AI Development

AI Agent Development

Assistants that complete multi-step work, not just answer questions.

Typical timeline: 6-12 weeks for a single production process
Category: AI Development
Engagement: Fixed-scope phases
You receive: Code, data & documentation

A chatbot responds. An agent completes a task: read the incoming email, extract the purchase order, check stock, draft the quotation, and queue it for approval. We build agents for narrow, well-defined processes where the steps are known and the value of removing them is clear.

The problem

Skilled staff spend their days moving information between systems

Someone reads an email, copies values into a spreadsheet, looks something up in another system, and writes a reply. The work requires judgement at one or two points and is purely mechanical everywhere else — but because of those judgement points, it has never been automated.

You will recognise this if

Experienced employees spend hours on copy-paste work between applications
Processing an enquiry, order or claim takes days mostly because of handoffs and queues
Rules-based automation was tried and failed because real inputs are messy and unstructured
Backlogs build up whenever one person is on leave

What you get out of it

The difference this makes

Whole tasks completed, not just answered

The agent performs the steps — reading, extracting, looking up, drafting, recording — and presents a finished result for approval.

Handles messy real-world input

Unlike rigid rule-based automation, it copes with free-text emails, inconsistent formats and incomplete information.

Humans stay in control

Approval gates sit before anything consequential — sending to a customer, committing stock, making a payment — so judgement remains with your team.

Every action is traceable

Each step, input and decision is logged, so when something goes wrong you can see exactly where and why.

What we build

Process mapping to identify which steps should be automated and which should not
Tool-using agent with controlled access to only the systems it needs
Human-in-the-loop approval queues for consequential actions
Confidence thresholds that escalate uncertain cases to a person
Full audit trail of inputs, reasoning summaries, actions and outcomes
Retry and fallback handling when an external system is unavailable
Evaluation suite that tests the agent against real historical cases before launch
Cost and usage monitoring with hard spend limits

How it gets used

A supplier processing emailed purchase orders

The agent reads each PO attachment, extracts line items, matches them to catalogue codes, flags price or stock mismatches, and prepares a sales order for a human to approve.

A services firm triaging inbound enquiries

Incoming email and form enquiries are classified, enriched, assigned to the right team and answered with a drafted reply the owner can edit and send.

An accounts team reconciling payments

Bank narration lines are matched against outstanding invoices, confident matches are posted, and ambiguous ones are queued with the agent's suggested options.

Technology we typically use

You do not need to care about this list — but your next developer will.

Claude API with tool use
Python with FastAPI
Task queues (Celery / Redis)
PostgreSQL
n8n
Evaluation harness

Timeline: Typically 6-12 weeks for a single production process

Questions

AI Agent Development — common questions

Something else on your mind? Ask us directly — it is usually faster than reading.

What happens when the agent gets something wrong?

The design assumes it will. Consequential actions sit behind human approval, low-confidence cases are escalated rather than guessed, every action is logged and reversible where possible, and we track an accuracy figure you can see. Processes where a mistake cannot be caught or undone are not suitable for an agent.

Will it replace staff?

In practice it removes the mechanical portion of a role and leaves the judgement, exceptions and customer relationships. Teams typically handle more volume rather than shrink. We would rather set that expectation honestly than oversell a headcount reduction.

How do we know it is accurate before trusting it?

We run it against a set of real historical cases where the correct outcome is already known, and report measured accuracy. It then runs in shadow mode — producing results that a person reviews without acting on them — until the numbers justify giving it responsibility.

Can it access our existing systems?

Where those systems have an API or a database we can read, yes, with credentials scoped to the minimum required and all access logged. Systems with no integration path may need a file-based exchange or a lightweight adapter, which we would assess during discovery.

Businesses that need this often need one of these as well.

AI Business Process AutomationAutomate the processes that defeated rule-based tools.Document Intelligence & Data ExtractionTurn invoices, forms and purchase orders into structured data automatically.RAG Knowledge Assistant DevelopmentAsk questions of your own documents and get sourced answers.API Integration ServicesMake the systems you already pay for talk to each other.

Thinking about ai agent development?

Tell us about your situation. We will tell you whether this is the right service, what it would involve, and roughly what it would cost.

Get a free consultation