AI Agents and Business Process Automation

Service Overview

Where the chatbot stops, the AI agent begins

In most companies, AI experiments start in a chat window and stay there: someone pastes an e-mail into ChatGPT, copies the answer and pastes it back into another program. It looks like time saved, but the process is still in human hands, nothing is recorded and the same work is done from scratch the next day. An AI agent is the approach that removes this copy-and-paste loop. The agent is triggered by an event, gathers the information it needs by itself, uses the large language model only for interpretation and decision suggestions, and writes the result directly into your business system.

We build these agents on n8n as workflows that run on your server or in a cloud environment under your control. Every step is visible, every run is logged and every decision can be traced back. Instead of “black box AI”, you get an automation layer that shows what it did, when and why.

Which tasks are a good fit for an agent?

Invoice and document processing: reading PDF invoices that arrive as e-mail attachments or in a shared folder, extracting fields such as supplier, amount, VAT and due date, matching them to the order record and posting them to your accounting software as drafts.
E-mail classification and routing: tagging messages that land in the info@ or support@ inbox by subject, urgency and customer, assigning them to the right team and drafting replies to frequent questions.
Quote preparation: matching a request that comes in by form, e-mail or WhatsApp against your product catalogue and price list, generating a quote draft in your template and sending it to the sales rep for approval.
Data entry and synchronisation: every point where a record in one system is moved to another by hand, for example from a web form to the CRM, or from the CRM to the ERP.
Reports and summaries: collecting daily order, collection or support ticket data and sending it to managers as a readable summary.

Who is it for?

It suits teams that repeat a specific task every day with the same steps, can describe the rules of that task, yet still depend on people because purely rule-based software cannot handle it (documents arrive in different formats, e-mails are free text). Accounting and finance, sales operations, customer service, procurement and logistics coordination are the areas where we most often start. If you are looking for a bot that talks directly with customers, the AI Customer Service Chatbot or Enterprise Chatbot Solutions pages are a better starting point; this page focuses on automations that do the work in the back office.

Scope

What do we build in an AI agent project?

Every project is a combination of these modules chosen to fit your needs; you do not pay for a layer you will not use.

Trigger and input layer

IMAP, Gmail or Outlook inboxes, webhooks, form submissions, files dropped into FTP or cloud folders, scheduled jobs. We tie when the agent runs to a real business event.

Document reading and field extraction

Structured data extraction from PDFs, scanned images and e-mail bodies. Output is forced into a JSON schema, and missing or suspicious fields are flagged automatically.

Classification and routing

The model determines the type, priority and owner of each incoming request; a rule engine validates the result, which is then passed to the right person, queue or channel.

Tool-using agent steps

When needed, the agent checks stock, looks up an account balance or reads the price list. Which tool it may access, and with what permission, is defined one by one.

Human approval (human-in-the-loop)

Critical outputs go to approval via Slack, Teams, e-mail or a dashboard. Approved, rejected and corrected records are kept to drive later improvements.

System integration

Connections to ERP, CRM, accounting, e-commerce and the WhatsApp Business API. Where there is no API, we work through a database view, file transfer or a middleware service.

Logging, error handling and monitoring

The input and output, model response and duration of every run are recorded. Failed steps are retried, and persistent errors are reported to the person responsible.

Cost and quota control

Per-task limits on model calls, caching and pre-filters that prevent unnecessary calls. You get reports on how much model usage each flow consumes.

Process

Six steps from pilot to scale

Most AI projects never leave the prototype stage; this process was designed to move the agent onto real workloads safely.

01

Process map and candidate selection

Together with your team we list the repetitive tasks and assess each one’s volume, cost of error and clarity of rules. For the first pilot we pick the process that will deliver measurable benefit in the shortest time.

02

Sample data and acceptance criteria

We collect real (anonymised where needed) document and e-mail samples and put the definition of a “correct result” in writing. This is where it becomes clear which fields are mandatory and which ones go to approval.

03

Flow design and prototype

We build the first end-to-end flow on n8n and test it against the sample data set. Prompts, schemas and validation rules mature at this stage.

