plain code. The model cannot change them.

A fixed rule or RPA script works when the decision can be written as a condition and the input always arrives in the same structure. An agentic workflow is for steps where the input needs reading or judgement, such as a carrier email or a free-text reason, but the possible actions and their limits are known.

Most processes need both, plus a person for rare or high-stakes decisions. We decide per step, and a step only gets a model when a fixed rule cannot do the job.

Processes with repeatable decisions, usable data, costly handoffs, and a clear owner. Typical examples are shipment exceptions, incoming invoices and orders, security policy checks, and unusual patterns in operational data.

Not every process needs AI. Sometimes the right answer is a fixed rule, a better report, or leaving the work with a person.

No. The workflow connects to the tools your team already uses, such as an ERP or TMS, a CRM, shared inboxes, and Teams, Slack, or email. Results and approval requests appear there, not in another place to check.

Only where you decide it can. Each step has one of four autonomy levels: auto for reads and reversible internal updates, approve for actions a named person signs off, recommend for proposals a person carries out, and never for actions the workflow has no credentials for.

Anything that costs money, commits your company, or goes to a customer starts at approve or recommend. A step moves to a higher level only after its production numbers support it.

Before launch we replay a few hundred historical cases and compare each decision with what your team actually did. In production, output that fails validation is retried once and then goes to a person, and low-confidence cases drop to recommend so nothing is written without a person.

If a source system or the model provider is down, runs wait or queue and your team works the manual process until it recovers.

It reads from the systems a step needs, limited to the fields that step uses. It has no credentials for actions outside its permissions, and sensitive data stays within the boundaries you define.

Every run is recorded with its inputs, tool calls, decisions, and approvals, so any run can be reviewed or replayed.

We agree the measures before anything is built and review them with your team. They depend on the work: for example, time until the owner knows about a shipment exception, the share of documents that need no manual touch, or how often reviewers agree a flag was worth raising.

With one process that costs your team time. We map how the work actually happens with the people doing it, then recommend a first workflow with its scope, approval points, and success measures. Or we give you a clear answer that AI is not the right tool yet.

A small team covering AI engineering, platform and distributed systems, and product. You work directly with the people who understand the problem and build the solution.