AI Agent Development That Works

Myth Digital is an AI agent development company in Belfast and London. Our senior in-house team builds agents that do real operational work: routing documents, extracting data, triaging approvals and answering queries from approved sources. Not chatbots, but production software with guardrails, confidence scoring and human review built in.

  • Strict guardrails built in
  • Working build early
  • Connects to your systems
100% In-house UK team
300+ Projects delivered in 10+ years
Since 2015 The year Myth was founded
Myth Digital team collaborating during an in-person discovery session at their Belfast office, reviewing workflow diagrams on a whiteboard

A chatbot holds a conversation. An AI agent does the work. We build agents that read documents, update records, route approvals and trigger workflows inside your existing systems.

The difference is action. A chatbot answers questions. An agent carries them out, extracting data, moving tasks forward, updating your CRM or ERP without anyone copying between tabs. Our agents connect to the systems you already run. They follow strict rules about what they can and can't do. Low-confidence decisions get flagged for human review. Every action is logged. That's useful autonomy with clear guardrails, built by our in-house team across AI development and workflow automation.

AlphaVictor
Bushmills
Coca-Cola
Complieye
Elecsys
Glen Dimplex
LifePass
The Lir
NHS
The North Face
Perks
Queen's University
Deep RiverRock
SPAR

What processes are worth automating with AI agents?

We build agents for repeatable, rules-based processes with clear success criteria. Here are four we deliver regularly.

  • Document handling

    Data Extraction & Routing

    Staff spend hours pulling data from invoices and contracts. An agent extracts the fields, routes them into your systems and flags anything it isn't confident about for a human to check.

  • Approval workflows

    Checks, Routing & Audit Trails

    Every purchase order, leave request or compliance sign-off follows a chain of approvals. We build agents that check the criteria, route to the right person and log the full trail. No chasing, no gaps.

  • Customer queries

    Triage & First-Line Resolution

    Order status checks, returns and booking changes all follow clear rules. Most get resolved automatically. The exceptions escalate to your team with full context attached.

Phone on concrete showing an AI assistant chat

Guardrails that keep agents accountable

We build agents with strict controls so they stay inside their remit and your team stays in charge. Confidence thresholds route uncertain actions to human review. Every decision is logged for audit.

  • Confidence scoring triggers human escalation
  • Intent boundaries limit permitted actions
  • Full audit trail on every decision
  • Role-based permissions restrict sensitive tasks

How do AI agents reach your systems?

Agents deliver value only when they connect to your existing tools and data.

CRM and ERP Connections
Team member presenting a system diagram on a whiteboard to colleagues

CRM and ERP Connections

Your agent needs live business data to act.

  • We build API connectors so agents read and write to your CRM, ERP or line-of-business tools directly
  • Every connection is scoped to the minimum data the agent needs
Databases and File Stores
Pointing at a system architecture diagram on a whiteboard

Databases and File Stores

Agents need internal databases and document stores.

  • We build connectors so agents query databases and fetch files on demand
  • Access is tightly scoped to only the data required
Legacy System Integration
Office meeting room with smart-workspace app overlay

Legacy System Integration

Older platforms rarely offer modern APIs.

  • We've connected agents to legacy platforms using automated UI-level interactions where no API existed, so your agent works with what you have
  • No forced migration required
Auth, Audit and Compliance
Compliance dashboard with stats and a UK map

Auth, Audit and Compliance

Every integration respects your security posture.

  • Role-based permissions control what the agent can access and change
  • Full audit trails log every action for compliance review
Data-Flow Mapping in Discovery
Person at a large monitor running a data-heavy web dashboard

Data-Flow Mapping in Discovery

We map your data flows before writing a line of code.

  • Discovery sessions identify every system, dataset and handoff the agent will touch
  • The result is a clear integration plan with no surprises at build time
Myth Digital team at work in the studio workflow

What this looks like in production

We built an AI assistant for QualiT that sits inside their construction operations. It answers staff queries from approved knowledge only, returns cited sources with every reply and shows a confidence score. Low-confidence queries route to a human reviewer. It's live and working daily.

  • Answers only from approved knowledge
  • Every reply cites its source
  • Low-confidence queries go to humans

Why choose Myth as your AI agent development partner?

AlphaVictor app Make it happen The North Face campaign AI development Newell Stores app Software development Juice Jar app App development MPS Data Discovery to deployment The Lir website In-house UK team

How does an AI agent development project work?

You see working software at every stage. Here's how a typical engagement runs.

