A system of agents working for your team
Analysis, decision, writing and review, each step with its own specialist
The problem
A well-made campaign needs several heads. Someone who looks at the data and understands what is happening with the base. Someone who decides who to talk to, when and with what offer. Someone who writes it. And someone who reviews it before it goes out.
In a large team those are four people. In most companies it is one, who does all four things spread across other tasks, and the result is that 1 campaign a month goes out when the base would support 4. Not for lack of ideas: for lack of hands.
The consequence is that data ages before it is used. The customer who is about to buy today enters the campaign in three weeks, when they no longer are.
The solution
A system where each of those tasks is done by a different agent, specialised in its own role, that passes the work to the next. A central orchestrator coordinates them.
The one that analyses
Reads what exists about each customer: recent behaviour, history, segment, and model outputs if they exist. Returns groups with a reason behind them, not a list.
The one that decides
Takes those groups and defines what corresponds to each one: what offer, through which channel, at what moment. Applies your business rules, not its own. Margins, inventory, maximum contact frequency, what cannot be offered to whom.
The one that writes
Writes the piece for each group respecting your editorial tone and building on what has worked before. Adapts the subject, the message and the call to action to what the analysis and the decision asked for that group, without rigid templates or generic outputs.
The one that reviews
Checks the result against the brand criteria, the coherence between what was decided and what was written, and any limits that have been set. When something does not comply, it sends it back to the appropriate agent rather than letting it through.
In the end your team receives the assembled campaigns, each with the reasoning for why that group, that offer and that moment were chosen. Approval takes minutes because the decision does not have to be reconstructed: it comes explained.
What changes compared to a single system doing everything
All of this could be asked at once in a single step. It works in a demo and fails in production, and it fails in a specific way: when the result is not useful, there is no way to know which part failed.
With separate roles, there is. If campaigns target the wrong group, the problem is in the analysis. If the group is right but the offer does not fit, it is in the decision. If everything is right and the text does not sound like your brand, it is in the writing. Each piece is adjusted alone, without touching the others and without retesting everything.
That is what allows a system like this to be maintained for years. It is not a black box that has to be rebuilt entirely when the business changes: it is a team where one member can be changed while the rest keep working.
More on the blog: Automating with AI does not remove the human, it moves them →
How it integrates
The system reads from where the data already lives and leaves the campaigns ready in the tool where your team works, in draft state. Nothing goes out without human approval, unless you decide otherwise for tiers where the risk is low.
And there is a record of everything: what each agent decided, with what information, and what the reviewer returned. That is what allows looking, three months later, at whether the system's decisions were good. The review criterion is not brought pre-loaded: it is defined with your team during development, reviewing real outputs until what the reviewer accepts matches what the person who currently gives the go-ahead would accept.
The same approach, other applications
Where work is currently distributed across several people with different specialisms, a team of agents can be assembled with the same logic.
Deciding what to do with each customer.
When several models are running, one agent per signal family, an integrator that combines them with your rules, and a reviewer that validates the result.
Hiring processes.
An agent that normalises applications, one that checks against requirements, one that fetches context, and a reviewer that flags weak evaluations before they reach a person.
Review against criteria.
Contracts, proposals or deliverables checked against the conditions your company requires, with what does not comply flagged and explained.