AI Development

AI Agent Development for Reliable Multi-Step Automation

We build AI agents that plan and carry out tasks across your systems — researching, updating records and resolving requests — with guardrails, approvals and full audit trails.

• See related work

What an AI agent is — and is not

An AI agent is a language model given a goal, a set of tools (APIs, database queries, search) and the ability to decide which steps to take. Instead of following a fixed script, it can look something up, decide what to do next, call another system and report back.

That flexibility is powerful, but it needs careful engineering. A well-built agent has a narrow remit, limited permissions, clear stopping conditions and a human checkpoint before anything irreversible happens. Our agent work sits within our broader AI development practice.

Good first use cases for agents

• Sales research: gather information on a prospect from your CRM, website and approved sources, then draft a tailored briefing. See our AI sales assistant for B2B project.
• Support resolution: read a ticket, check order status and account history, and either resolve it or prepare a fully researched hand-over.
• Back-office reconciliation: match invoices, payments and purchase orders, investigate mismatches and propose corrections.
• IT and operations: triage alerts, gather diagnostics and run pre-approved remediation steps.

The pattern: tasks that currently need someone to open four systems, copy information between them and make a routine judgement.

How we build agents safely

Scoped tools, not open access

Each agent gets a small set of purpose-built tools — get_order, create_credit_note, search_knowledge_base — with typed inputs, validation and their own permission checks. It never gets raw database credentials.

Human-in-the-loop by default

Actions that spend money, contact customers or change important records go to an approval queue. As confidence grows, you can promote low-risk actions to fully automatic.

Observability

Every run is traced: the plan, each tool call, inputs, outputs and the final result. That makes failures debuggable and gives auditors a clear record.

Evaluation on real scenarios

We build scenario test suites from historical cases and measure task success rate, cost and time per task before and after each change.

Technology

We build agents on Anthropic Claude and OpenAI models using tool calling and the Model Context Protocol (MCP) where it fits, orchestrated from .NET or Python services. Agents run as background workers in your cloud and are triggered by events, schedules or users.

Agents vs traditional automation

If a process is fully predictable, classic business automation is cheaper and more reliable. Agents earn their keep where inputs vary and some judgement is needed. Often the best design combines both.

• What you get

  • Production AI agent

    Running as a monitored service in your cloud environment.

  • Tool library

    Documented, tested tools the agent (and future agents) can use.

  • Approval and trace UI

    Interface for reviewing pending actions and past runs.

  • Risk and autonomy policy

    Documented rules for what the agent may do alone and what needs sign-off.

• Industries we serve

  • Professional Services

    Client portals, CRM, project and time tracking, billing and AI-assisted document workflows for consultancies, agencies, law and accountancy firms.

  • E-commerce

    Custom e-commerce platforms, headless storefronts and back-office integrations for online retailers who have outgrown templates and manual order handling.

  • Logistics

    Fleet tracking, driver apps, dispatch and warehouse software for logistics, courier and distribution companies that need live visibility and proof of delivery.

Benefits

Why invest in ai agent development

• Handles variable work

  Agents cope with messy, varied requests that break rigid rule-based automation.

• Fewer system hops

  One agent gathers information from several systems that staff would otherwise search manually.

• Human control where it counts

  Approval queues keep people in charge of sensitive or irreversible actions.

• Fully auditable

  Step-by-step traces show exactly what the agent did and why.

Capabilities

What we build and deliver

• Tool and API integration

  Purpose-built, permission-checked tools that connect agents to CRM, ERP, helpdesk and internal APIs.

• Multi-step planning

  Agents break goals into steps, adapt when results are unexpected and know when to stop.

• Approval workflows

  Configurable human approval for actions above a risk or value threshold.

• Run tracing

  Full traces of plans, tool calls and outputs, searchable for debugging and audits.

• Event and schedule triggers

  Start agents from webhooks, queue messages, schedules or user requests.

• Scenario test suites

  Historical cases replayed to measure success rate before each release.

Process

How we deliver

A transparent, milestone-based process with working software at every stage.

• Task analysis

  Shadow the current process, list systems touched and decisions made, and define the agent's remit.

• Tool design

  Design the minimal set of safe, typed tools the agent needs, with permissions and validation.

• Agent prototype

  Build the agent loop and test it on historical scenarios in a sandbox environment.