  1. Discovery and scoping

    We map workflows, data sources and integrations. Then we produce a scoped plan with clear success criteria before any build starts.

  2. Agent design and guardrails

    We define intent boundaries, confidence thresholds and human-review triggers. You approve the agent's remit before code begins.

  3. Pilot build with demo

    A focused pilot on one workflow, typically ready in a matter of weeks. You test a working agent against real tasks and give us direct feedback.

  4. Integration and data connection

    We connect the agent to your systems via APIs and custom adapters. No forced migration.

  5. Testing, compliance and review

    Validation, confidence scoring and audit trails go in here. Low-confidence outputs route to human review automatically. Nothing slips through unchecked.

  6. Production deployment and monitoring

    Your agent goes live with real-time monitoring and alerts. Most projects reach full production within a few months of discovery, depending on the number of integrations involved.

Myth Digital team at work in the studio
Our track record

Delivering production software since 2015

Since 2015
Building production software
2 offices
UK offices, Belfast and London
300+
Projects delivered
  • In-house Belfast and London team

    Senior engineers, designers and AI specialists working from our own offices. The people you meet build your product. No offshoring or subcontracting.

  • Phased, milestone-based delivery

    Every project follows clear milestones with working builds at each stage, so you review, sign off and steer direction throughout.

  • AI running in production

    Our AI systems run real operational work today, with confidence scoring, human-review triggers and built-in monitoring.

  • Cross-sector experience

    We've shipped projects in construction, compliance, sports and retail, so we understand different data flows and regulatory pressures.

  • Design and development together

    UX research, interface design and engineering sit in the same team, cutting handoff gaps and speeding iteration.

Meeting with a spreadsheet open on a laptop

You don't need your own models

Proprietary models and vast datasets aren't prerequisites. Agents can use third-party language models connected to your documents, databases or knowledge bases. If compliance requires it, we can run private models instead. In most cases we favour retrieval-augmented generation (RAG) over fine-tuning, because it lets the agent draw on your live data without retraining. The critical factor is the quality and accessibility of your business data. During our feasibility work, we audit what you have and flag gaps before any build begins.

  • Third-party or private models, your choice
  • Start from the data you already have
  • We audit data quality before building

Is an AI Agent Right?

Not every problem needs an agent. Here's a quick way to tell whether your business is ready.

A great fit

  • You have a repeatable, rules-based process to automate
  • Clear success criteria exist for the task
  • Systems and data are already in place to connect to
  • You're willing to start with a scoped pilot
  • A team member can review outputs during rollout

Not the right fit

  • No defined process or workflow to hand off
  • You expect fully autonomous decisions with zero human oversight
  • No existing data, documents or systems to work from
  • You only need a generic chatbot widget
  • No internal stakeholder available to test and give feedback

Recent projects

FAQ

AI agent development FAQs

What is an AI agent and how is it different from a chatbot?

A chatbot holds a conversation. An agent takes action. It reads documents, extracts data, updates records, routes approvals and triggers workflows inside your systems, within rules that define what it may and may not do. Anything uncertain goes to a person.

What business processes are worth automating with AI agents?

Repeatable, rules-based work with clear success criteria. Invoice and contract data extraction, document routing, approval triage, customer queries answered from approved knowledge, and internal questions about policies and procedures. If a task follows a checklist today, an agent can usually take it on.

How do you stop an AI agent making mistakes or acting outside its remit?

Every agent we build has boundaries that limit what it can do, confidence thresholds that send uncertain cases to a human reviewer, and an audit trail of every decision. Its access is scoped to the minimum data the task needs.

Can an AI agent work with our existing systems and data?

Yes. Agents connect to your CRM, ERP, databases and document stores through secure connections we build for each system. The agent reads and writes only what it is permitted to, and your existing tools stay in place.

How much does AI agent development cost?

We start with a scoped pilot so you can see an agent working on your real process before committing further. The pricing tiers on our AI development services page give the starting points for a proof of concept, a production build and ongoing support, and you receive a fixed proposal after discovery.

How long does it take to build an AI agent?

A scoped pilot typically takes two to four weeks. Taking it to production, with integrations, guardrails and monitoring, usually runs eight to twelve weeks depending on how many systems it touches. You see working software at each stage.

Do we need our own data or models to build an agent?

No. Most agents use a leading third-party language model connected to your documents, databases or knowledge base through retrieval, so the agent draws on your content without training a model. Where compliance requires it, we can run private models instead.

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