AI Agents
AI agents that work inside your workflows.
ALGOFACT builds AI agents that connect to the ERP, CRM, hospital, email and document systems a company already runs, carry a task through step by step and wait for human approval on critical decisions. We define the agent’s task and tools, test it against an evaluation set built from real cases, and put it into production with monitoring and logging. Delivery runs from our engineering team in Adana and Ankara TEKMER under ISO 27001:2022.
At a glance
Last updated:
- What it does
- Runs multi-step tasks across systems
- Control
- Human approval on critical steps
- Information security
- ISO 27001:2022
- Where it runs
- Cloud, on premises or hybrid
- Delivery
- Engineering team in Adana and Ankara TEKMER
- First step
- Discovery call and scope presentation
When is an AI agent the right tool?
An AI agent is software that connects a large language model to a set of tools: it reads a record, queries a system, drafts a document, asks a person for approval when needed and moves the work to the next step. It earns its place in tasks where a rule-based script keeps breaking, yet the work should not take up a person’s whole day.
Not every task needs an agent. A workflow with clear rules is cheaper and more predictable as classic automation, and answering a single question with sources only needs a RAG assistant. We draw that line in the discovery call and recommend an agent only where the work really requires judgment and sequencing.
Where agents are used
Agents pay back fastest on repetitive work that means reading several systems and documents together.
- 01
Request and document handling
Reads incoming email, forms, orders or invoices, classifies them, flags missing information and opens the right record in the ERP or CRM. - 02
Customer and support operations
Drafts sourced answers from the knowledge base, updates the support ticket and hands the conversation to a person when it is not confident. - 03
Reporting and data questions
Turns a plain-language question into a database query, reports the result with an explanation and keeps the query itself for audit. - 04
Operational workflows
Runs steps such as appointments, stock, shipments or applications across systems in order, and waits for approval on steps that touch money, contracts or patient data.
How the project runs
- 01
Task analysis
We map who does the task today, in which systems and with which exceptions. How success will be measured is written down here. - 02
Tools and permissions
Read and write access is defined separately for every API and data source the agent may call. The agent cannot reach any system it does not need. - 03
Evaluation set
We collect real examples from past cases and measure every version of the agent against them. A model or prompt change ships only when it does not regress on the set. - 04
Pilot
The agent first runs with a limited group of users, with every step approved. Errors and approval rates are reviewed together at the end of the pilot. - 05
Production and monitoring
We go live with logging, cost tracking, error alerts and a runbook. If you want, an embedded team keeps improving the agent after the first release.
Security and control
Every step the agent takes, every tool it calls and the data it uses are logged, so the reason behind a decision can be traced later. Text from outside sources (email, web pages, uploaded files) is handled as data, never as instructions, which limits prompt-injection risk.
For processes that involve personal data, KVKK and GDPR requirements are handled at design time. If data must not leave your environment, we run open models on your own servers or private cloud; hosted models are used only where the data processing terms allow it.
What you receive
- 01
Agent architecture and tool definitions
The task flow, the tools the agent uses, its permissions and its approval points. - 02
Evaluation set and report
A test set built from real cases and each release’s results on it. - 03
Integrations
API and message-queue connections to ERP, CRM, hospital information, email and document systems. - 04
Monitoring and logging
Step-level logs, cost and error tracking, and alert rules. - 05
Handover and runbooks
Documentation that lets your team run and extend the agent.
Questions
Approach01What is the difference between an AI agent and a chatbot?
A chatbot answers a question. An agent completes a task: it reads data from your systems, creates or updates records, orders the steps and asks for approval when needed. Many projects use both together.
02Which language models do you use?
We choose by task, data-privacy requirement and cost. The choice between commercial APIs and open models running on your own servers is made on evaluation-set results, and the agent is built so the model can be swapped later.
03Where is our data processed?
In the cloud, on premises or in a hybrid setup, depending on your requirements. Sensitive data does not have to leave your environment; in that case the models run on your infrastructure.
04What happens if the agent makes a wrong move?
Steps that are hard to undo require human approval by default. Because every step is logged, an error is traced back to the tool call where it happened and added to the evaluation set as a new example.
05What drives the timeline and cost?
The number of systems to connect, how often the task hits exceptions, the size of the evaluation set and where the model runs. After the discovery call we present the scope, the pilot plan and the expected running cost in writing.
Tell us about your project
Describe the problem briefly and we will reply with a time for a 30-minute discovery call.
Book a discovery call