An AI agent does more than answer questions. It carries out tasks on behalf of your staff: retrieving data, proposing decisions, calling tools, choosing next steps. We build domain-specific agents that work on your data, your systems and your rules, and are model-agnostic.
A chatbot answers a question and stops. An AI agent is given a goal, such as "research this lead", "classify this ticket" or "check this invoice against our purchasing terms", and works out for itself which steps are needed. It consults your documents, calls your APIs, compares options, validates intermediate results and returns a substantiated outcome, often with a trail of sources and decisions.
That difference makes building one harder. The agent needs to know which tools are available, when it may act, when it must escalate, and how to account for its steps. A chatbot talks; an agent does something that matters. That means strict requirements for reliability, observability and cost control. We build AI agents for businesses that do all of this in a production-ready environment, with monitoring per agent run, cost budgets per task and human-in-the-loop for actions with impact.
Under the bonnet, agents rely on concepts such as Anthropic's tool use, OpenAI Assistants, Microsoft Copilot Studio and open-source frameworks like LangChain and LlamaIndex. The best combination depends on your use case, data residency requirements and the models you have access to. We work model-agnostically: Anthropic Claude, OpenAI GPT, Azure OpenAI, Google Gemini, or open-source models such as Llama and Mistral within your own VPC. No vendor lock-in, no dogma about a single framework.
Not every task suits an agent. For tightly defined, deterministic processes, a standard integration or a rule-based workflow is often cheaper and more reliable. An agent adds value where the work requires judgement, where data must be brought together from several sources, or where the input is so irregular that a traditional if-then flow cannot keep up. In every initial conversation, we help you weigh this up honestly.
Depending on how much autonomy the agent is given and how deeply it integrates with your existing systems.
The agent reads along, searches, summarises and suggests. No write actions on your production systems. Ideal as a starting point: low risk, quick value, easy to monitor. Think of a sales research agent that combines LinkedIn, your CRM and public sources into a briefing for a meeting, or a research agent that gathers market signals weekly and summarises them for your management team.
The agent proposes actions and carries them out once a human approves. For example, a support agent that classifies a ticket, drafts a reply with source references and waits for one-click approval before it goes to the customer. Or a purchasing agent that drafts a request for quote and only sends it after review. The review step is built in deliberately: trust is something you build up gradually, not something you switch on.
The agent acts autonomously within tight boundaries. For example, an operations agent that classifies monitoring alerts, runs standard runbooks and only escalates when there is a deviation or uncertainty. This requires strong guardrails, observability for each step, a fallback path for when the agent is unsure, and clear limits on which tools the agent may call in which context. Only deploy it for tasks where the real risk of an incorrect action is low or easily reversed.
A production-ready agent, running in your environment or hosted by us, with everything around it to manage, adjust and extend it.
Production and staging, in your cloud (EU region) or with us. Your own prompts, your own tools.
Full source code, a versioned prompt library, and the tool definitions the agent uses.
Insight into which prompts, which tools, which costs and which errors occur per run.
A test set to catch regressions when you change prompts, models or tools.
Model updates, prompt tuning, cost monitoring, adding new tools.
Eight use cases we guide clients through. The common thread: repetitive work that requires judgement, on data that lives across multiple systems.
Combines CRM, LinkedIn, public sources and your own sales notes into a meeting briefing. Can draft a first email and update CRM fields.
Classifies incoming tickets, finds similar cases and proposes a reply with sources cited. Escalates automatically when signs of urgency or complaint appear.
Detects anomalies in monitoring data, links them to runbooks and suggests a follow-up action. Generates weekly reports based on your own KPIs.
Reviews documents against internal rules or external standards. Performs sanctions list checks and due diligence steps, and flags deviations for human review.
Answers questions about employment terms from your own documents. Screens CVs against role criteria without using bias-sensitive fields.
Searches supplier catalogues, compares prices and prepares quote requests. Takes your procurement policy and approved suppliers into account.
Codes incoming invoices, checks expense claims against expense policy, and flags possible fraud patterns. Posts to your accounting system once approved.
Triage of monitoring alerts, automatic execution of standard runbooks, and escalation routing to the right team with context handed over.
We map out the task: the goal, the data, the tools and the risk boundaries.
We build a working prototype on a single model, along with an evaluation set to measure quality objectively.
Guardrails, observability, cost controls, RAG pipeline, and integrations with your systems.
A pilot with a small group, followed by a wider rollout. We keep tuning the models and prompts.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →An agent that carries out tasks on behalf of your organisation touches on privacy, security and compliance. We identify those frameworks at the outset and build them into the design, rather than tick them off afterwards.
The agent only has access to the data needed for the task. We carry out a DPIA for every project that processes personal data, and we set retention limits in code, not just in policy.
At the start of the project, we determine which category your agent falls into (minimal, limited or high risk) and align documentation, transparency towards users and monitoring accordingly. For high-risk use cases (HR, government, critical infrastructure), we build in additional audit trails and human escalation points.
For regulated data, everything runs within EU regions: Azure OpenAI in West Europe or Sweden Central, Anthropic via AWS Bedrock EU, or a self-hosted Llama deployment on your own premises. No model data, logs or embeddings need to leave the EU for the US.
The agent cannot do everything. A rules engine checks output for forbidden content, tool usage and cost budget. High-impact actions require a second model validation or human approval. We log all failures in full, including the prompt, response and tool trace, for later review.
For each project, we choose what best suits quality, latency, cost and data residency. We are model-agnostic, with no allegiance to a single vendor or framework. For specific smart API integrations between the agent and your systems, we work to the same principles. Where an agent is too heavy for the task, we fall back to a simpler solution, and where your use case goes well beyond a single agent, we guide you through a broader enterprise AI implementation.
A free, no-obligation half-hour introductory conversation. We listen to the task you want to automate, ask sharp questions about data, risk and volume, and give you an honest view on whether an agent is the right answer, perhaps a chatbot, or no AI at all.
We'd love to hear from you.
Long story, or would you otherwise have emailed it? Choose Detailed briefing: headings and bullet lists, images in the text and attached files.
We'll read through it and usually reply within one working day. Have anything to add? Send it to fabian.vandijk@appfront.nl.
Thank you for your interest! We'll get back to you as soon as possible, usually within 1 working day.
Appfront uses cookies and similar technologies to keep the website working properly, for analytics and for marketing. You choose what you allow. Read more in our privacy policy.