04

Shadow mode (shadow run)

The agent runs on live data but does not write to your systems; its results are compared with the work your team does by hand. Deviations are fixed and thresholds are tuned.

05

Gradual go-live

The agent goes live first in approval mode, then in automatic mode for records that pass the confidence threshold. A rollback plan and a manual operating procedure are kept ready.

06

Monitoring and expansion

Once the first process has settled, a second and third process are added on the same infrastructure. Because shared components (document reading, the approval module) are reused, each new flow is built faster.

Comparison

Which automation approach suits which job?

It is common to combine these approaches in one project: rule-based steps are cheap and deterministic, while agent steps handle the flexible part.
Approach	When is it the right choice?	Limitation
Rule-based automation (RPA / classic workflow)	When data always arrives in the same format and decisions can be written entirely as “if-then” logic	Breaks when the format changes or free text comes in
Chatbot / conversational assistant	When a user asks a question and expects an answer	Does not start work by itself and cannot run a multi-step process on its own
AI agent (n8n + LLM)	When input is irregular but output is well defined; when documents, e-mails or request texts are being processed	Needs an approval step for critical decisions and well-defined acceptance criteria
Custom software module	When the process is the company’s core product, or high volume and a dedicated interface are required	Development time and cost are higher

Deliverables and Pricing

What do you keep, and how is it priced?

What we deliver at the end of the project

Version-controlled n8n workflows running in your environment, with a configuration that stores credentials securely
For each flow, a document covering the input-output schema, the prompts used and the validation rules
Setup of the approval queue or notification channel, plus user training
A shadow-mode comparison report showing where the agent is accurate and where it drops into approval
A manual operating procedure for failures and a list of responsible people

Pricing

There is no fixed package price published on the site for AI agent projects, because the cost is driven by the number of systems to connect, the variety of documents and the complexity of the approval rules. After the discovery call, a fixed-price proposal tailored to your project is sent within 24 hours. The proposal lists the development fee separately from ongoing costs such as model API usage and servers, so you see your monthly running cost from the start.

If you would also like to connect your existing WhatsApp channel to these automations, our n8n WhatsApp Chatbot service shares the same infrastructure. For the ERP or accounting side of the agent, our ERP Integration and API Integration pages explain the connection methods in detail.

Sık Sorulan Sorular

A chatbot talks to a user and answers questions. An AI agent starts working on a trigger (an incoming e-mail, an uploaded PDF, a new order) and carries out several steps on its own: it reads the document, extracts the fields, suggests a decision based on your rules, opens a record in the ERP or CRM and, when needed, sends an approval request to the right person. For bots that talk to customers, see our AI Customer Service Chatbot page.

We never give the agent full authority. We define a confidence threshold for every step; if the model is unsure about the data it extracted, or a critical field such as an amount, an account or a tax number breaks a rule, the workflow stops and the record drops into an approval queue. Human approval can be made mandatory for every output that goes to accounting or to a customer.

Because n8n can be installed on your own server, your documents and e-mails do not have to pass through a third-party automation cloud. That is a real advantage for data protection, whether under KVKK (Turkey’s data protection law), the GDPR in Europe or the data rules in the Gulf. Code nodes also let us write custom logic, and workflows can be placed under version control. If you already run Zapier or Make flows, we can design the new setup to work alongside them.

We choose the model by task type: document reading, classification and text generation can each work better with a different model. Under enterprise API agreements, the data you send is not used for model training by default; even so, we review the provider’s data processing terms with you during the project and, where necessary, mask personal data before it is sent to the model.

Yes. Because the workflows live in a visual editor, adding a new approval step, changing a category or switching the notification channel from Slack to e-mail are things your own team can do. At handover we provide documentation explaining what each flow does, plus a short training session.

A pilot for a single process (for example, reading incoming invoices and posting them to your accounting software) is usually completed within a few weeks. The exact timeline depends on the API readiness of the systems to be connected, the variety of documents and how clear the approval rules are, and it is stated in writing in the proposal after the discovery call.

Related Pages

AI Agents and Business Process Automation