Agentic AI

Agents that work, not just talk.

An AI agent reads your systems, runs calculations, prepares actions and submits them to you. Well designed, it saves hours. Poorly supervised, it makes things up. Our job is to design it, and to keep it in check.

Four kinds of agents

The right agent for the right job.

For leadership

The decision assistant

It answers leadership's questions about their own data, with figures to back it up. It cites its sources, flags what is estimated, and offers to open the relevant screen, already filtered.

read-onlysourced answers
For operations

The operator agent

It carries out tasks in your tools: completing records, sorting, preparing an import, sending reminders. Every sensitive action waits for your approval before it goes out.

prepared actionshuman approval
For your applications

The development agent

Embedded in your team, it keeps your applications moving forward: fixes, new screens, more reliable imports. Every release is tested and reviewed by a human.

systematic testinghuman reviewtraceability
For sensitive data

The offline agent

It runs on a model installed on your premises, with no internet connection. Your data never leaves the building, and the agent works just as well in a demo as in the workshop.

local modelno outbound data

Guardrails

What a BigWave agent never does.

  • Make up a figure: calculations are run by code, and the model only presents them.
  • Act without a trace: every action is logged, with its inputs and its result.
  • Change your data without consent: a sensitive action waits for explicit approval.
  • Exceed its budget: AI costs are measured per task.
  • Ignore its providers' terms of use: we check them before using any provider.
log · operator agentsimulation · fictitious data
09:02:11Reads 1,240 incomplete product recordsok
09:02:48Completes 1,108 records (descriptions, specifications)ok
09:03:05Sets aside 132 records: invalid EAN or ambiguous productto review
09:03:06Quality check on 50 randomly sampled recordsok
09:03:07Prepares the import into the catalogueawaiting approval
09:03:07AI cost of the task: €3.40under budget

How we build an agent

Small, measured, then scaled.

STEP 1

One well-chosen task

Frequent, time-consuming, with an output that is easy to check. We measure the time it takes today.

STEP 2

One agent, under control

It works alongside the team, which approves every output. We measure quality, time saved and cost.

STEP 3

Autonomy, where it is earned

Tasks that pass the checks gain autonomy. The others still require approval.

Which task would you hand to an agent?

Let's talk →