AI AGENTS & AUTOMATION
AI adoption rarely stalls on the model. It stalls on the process: which workflow to automate, who stays accountable, and how the result is measured. We start from one concrete workflow and finish it.
WHEN IT FITS
Not every process needs AI. These signs suggest it is worth a serious look.
01
Pulling data from several systems, preparing reports, triaging incoming requests — work that costs time but needs no judgement.
02
Signals arrive from monitoring, ITSM and email, but hours pass before anyone joins them into a single picture.
03
The pilot worked in the presentation and never reached production, because boundaries, integrations and ownership were missing.
04
Decisions touch customers or money, so you need auditable actions, explicit limits and human approval at the risky edge.
WHAT YOU GET
Each stage ends with something you can judge and use, even if you decide not to continue.
01
We review your workflows and select the ones worth automating — by volume, repetition, cost of error and data availability.
Deliverable: a prioritised list of processes with an assessment and a recommended starting point.
02
One process automated end to end: integrations, agent logic, safe boundaries, human approval points and instrumentation.
Deliverable: a working pilot in the production environment and the data to decide about scaling.
03
Documentation, team enablement and a plan for applying the same model to other processes without outside help.
Deliverable: a team that owns the solution, and a clear path to expand.
HOW I WORK
A
Who runs it today, how long it takes, where it goes wrong, and what data is genuinely available.
B
What the agent may do alone, where human approval is required, and how every action is recorded.
C
A pilot in the real environment with explicit metrics: time saved, error rate, handling time.
D
Documentation, runbooks and the knowledge your team needs to carry on without me.
FAQ
Ordinary automation executes a predefined sequence of rules. An AI agent also gathers context, assesses the situation and proposes or takes an action against a goal rather than a fixed script. In practice the best result is a combination: rules where rules suffice, an agent where judgement is needed.
With one concrete process that repeats, costs real time and has a clear outcome. Broad “AI strategy” projects usually end as documents. One finished process delivers both value and the experience to expand from.
Safety here is a question of constraints. The agent gets specific permissions, accessible data and allowed actions; risky steps require human approval; every action is logged. Where data is processed, and which model is used, are agreed against your requirements.
A single-workflow pilot typically runs from a few weeks to a couple of months, depending on how many integrations are involved and how quickly data and decision-makers are available.
We record the current state before starting: how long the process takes, how often it repeats, how many errors occur. After the pilot we compare the same figures. If they do not improve, that is also a result — and a far cheaper one than a broad rollout.
Usually not. Agents connect to what already works — ITSM, monitoring, email, databases — through existing interfaces. Replacing a system is considered only when integration is otherwise impossible.
OTHER SERVICES
EXAMPLES
Concrete examples: the situation work starts from, what is done, and what it is judged by.
START HERE
The most useful first conversation is about one specific workflow that costs too much time. That alone shows whether AI agents are the right answer here.