Bringing in outside help for AI automation is a new decision for most businesses, and it comes with sensible questions. Where does it start? How much time will it take from my team? What will I have at the end, and who looks after it afterwards?
This post answers those questions by walking through the four stages we follow on every project, what you receive at each one, and what we need from you along the way.
It starts with a free strategy call
The first conversation is about your business, not about technology. We ask how your team spends its week, which tasks feel repetitive, and what you would do with the time if those tasks ran on their own. By the end you will know whether an AI agent is a good fit and which areas are worth a closer look.
Stage 1: Consultation
We map the workflows you want to improve by sitting with the people who actually do the work, because they know the shortcuts and exceptions that no process document mentions. We measure how often each task happens and how long it takes, so every recommendation is based on your real numbers.
You receive a workflow map, a ranked list of tasks worth automating, and a baseline of what the current process costs in hours. That is the core of our automation audit, and it is yours to keep whatever you decide next.
Stage 2: Planning
With the audit in hand, we scope the agent: exactly what it will do, which systems it will connect to, where it hands over to a person, and what it will deliberately leave alone. We choose the AI model per task rather than per vendor, whether that is OpenAI, Anthropic, Google or an open-source model, so you are never locked in to one provider.
You see the expected time saved and a clear scope before any build begins, so there are no surprises later.
Stage 3: Execution
We build the agent, connect it to the tools you already use, and test it against real cases from your business, including the awkward ones. Your existing systems stay in place; the agent works with them rather than replacing them.
Testing against real examples is what makes an agent dependable. Anthropic’s guide to building effective agents makes the same point: agents should check real results at each step, and stopping conditions keep them under control. We build those checks in from the start.
Stage 4: Go live and tune
We launch the agent, monitor every run, and review how it performs against the baseline from the audit. Each month we improve it, so it keeps getting more accurate and often cheaper to run as better models become available.
You get regular reporting on what the agent handled and what it passed to your team. This ongoing care is our managed AI agents service, and it is what keeps an agent useful as your business grows and changes.
What we need from you
- Time with the people who run the workflow, mainly during the consultation stage.
- Access to the systems the agent will connect to, set up with the permissions it needs and nothing more.
- A named person who can make decisions about how the process should work.
- Feedback during testing, so the agent matches how your team actually wants the work done.
Most of your team’s time is needed at the start. Once the agent is live, the work shifts to us.
Questions to ask any AI agent agency
- Will you study our actual workflow before you quote?
- How will you test the agent before it goes live?
- What happens when the agent is unsure or gets something wrong?
- Which AI models do you use, and can they be changed later?
- Who looks after the agent once it is running?
A good partner will have clear answers to all five. These are the questions our process is designed around.
Book a free strategy call and we will walk through your workflows together.