AI agent development - with guardrails instead of hype
AI agents handle multi-step tasks on their own: they research, check documents, call systems, and prepare decisions. But between a demo and reliable production sits engineering - guardrails, evaluation, monitoring. That's what Olio delivers: AI agents that work in daily operations, GDPR-compliant on EU infrastructure. Boutique agency from Mönchengladbach, Germany, working remotely across the EU.
What AI agents can actually do today
An AI agent is more than a chatbot: it plans steps, uses tools - search, databases, internal APIs - and works through tasks that used to be manual case handling. Realistic fields in 2026: pre-qualifying and enriching inquiries, checking tenders and contracts against your criteria, summarizing research across sources, triggering follow-up actions in third-party systems. The boundary matters just as much: fully autonomous agents without oversight are rarely defensible in business processes - we build agents with a clear mandate, defined tools, and human approval in the right places.
Typical use cases
Sales: enriching and prioritizing inbound leads and handing them to the team with call preparation. Procurement and legal: checking contracts, terms, and tenders against your criteria and flagging deviations. Support: classifying tickets, proposing answers from the knowledge base, closing standard cases with approval. Back office: reconciling data between systems, finding inconsistencies, preparing reports. The common denominator: recurring knowledge work with clear criteria and measurable hours.
Architecture: guardrails, tools, evaluation
Reliable agents come from architecture, not prompts. We define the agent's tools as vetted, permission-scoped interfaces, validate outputs against your data, set confidence thresholds and escalation paths, and measure quality with an evaluation set built from real cases - before go-live and continuously after. Add per-case cost control, audit logs for every action, and operations on EU infrastructure, with EU model endpoints or self-hosted models.
Agent, chatbot, or automation?
Not every task needs an agent. A chatbot answers questions from your knowledge, a workflow automation processes events by fixed rules, an agent takes on open-ended, multi-step tasks with room for judgment. The right solution is often a combination - and sometimes an agent is the most expensive answer to a problem a simple workflow solves. In the audit we classify your cases honestly before money flows into the wrong architecture.
Why Olio
We are software engineers with LLM production experience, not a prompt agency. Olio agents are versioned, tested, and monitored like any other software - with a documented handover so your team can understand and evolve the solution. As a boutique agency you work directly with senior engineers, and we'll tell you when a use case isn't ready for agents yet. That saves you the most expensive kind of AI project: the abandoned one.
What you get with us
- Production-grade agents instead of demos: guardrails, evaluation, monitoring, audit logs
- A pilot in production within 3 to 5 weeks - with measurable time savings from day one
- Human approvals in the right places - autonomy only where it's defensible
- GDPR-compliant operations: EU infrastructure, EU endpoints or self-hosted models
- Connected to your systems: CRM, ERP, email, ticketing, internal databases
- Honest use-case assessment: we'll say where an agent pays off - and where a workflow is enough
Core Technologies
Let's plan your first agent
Frequently asked questions
What does AI agent development cost?
A pilot agent for one scoped process runs €10,000 to €25,000, including guardrails, an evaluation set, and operations setup. Production solutions with multiple tools, system integrations, and approval workflows land between €25,000 and €75,000. Add running model and operations costs, which we calculate transparently up front.
What's the difference between an AI agent and a chatbot?
A chatbot answers questions in a dialog. An agent pursues a goal across multiple steps: it plans, uses tools like search, databases, or APIs, evaluates intermediate results, and delivers a work product - a checked case, a research summary, or a prepared dataset.
How do you keep the agent from making mistakes or hallucinating?
Through architecture: narrowly defined tools with minimal permissions, output validation against your data, confidence thresholds with escalation to humans, and an evaluation set of real cases that every change must pass before deployment. The agent doesn't decide uncertain cases itself - it queues them for approval.
How fast is an agent in production?
A pilot for a clearly scoped process is live within 3 to 5 weeks - including system integration, guardrails, and training. After that we expand step by step with more tools and cases, prioritized by measured time savings.
Can this be done GDPR-compliantly?
Yes. The agent runs on servers in Germany or the EU, models are used via EU endpoints or self-hosted. Every action is logged, personal data can be pseudonymized before model calls, and data processing agreements are part of the project scope.
Which models do you use?
Vendor-neutral: Anthropic and OpenAI models via EU endpoints, and self-hosted open-source models where sensitive data requires it. The choice follows from the task, data protection requirements, and cost per case - not from a partnership.
Isn't an off-the-shelf copilot or a custom GPT enough?
For personal productivity: often yes. For business processes, ready-made assistants lack system integrations, permissions, audit logs, and quality measurement. Rule of thumb: what one employee uses for themselves can be a product off the shelf - what carries a company process needs engineering.
Can the agent work with our existing systems?
Yes - that's the core of the project: we connect CRM, ERP, ticketing, email, and internal databases via APIs, wrapped in permission-scoped tool interfaces so the agent can only do what it's allowed to do.
Who operates and maintains the agent after launch?
Either works: we run operations, monitoring, and evolution with agreed response times - or your team takes over based on the documentation. Agents need ongoing care because models, data, and processes change; we budget for that from the start.
How do we know whether an agent is worth it for us?
Three questions: does the task cost several hours a week today? Does it follow explainable criteria? Is the required data digitally accessible? Three yeses make a strong candidate. In a compact audit we work out the business case before you invest.
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