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Custom Agent and App Development

Agents and AI applications built for one workflow and run in production.

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The work around the agent

An AI agent needs more than a chat window.

Your team needs to check results, approve actions and see what’s been done. We build agents into the tools they already use, with clear charts, tables and approval steps, so they can put the results to work.

A finance agent in a chat window. Asked why the Brightwater credit hasn’t been approved yet, it says it’s waiting on Dana Ruiz, the controller: it messaged her on Tuesday and this morning with no reply, and the customer email goes out once she says yes.
  1. 1

    Problems are spotted at a glance

    Results land in charts and tables built for the job, so a reviewer sees the odd number right away instead of reading through chat.

    An approvals queue with four items waiting. The Brightwater credit, $4,800, is flagged at 6 days while the others have waited a day or less, and a chart card shows this week’s average wait spiking above the previous five weeks.
  2. 2

    Nothing reaches a customer unchecked

    Refunds, customer emails and record changes stop at an approval step, and a person signs off before they go out.

    An approval card for a $4,800 credit to Brightwater Logistics, over the $2,500 limit, waiting for Dana Ruiz, the controller, to send it back or approve it in the AP platform. The email to the customer below it is not sent.
  3. 3

    Every case runs the same way

    The workflow sets the steps, and the agent drafts, triages and checks inside them. The outcome doesn’t depend on who picked up the case.

    The customer credits workflow: three steps done by the agent, approval of credits over $2,500 by the controller, and posting and sending by the AP platform, with October’s credits on the same five steps: Tidewell sent, Brightwater with the controller, Lumen Studio drafting.
  4. 4

    You can show how every decision was made

    Each step, source and approval is logged, ready for an auditor, a manager or an upset customer.

    The run history for the Brightwater credit: each step the agent took, its ERP and contract sources, and Dana Ruiz’s approval in the AP platform at 9:42 AM, with an Export button.
A finance agent in a chat window. Asked why the Brightwater credit hasn’t been approved yet, it says it’s waiting on Dana Ruiz, the controller: it messaged her on Tuesday and this morning with no reply, and the customer email goes out once she says yes.

What we do

One workflow, in production, with every step on the record.

We build the agent into the surface the team already works in, on AgentFlow, the harness we build on and you own: connections to your systems, memory, approval gates, guardrails, and a trace of every step and dollar. Four of the mechanisms we have shipped:

Reviewer feedback becomes proposed rules a senior engineer approves. A code change cannot alter the rules that review it.

How the work runs

Four steps, one workflow, demoed every week.

  1. 01Embed

    Join the team that owns the workflow and pick the first use case.

  2. 02Architect

    Agree on data access, tools, approval points and evals before any code.

  3. 03Ship

    A working version in your environment, demoed weekly until people use it daily.

  4. 04Hand over

    Code, runbooks and the rule loop. You own the agent and the harness it runs on.

Other services

Seven more ways we put AI to work.

Get to production in 4–6 weeks.

Book a call today.

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