Service · typically 6–8 weeks to a production-grade pilot
AI agents built for production, not for the demo.
Anyone can build an agent that works once. We design and build single- and multi-agent systems that work every day: with tool access to your systems, memory, planning, evaluation suites, guardrails and a deployment on EU infrastructure or in your own cloud. Vendor-neutral, and handed over so your team can run it.
When a custom agent is the right answer
Off-the-shelf assistants cover generic tasks. The work that defines your company needs an agent that knows your systems, your rules and your exceptions.
An AI agent is software that uses a language model to pursue a goal: it reads context, plans steps, calls tools such as your ERP, CRM, document store or ticketing system, checks the result and continues until the task is done or a person needs to decide. Unlike a chatbot, it produces outcomes, not just answers.
Custom development pays off when the process is specific to you, when data cannot leave your control, when a platform's built-in agent cannot reach the systems it needs, or when several agents must cooperate: one researches, one drafts, one checks. Where a standard product does the job, we say so; see our build-vs-buy guide.
Everything we build comes with what makes it operable: evaluation suites, observability, permissions, fallbacks and cost controls. That is the difference between a pilot and a system, and the reason most agents never leave the pilot stage.
- FormatScoping, architecture, build, evaluation, hardening, hand-over
- DurationTypically 6–8 weeks to a production-grade pilot
- ForCTOs, CIOs, heads of digital, product leads, founders
- OutcomeA deployed agent with tests, monitoring, documentation and a trained team
Types of agents we build
Most projects start with one of these. Multi-agent systems combine several behind one orchestrator.
Knowledge agents
Answer questions and prepare documents from your internal knowledge, with citations and permission checks, and look things up in several systems before answering. See our knowledge assistants.
Workflow agents
Embedded in a business process: they receive a case, gather data from your systems, apply your rules, take the next step and escalate when unsure. The backbone of agentic workflow automation.
Customer-facing agents
Chat, email and voice agents that handle enquiries, order status, returns, appointment booking or first-level support within a defined scope, with a clean hand-over to your team and full transcripts. See customer service.
Coding agents
Agents inside your engineering workflow: triaging issues, writing and reviewing code against your standards, generating tests and documentation, migrating legacy code under human review. See IT and engineering.
Browser and computer-use agents
Agents that operate applications and websites the way a person would, for systems without an interface: supplier portals, legacy tools, public registers. Used sparingly, sandboxed and with strict permissions.
Multi-agent systems
Several specialised agents coordinated by an orchestrator: one researches, one drafts, one checks, with clear hand-offs and shared state. Useful where a single agent would become too large to test and too vague to trust.
What production-grade means to us
Evaluation suites
A test set of real cases with expected outcomes, run on every change to prompts, models or tools. No release without a passing score; regressions are caught before users see them.
Observability
Every run is traced: inputs, reasoning steps, tool calls, outputs, latency and cost, visible to your team in tools such as Langfuse or your existing monitoring stack.
Permissions and fallbacks
Agents act with their own identity, least-privilege access and explicit allow-lists for actions. When a model is unavailable, a tool fails or confidence is low, the agent retries, switches model or hands over to a person with full context.
Cost controls
Budgets per run and per day, model routing by task difficulty, caching and rate limits. You know what an agent costs per case before it goes live.
Guardrails
Input and output checks against prompt injection, data leakage and off-policy behaviour, plus content rules specific to your industry. More in our guide to guardrails for agents.
From scoping to hand-over
Scoping
We define the agent's job on one page: goal, inputs, tools, decisions it may and may not take, success metrics, human checkpoints. If the scope is unclear, we prototype for two days before committing.
Architecture and model choice
Single or multi-agent, which framework, which models for which steps, where memory lives, how tools are exposed (APIs, connectors, MCP servers), where it runs. Written down for your IT and security teams to review.
Build
Tools and MCP servers for your systems, the agent logic, the memory layer, the approval interface. We work in your repository from day one, so nothing is a black box.
Evaluation and hardening
The evaluation suite runs against real cases; we tune prompts, routing and tools until the numbers hold. Then we test the unhappy paths: bad inputs, broken tools, injection attempts, cost spikes.
Controlled rollout
The agent goes live for a defined group, first with every action reviewed, then with sampling. Feedback flows back into the evaluation set.
Hand-over
Documentation, runbook, training for your developers and operators, and a joint decision on what comes next: scale-out, a second agent, or managed AI operations by us.
Frameworks, platforms and models we work with
We choose per project and stay vendor-neutral. Naming a product means we build with it and evaluate it, not that we are partnered with or certified by its vendor.
Frequently asked questions
Which framework do you recommend?
None by default. LangGraph suits complex, stateful flows with strict control; CrewAI and the OpenAI Agents SDK get straightforward multi-agent set-ups running quickly; the Anthropic Claude Agent SDK is strong for tool-heavy, long-running work; Copilot Studio, Agentforce, Vertex AI Agent Builder and Bedrock AgentCore make sense when you already live on that platform. We decide after scoping.
Can the agent run on EU infrastructure or on-premises?
Yes. Depending on the requirement we deploy in EU regions of the hyperscalers, with EU-based model providers or on your own infrastructure with open-weight models. Fully on-premises set-ups trade some model quality for control; we show you the difference on your own test cases before you decide. See sovereign AI and EU hosting.
What is an MCP server, and why would we need one?
The Model Context Protocol (MCP) is an open standard that lets an agent use external systems as tools in a uniform way. An MCP server for your ERP, for example, exposes actions such as finding a customer or creating an order with defined permissions, so every agent you build later can reuse it. See MCP explained.
How do you keep the agent from doing something harmful?
Through design rather than hope: least-privilege permissions, allow-lists for actions, approval steps for anything irreversible, input and output guardrails, rate and budget limits, and an evaluation suite that includes adversarial cases. Plus logging that lets you reconstruct every run.
Do we own the code, and how does this relate to your strategy work?
Yes. We work in your repository, on your accounts, with your licences; at hand-over you hold code, evaluation set, documentation and runbook. The AI strategy and readiness assessment identify and rank use cases; this service builds them. If you already have one, we start with a short scoping week.
Related services
Agentic workflow automation
Multi-step business processes automated end to end by LLM-powered agents across ERP, CRM, ticketing and email, with human approval steps built in.
Enterprise knowledge assistants (RAG)
Assistants that answer from your SharePoint, Confluence, DMS, ERP and ticket history: permission-aware, with citations, evaluated, hosted in the EU.
Managed AI operations (AgentOps)
Monitoring, evaluation, model upgrades, cost control and incident handling for agents and LLM applications after go-live, with a monthly review and documentation your auditors can read.
Guardrails for AI agents
Eight layers of control that let an agent act in your systems, a risk-to-guardrail mapping and a worked accounts-payable example.
Let's find the first workflow worth automating.
A 30-minute intro call, no slides and no obligation. We listen, ask about your processes, and tell you honestly where AI agents would pay off and where they would not.