AI agent delivery

An agent is not a prompt with a user interface wrapped around it. The ones that get used can read business data, call the systems you already run, hand the task back to a person when confidence is low, and leave an audit trail for every output. We deliver per scenario — one scenario, one agent with acceptance criteria — and get one working before starting the next. We do not build one-shot platforms.

Who it is for

• The scenario and its acceptance criteria are already clear, either from your own work or from an advisory or service design engagement
• Usable data sources and system interfaces exist, or you are willing to let us do the data preparation
• The work is repetitive and needs to scale: review, quality control, content production, information extraction, reporting

Not a fit:

• Scenarios where the cost of an error is extreme and no human review step can be inserted.

How we run it

1. Decompose the task. Break the scenario into steps an agent can complete on its own, and mark the ones that require human confirmation.
2. Connect the data. Organise the knowledge sources, build the retrieval layer, and state which data never reaches the model.
3. Build the agent. Workflow and prompts, tool and system API integration, failure fallbacks.
4. Tune accuracy. Build an evaluation set from real historical data and hold release until it clears the acceptance line.
5. Hand over. Operations manual, evaluation set and iteration process are handed to your team.

What you get

• A running agent, including workflow, prompts and tool integrations
• Evaluation set and accuracy report
• Human fallback process and permission design
• Operations manual and iteration guide

Related cases

Questions we get asked

What has been delivered so far?

Guomai Culture: 400 AI reading-companion agents on a knowledge graph covering 10,000 books. SpeedLogs: ControlTower, RiskClaw and SupplierEvaluation. Think-Bridge: VatClaw, a VAT filing automation agent. Those four custom agents are not listed on ClawdMart; they were built for a specific client and may become standard SKUs later.

Do you build on our infrastructure or yours?

Yours, in almost every case. Where data cannot leave your environment we design for private deployment, and the selection engagement covers that trade-off explicitly.

What happens when the agent is wrong?

It is designed to be wrong safely: confidence thresholds route uncertain cases to a person, every output is traceable to its inputs, and the evaluation set measures the error rate before release rather than after.

Can we buy an off-the-shelf agent instead?

Eight are listed on ClawdMart as subscription products. If one of them fits your task, buying it is cheaper than a delivery project.

Related services

• Hands-on implementation support
• ClawdMart