Many conversations about AI in business begin with the question, “Which model should we choose?” It is a natural question, but it usually leads nowhere. The model matters, but it changes nothing in the company by itself. Change begins when AI takes over specific, repetitive work in a specific process while a person retains the decision.
This is why we reverse the order in AI projects: first the task and accountability, then the model. The five steps below lead from an idea to the first AI agent working in day-to-day operations.
Step 1. Choose one task, not an entire department
A good starting point is work in which people read, compare, and prepare material for a decision according to a repeatable pattern. These situations are easy to recognize:
- an account manager reads a dozen attachments before identifying the requirements for an RFQ response;
- a salesperson searches for similar proposals even though the first draft of a new proposal follows a repeatable structure;
- a lawyer reviews a contract alone and prepares amendments in the document;
- an administrator reviews a series of alerts before finding the event that actually requires action.
Choose one such task. Record who performs it today, how many times a week it recurs, and which decision remains with a person. The last question is the most important because it determines the entire project.
Before implementation: the account manager reads every attachment before starting to prepare a response.
After implementation: the AI agent compiles the requirements from the attachments, and the account manager reviews the summary and decides on the response.
Step 2. Describe the process and data before choosing the technology
An AI agent works with what it receives: documents, data from systems, and company knowledge. Before the first line of configuration is created, answer several questions:
- Input: what goes to the agent? An inquiry, specification, draft contract, invoice, or system record?
- Output: what should the agent prepare? An assessment, draft, list of changes, or proposed next step?
- Exceptions: what happens when a document is incomplete or unusual?
- Knowledge sources: which policies, instructions, and templates should it use?
- Access: which data can the agent access, and which data must remain unavailable?
Incomplete or inconsistent data leads to incorrect conclusions regardless of model quality. Preparing the data, definitions, and access is therefore part of every AI project, not a separate topic to address later.
Step 3. Define the approval point: AI recommends, a person approves
In a well-designed process, AI moves through five stages:
- Input: documents, data, and context from the process.
- AI analysis: the agent reads, compares, searches company knowledge, and prepares a result.
- Recommendation: a proposed assessment, response, change, or next step, with supporting reasons.
- Human decision: an employee reviews, corrects, and approves the result. Nothing moves forward without this step.
- Action in the system: the approved result enters the process, for example a workflow in AMODIT, an ERP, or another application.
The fourth stage is not a formality. It ensures that AI takes over work, not accountability. A salesperson approves the price, scope, and commitments in a proposal. A lawyer approves contract clauses. An administrator chooses the response to an alert.
Step 4. Decide where the agent should work and what it should connect to
An AI agent that requires employees to switch to a separate window and copy content into it quickly falls out of use. This is why we design it as part of the process: the agent retrieves the necessary data from company systems and sends the approved result to the place where the work continues.
Astrafox builds comprehensive solutions using AI agents and integrates them with any systems the company already uses: ERPs, CRMs, data warehouses, and line-of-business applications.
When justified by the use case, we deploy agents inside AMODIT, within the processes where the team already works. We then use the functions available in AMODIT: AMODIT Copilot, AskAI, and agentic rules.
The task, the systems the agent must work with, and the security requirements determine the solution. We establish the agent’s access to data separately for each implementation.
It is also important to remember that the company does not need to train its own model. We use existing language models and configure them for the task, company knowledge, and integrations.
Step 5. Start with a pilot and evaluate the result in daily work
The first agent should work on real cases within a limited scope: one team, one document type, and clear evaluation criteria. Before launch, determine how the team will recognize that the solution is useful. For example:
- whether people in the process start with prepared material instead of a blank page;
- how often the agent’s result requires corrections and what types of corrections are needed;
- whether the human decision is always visible and recorded in the process.
After the pilot, it is easier to decide whether to expand the scope, improve the agent’s instructions, or select a different task. Each subsequent agent is created faster because the process, data, and approval rules are already organized.
Where should your company begin?
Instead of asking “which model should we choose?”, ask the team: what do you read, compare, or re-enter today before making a decision? The answer is the best starting point for the first AI agent.
Astrafox has delivered AI solutions for sales, contract work, and IT infrastructure maintenance. We build agents on existing language models and connect them with company systems so that every solution has a clearly defined input, output, and point at which a person makes the decision.
Do you have a task AI could take over? Describe it, and we will recommend where to start. Describe your challenge →