05 / Autonomous AI Agents

Autonomous AI Agents Services

Agents that plan, use tools, and complete multi-step tasks without supervision.

Wireframe of an automated conversation: an inbound message, the retrieved context behind the reply, and a handover to a person. Layouts are illustrative — every build is shaped around your own data.

AI agents go beyond single LLM calls — they plan, call tools, evaluate results, and iterate. I design and build production-grade agents using LangChain, LlamaIndex, or custom orchestration layers, with the reliability engineering needed to make them safe to deploy.

Use Cases

Research and summarisation agents

Given a topic or company name, the agent searches the web, reads pages, extracts key facts, and returns a structured briefing document.

Customer service agents

Handles tier-1 support: classifies intent, retrieves from knowledge base, drafts response, escalates to human when confidence is low.

Data analysis agents

Given a CSV or database access, the agent writes and runs SQL/Python, interprets results, and answers natural-language questions about your data.

Workflow orchestration agents

Agents that manage multi-step business processes: reading inboxes, creating tasks, calling APIs, and following up — on a schedule or triggered by events.

Common Questions

How do you prevent agents from doing something wrong?

Confidence thresholds before irreversible actions, explicit human approval gates, and rollback paths for everything destructive. I don't deploy agents without undo logic.

What frameworks do you use?

LangChain and LlamaIndex for complex multi-step agents. For simpler tool-use scenarios, I often use the OpenAI function calling API directly — less overhead.

Can agents access my internal systems?

Yes — I build custom tools that wrap your internal APIs, databases, or file systems, with authentication and access scoping so the agent only touches what it should.

What you get

→Agent architecture design (ReAct, plan-and-execute, multi-agent)
→Tool and function definition with schema validation
→Memory and context management (short-term + long-term)
→Human-in-the-loop escalation paths
→Observability: trace logging, token usage, action audit trail
→Rollback and undo logic for destructive actions

Start a project

Related services

AI Security LLM Integration n8n Workflow Automation

What AI agent development services deliver

An agent is worth building when the task needs judgment across several steps. When it does not, a workflow is cheaper, faster and far easier to debug.

Tool-using agents

Agents that query your systems, take actions and report back, with every tool call logged and every action reversible.

Bounded autonomy

Explicit limits on what an agent may do without a human, because the failure mode of an unbounded agent is expensive and arrives without warning.

Escalation that works

Recognising when a conversation has left what the agent handles well, and handing to a person with the full context attached.

Evaluation before deployment

A test set drawn from your real cases, scored before anything reaches a customer. Agents that were never evaluated fail in production instead.

Self-hosted models where required

Locally run models where no data may reach a third-party API — a constraint that shapes the architecture rather than being bolted on.

When an agent is the wrong answer

Most tasks presented as agent problems are workflow problems. If the steps are known in advance and the decision points are rules rather than judgment, a deterministic workflow will be faster, cheaper, and debuggable — and it will not occasionally do something surprising.

Agents earn their cost when the path genuinely varies by input: triage across many categories, research that follows where it leads, support that has to decide which system to consult. The signal is whether you could write the flowchart. If you could, build the flowchart.

The other honest limit is accuracy tolerance. An agent that is right 90% of the time is excellent for drafting and unacceptable for anything recorded without review. The correct design puts deterministic validation between the model and anything that gets written down, and that boundary is a design decision made before any code.

How a project runs

The same sequence every time. It is deliberately front-loaded: most of the risk in an automation project sits in understanding the process, not in building it.

Map the process before writing anything

The first session is spent on how the work actually happens, which is almost never how the documented process says it happens. Who touches what, in which order, and where the time really goes. Most failed automation projects failed here rather than in the build, because they automated the described process instead of the real one.

Measure the cost of doing nothing

Hours per week, error rate, and what those hours would otherwise be worth. This is what decides whether a process is worth automating at all — and it is also the number you compare against afterwards, which is why it gets recorded before anything is built rather than estimated after.

