What is an artificial intelligence agent?
Artificial intelligence agent (AI Agent) is a system based on large language models that can receive a goal, break it down into smaller steps, use different tools to perform each step, and finally deliver the result. The main difference between an agent and a chatbot is that the chatbot responds, but the agent takes action.
For example, you say to a chatbot, "How were sales last month?" And you get an answer. But you tell an agent, "Prepare last month's sales report and send it to the managers"; The agent reads, analyzes, reports, and sends data from the sales system.
How do agents work?
An agent consists of three main components:
Brain (linguistic model): Understands the goal, plans and decides on the next step.
Tools: facilities that the agent can use; Like searching a database, sending an email, reading a file or calling your systems API.
Memory: Keeping the information of the previous steps and the context of the work, so that the agent can continue the long tasks.
Capabilities of Netanik smart agents
Automatic decision making
Based on the rules and goals defined for him, the agent makes the right decision in different situations; For example, prioritizing incoming requests or choosing the appropriate action for each case.
Multi-step performance of tasks
The tasks that used to require several people and several software, such as collecting information from different sources, analyzing and preparing the output, are done in an automatic flow.
Gradual learning
By recording the results and feedback, the performance of the review agent and its instructions and tools are improved to become more accurate over time.
Full reporting
All agent actions are recorded, from decisions to tools used. This transparency is necessary for control, error correction and trust building.
Human supervision in sensitive areas
For sensitive actions, such as payment, sending a message to the customer or changing important data, a human verification step (Human in the Loop) is defined so that the agent never acts outside of his authority.
Applications of artificial intelligence agents in business
Sales: Researching potential clients, drafting proposals and following up on leads.
Support: Categorize tickets, collect necessary information and automatically solve simple problems.
Operation: Inventory monitoring, purchase order registration and coordination between systems.
Content and Marketing: Research, draft content and analyze campaign performance.
Financial and administrative: Extracting information from documents and invoices and matching them with the accounting system.
The steps of making an artificial intelligence agent
1. Choose the right process
The best starting point is an iterative, time-consuming process with relatively clear rules. Choosing the right first process is the most important factor for the success of the project.
2. Workflow design and tools
Work steps, required tools, agent access level and human monitoring points are designed.
3. Development and testing in a secure environment
The agent is first tested in the test environment and on real data but without external effects.
4. Gradual deployment and monitoring
The agent is employed first with full supervision and then with a gradual increase in authority.
agents and automation; What is the difference?
Automation of processes Classic is great for work with a fixed and predictable path. Agents become valuable where the task requires understanding text, making judgments, or choosing between multiple paths. In many projects, a combination of both works best.
artificial intelligence agent architecture; Start simple
One of the important lessons in building agent-based systems is that more complexity does not necessarily mean better results. As Anthropic in the article Making effective agents Many problems are better solved with simple, predictable workflows, he explains, and a fully autonomous agent is only needed where the path of work cannot be determined in advance. in design Artificial intelligence agent In Netanik, the simplest architecture that solves the problem is always chosen; Because the system is simpler, more reliable, cheaper and more maintainable.
Multiagent systems
For large and complex tasks, several specialized agents can be designed, each of which is responsible for a part of the work; For example, a research agent, a writer agent and a reviewer agent who control each other's work. A coordinating agent divides the tasks and summarizes the results. This architecture is similar to a work team and is used for processes such as preparing comprehensive analytical reports or processing multi-stage files.
artificial intelligence agent security; The principle of minimum access
An agent who has access to your systems should be screened with the same care as a new employee. The security principles of Netanik agents include the following:
Minimum access: The agent only has access to the tools and data that are necessary for its task.
Read-only access if possible: An agent that only needs to prepare reports is not allowed to change data.
Restriction of sensitive measures: Paying, deleting data or sending messages to the customer requires human approval.
Tamper resistance: An agent that reads external text such as an email or web page is protected against malicious commands hidden in that text.
Complete registration of events: Every action can be tracked and reviewed.
Artificial intelligence agent along with other solutions
Agents are usually built on top of other infrastructures: API integration To connect to models and systems, Smart chatbot As a conversation interface with the user and data analysis To make decisions based on business information. The right design of these components together makes a truly functional AI agent.
Frequently asked questions about artificial intelligence agents
How long does it take to build an artificial intelligence agent?
A trial version usually takes 2 to 4 weeks and full agents integrated with the organization's systems take 3 to 5 months.
Could the agent be doing something wrong?
Any system may fail; Because of this, agents' access is limited to necessary tasks, sensitive actions require human approval, and all activities are logged for review.
Does the agent replace the employees?
The goal of agents is to eliminate repetitive and time-consuming tasks so that employees can focus on tasks that require creativity, human communication, and judgment.
Who owns the project's code and knowledge?
Code, prompts, and documentation are delivered to you and there is no permanent dependency on Netanik.
With which models is the artificial intelligence agent made?
Depending on the need, advanced models such as Claude, GPT and Gemini or open source models are used on the internal server. Model selection is based on accuracy, cost, and privacy requirements.
How do we know which of our processes is suitable for an artificial intelligence agent?
A process that is repetitive, takes a lot of time from the team, requires reading and understanding the text, and the rules can be explained is a good option to start with. In the counseling session, your processes will be reviewed from this point of view.