• Supervised pilot

  Run the agent alongside staff with all actions requiring approval; measure accuracy and time saved.

• Graduated autonomy

  Promote proven low-risk actions to automatic and expand the agent's scope step by step.

Technologies

Technology we use for this service

• Anthropic Claude

  We build AI assistants, agents and document workflows on Anthropic's Claude models, known for long-context reasoning, careful instruction following and strong writing.

• OpenAI

  We integrate OpenAI models into business software for assistants, document processing, search and content workflows, with grounding, evaluation and cost controls.

• AI & Machine Learning

  Applied AI and machine learning for forecasting, classification, recommendations and document understanding, alongside large language models where they add value.

• .NET

  We use Microsoft .NET to build fast, secure and maintainable business software: web APIs, ERP and POS back ends, background services and cross-platform apps.

Related work

Projects we've delivered

• AI Sales Assistant for B2B Teams

  An LLM-powered sales assistant that qualifies inbound leads, drafts tailored follow-ups and summarises accounts from CRM data for a B2B industrial supplier.

• Fleet Logistics Tracking App

  A driver app and dispatch dashboard for a regional delivery company, with live vehicle tracking, digital proof of delivery and optimised route sequencing.

• Manufacturing ERP Rollout

  A custom ERP for a mid-sized metal fabrication manufacturer, connecting sales orders, production planning, inventory, purchasing and finance in one system.

Case studies

Results from similar engagements

• AI Support Assistant for a B2B SaaS Company

  How a B2B SaaS company deployed a grounded AI support assistant that resolves 42% of conversations on its own and hands the rest to agents with full context.

• Retail Chain POS Modernisation

  How a 60-store retail chain replaced ageing on-premise tills with an offline-capable cloud POS, cutting checkout times by 35% and ending overnight price syncs.

What our clients say

• The assistant resolves the repetitive how-do-I questions and hands everything else to us with a proper summary. Our agents spend their time on the tricky tickets now, and customers get answers at 2am without us adding a night shift.

• We used to lose half a day's takings every time a store's broadband went down. Since the new tills went in, outages barely register, and head office can change a price at lunchtime and see it on every shelf-edge promotion by the afternoon.

• Parents now have one app instead of four, and our office teams have stopped spending Monday mornings chasing absence calls. The migration was the part we worried about most, and the trial runs meant there were no surprises on go-live day.

Solutions

Related software solutions

• AI Business Automation

  AI business automation platform that reads documents, classifies requests and runs multi-step workflows across your systems, with human approval where it matters.

• Lead Management Software

  Lead management software that captures enquiries from every channel, scores and routes them instantly and tracks follow-up, so no lead goes cold and marketing sees what converts.

• AI Customer Support

  AI customer support assistant that answers questions from your own knowledge base, takes actions through your systems and hands off to human agents with full context.

FAQ

Frequently asked questions

• What tasks are AI agents good at?

  Tasks that involve gathering information from several systems, applying routine judgement and taking a follow-up action — such as researching prospects, resolving support tickets or reconciling transactions. Fully predictable tasks are usually better served by traditional automation.

• Can an AI agent make mistakes?

  Yes, which is why we limit its tools and permissions, require approval for sensitive actions and test it against historical scenarios. Autonomy is increased only for actions with a proven track record.

• Which models do you use to build agents?

  Mostly Anthropic Claude and OpenAI models with native tool calling. We pick based on measured task success, speed and cost for your scenarios.

• How long does it take to build an AI agent?

  A focused agent with a handful of tools typically reaches a supervised pilot in 6–10 weeks, including tool design, prototyping and scenario testing.

Insights

Further reading

• AI Web Application Development: A Practical Guide

  A practical guide to AI web application development: use cases, architecture, retrieval-augmented generation, choosing LLMs, security, evaluation, costs and delivery steps.

• How Much Does Custom Software Development Cost?

  What drives custom software development cost: scope, integrations, team, compliance and support, with indicative ranges by project type and tips for controlling your budget.

• ERP vs CRM: What's the Difference and Which Do You Need?

  ERP vs CRM explained: what each system does, how they differ, where they overlap, when you need one or both, and how to integrate them without duplicating data.

Wondering if an agent could do it?

Describe the task and the systems involved — we will assess whether an agent, classic automation or a mix is the better fit.

Describe the task and the systems involved — we will assess whether an agent, classic automation or a mix is the better fit.

Contact

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