AI Agent Development
AI agents that do the work your team shouldn't.
Custom agents and agent teams, engineered for production: tool use, memory, orchestration, and a human in the loop where it matters. Not chatbots. Systems that run real operations against real data.
Definitions
What is an AI agent?
Two terms get used loosely in this space. Here is exactly what we mean by them, and why the difference is architecture, not intelligence.
AI agent
An AI agent is software that receives a goal, works out how to achieve it, and executes the steps autonomously. It uses a large language model as its reasoning engine and acts through tools: your databases, APIs, email, and internal systems. A chatbot answers questions. An agent does work.
Think of the difference between a search engine and a research assistant. One gives you links; the other reads the sources, synthesises the findings, and delivers a brief.
Agentic engineering
What is agentic engineering?
Agentic engineering is the discipline of making agents reliable enough for production. Decomposing goals into steps, selecting the right tools, maintaining context across long-running tasks, recovering from failures, and escalating to a human at the right moment.
Getting any one of those right is hard. Getting all of them right at once is what separates demo-quality AI from systems a business can trust with real work.
What we build
Real agents for real businesses.
None of this is theoretical. These are the systems we build and run in production for clients across the UK and Ireland.
See the workEmail and communications agents
01Triage inbound email, draft responses, route messages to the right team, and follow up on outstanding threads. They read context, not just keywords.
Data processing agents
02Ingest unstructured data, documents, PDFs, spreadsheets, web pages, extract what matters, and load it into your systems. Intelligent ETL that understands content.
Customer service agents
03Handle customer queries, look up account information, resolve common issues, and escalate intelligently. Not a decision tree. An agent that understands the problem.
Operations and workflow agents
04Run on schedules: daily reports, weekly reviews, continuous monitoring. They check systems, flag anomalies, and take action without waiting for a prompt.
The agentic stack
Five layers of agentic infrastructure.
A production agent system is not one thing. It is five layers working together, and missing any one of them is how demos die on the way to production.
The reasoning engine
01Large language models
The foundation. Claude, GPT-class, and open-source models provide the reasoning capability. But a model alone is not an agent, it is a component.
- Model selection based on task requirements
- Prompt engineering for reliable, structured output
- Cost optimisation across model tiers
- Fallback chains for resilience
How agents act on the world
02Tool use
Agents need hands, not just a brain. Tool use lets them read files, query databases, call APIs, write code, and interact with any system you expose to them.
- Tool definition and schema design
- Permission boundaries and sandboxing
- Error handling and retry logic
- Composable tool libraries
How agents think through problems
03Planning and reasoning
Decomposing a complex goal into steps, sequencing them correctly, and adapting the plan when things change. This is where most agent implementations fail.
- Goal decomposition into subtasks
- Dynamic replanning on failure
- Chain-of-thought and structured reasoning
- Confidence scoring and escalation triggers
How agents learn and remember
04Memory and context
Short-term working memory for the current task. Long-term memory for patterns, preferences, and domain knowledge. Without memory, every task starts from zero.
- Conversation and task context management
- Retrieval-augmented generation (RAG)
- Vector stores and embedding pipelines
- Knowledge base curation and maintenance
How agents coordinate
05Orchestration
The management layer that schedules agents, routes tasks, enforces budgets, handles approvals, and keeps everything running. This is the factory floor.
- Multi-agent coordination and handoffs
- Routine scheduling and triggers
- Budget management and cost controls
- Human-in-the-loop approval flows
How it works
From problem to production agent.
The same forward deployed model we use for everything we ship: embed, learn the problem by building, own the outcome in production.
Discovery
01Understand the problem
We map the workflow you want to automate. What triggers it, what decisions are involved, what the output looks like, and where humans need to stay in the loop.
Design
02Architect the agent
We design the agent system: which models, which tools, what memory it needs, how it handles failures, and how it escalates. No guesswork.
Build
03Ship to production
We build, test, and deploy. Our engineers embed with your team so the system integrates cleanly with your existing infrastructure. Typical build: 4-8 weeks.
Scoped like everything we do: Discovery to de-risk it, then Build to ship it.
Why us
This is hard to do right.
Agentic engineering is a new discipline. The tooling changes monthly, the failure modes are non-obvious, and very few teams have shipped a production agent system.
The gap between a demo agent and a production agent is enormous. Demos work in happy-path conditions with clean inputs and a human watching. Production agents handle ambiguous inputs, recover from failures, manage costs, respect rate limits, and know when to stop.
Teams that have operated agent systems in production know which architectures scale and which collapse, which orchestration patterns hold and which are brittle. That experience cannot be read out of documentation, and hiring for it is brutally hard.
We built the company around this work. Our tooling, processes, and delivery model are designed for building AI systems, and we run agents in our own operations: code review, research, monitoring, client delivery. We are not learning on your project.
And we work as forward deployed engineers: senior engineers embedded with your team, owning the outcome in production, not a black box that delivers a handoff.
FAQ
Common questions.
Let's talk
Tell us what you're building.
One conversation. An honest take. No commitment until it makes sense.
Based in Belfast, working with teams globally