Build the smallest useful version

One process, working end to end, in production, before anything else starts. A narrow system that people actually use beats a broad one that waits on a second phase, and the edge cases that matter only surface once real work runs through it.

Run it against reality

The first two weeks of live use produce more design corrections than any amount of planning. Failures get surfaced loudly, retried and logged, because silent failure is the most expensive property a workflow can have and the one noticed last.

Hand it over properly

Documentation, credentials, and a walkthrough with whoever will maintain it. A system only one person understands is a liability regardless of how well it runs, so handover is part of the work rather than an optional extra at the end.

Ways to work together

Three arrangements cover almost every engagement. Most start with the first or the second; the third only makes sense once something is live.

Fixed-scope project

One defined process, a fixed price and an agreed definition of done. The right fit when the problem is clear and bounded — an order flow to connect, a CRM to build, a reporting pack to automate. Most first engagements are this, because it lets both sides find out how the other works without a long commitment.

Assessment first

One to two weeks mapping processes and measuring where the hours actually go, ending in a ranked list with effort and payback estimates. Useful when there is a backlog of automation ideas and no agreement on which matters. The document stands on its own and is yours whether or not you build anything with me.

Ongoing retainer

A recurring block of time for maintenance, extension and new automations once systems are live. Integrations break when the systems either side of them change, and a retainer means that gets fixed before it becomes an outage rather than after.

How the working relationship is set up

Remote, with real overlap

Work is delivered remotely. Across Europe and the Nordics the working day is effectively identical; in the Gulf it starts three hours ahead, which still leaves your full morning covered. There is no local office in any market, and none is claimed anywhere on this site.

You own what gets built

Source code, infrastructure and data stay yours. Systems are deployed on infrastructure you control — your server, a European provider, or a VPS in your own account. There is no per-seat licence and no dependency on me continuing to be involved.

Self-hosting is a first-class option

Self-hosted n8n, self-hosted databases and locally run models are all supported and, in several of these markets, preferred. Where no data may reach a third-party API, that constraint shapes the architecture from the start rather than being retrofitted.

Direct contact, one person

You deal with the person building the system. There is no account manager relaying requirements, which is the main practical advantage an independent consultant has over an agency at this size — and the main reason scope stays honest.

Questions worth asking before you commit

What if we are not sure automation is the right answer?

Then the assessment is the right starting point, and it is designed to be able to conclude that you should not automate something. A process that is broken should be fixed before it is automated, and one that runs twice a month rarely earns the build. Receiving that answer in week one is far cheaper than discovering it after a project.

We have been burned by a failed automation project before.

That is common, and the cause is usually scoping or adoption rather than technology — a system built for the documented process rather than the real one, or one nobody was trained to maintain. Both are addressed by mapping the real process first and treating handover as part of the work.

How do we avoid depending on one person?

By owning everything: source code, infrastructure, credentials and documentation, with a walkthrough for whoever maintains it. The test is whether another developer could pick the system up from the repository and the documentation alone, and that is the standard handover is written to.

Is our data safe?

It stays where you need it to. Systems can run entirely inside your own infrastructure, including self-hosted models where no data may reach a third-party API. Where GDPR applies, data stays in the EU by default, with named access control and audit logging as standard rather than as an upgrade.

How quickly can something be running?

A first working version of a single process is typically weeks rather than months. Larger platforms are sequenced as modules so something is in production early and the rest builds on a foundation that already survives real use.

Sectors this is built for

AI Automation for Hospitality and Tourism AI Automation for Real Estate

Markets where this is delivered

United Arab Emirates the Netherlands Ireland Denmark

Get in touch

Tell me what needs automating

Describe the process that is costing you time and roughly how much. I reply to every enquiry personally, usually within one working day.

Email: inquiries@artenisalija.com
Response: Usually within one working day, Mon–Fri CET
Delivery: Remote across Europe, the Nordics and the Gulf