An AI agent is a software system that takes a goal, plans the steps, uses tools inside your systems, checks the result and changes course when needed. A chatbot answers a question; an agent finishes the job. Intelligence AI designs custom AI agents for businesses in Türkiye and abroad, connects them to your existing systems and runs them in production. A working agent prototype within 2-4 weeks of the first meeting, a production system within 2-3 months.
Classic software executes rules written in advance. A chat assistant built on a large language model (LLM) generates text in response to a question. An AI agent does something different: it receives a goal, plans the steps towards it on its own and carries those steps out on real systems.
An agent has four core components:
These components run in a loop: observe → think → act → verify → repeat. The agent keeps cycling until it reaches the goal or hits a defined boundary. “Agentic AI” is the general name for the system approach that builds this loop.
| Classic automation (RPA) | Chatbot / assistant | AI agent | |
|---|---|---|---|
| Input | Fixed trigger | User question | Goal or event |
| Decision | Pre-written rule | Text generation | Planning + tool selection |
| Unexpected case | Stops, raises error | May make something up | Re-plans or asks a human |
| Output | Same action every time | Text | Completed work (record, quote, decision) |
| Best for | Stable, structured process | Information access, support | Variable, multi-step, multi-system process |
In practice the three are not rivals but complements. An agent can trigger an RPA bot in one of its steps or talk to the user through a chat interface. The right question is not “which one should we pick” but “which part of the process needs which approach”. Making that distinction together is the first step of our enterprise AI consulting.
Agents deliver the highest return on work that needs human attention but not human judgement: multi-step, multi-system, repetitive processes with a measurable cost of error. The scenarios we meet most often:
Agents that read an incoming request, pick the right items from the catalogue and price list, apply the discount policy based on past quotes and produce an approval-ready proposal in minutes. Our Proposal Robot is exactly this agent, packaged; we also build company-specific versions.
Agents that monitor competitor prices, update prices within stock and margin limits, notice a lost buy box and intervene. SellBox is our agent-based system doing this for marketplace sellers.
Agents that do not just answer but start a return, check the order status in the system, reschedule an appointment and hand over unresolved cases to the right team with a summary. The difference from a classic chatbot: at the end of the conversation the work is done.
Agents that read an invoice and match it to the accounting entry, write to the supplier on a mismatch, review a suspicious transaction and prepare the case file. Our Financial Intelligence and Document Intelligence solutions form the foundation for these agents.
Agents that scan news sources, social media, tender and regulation sites, classify and summarise what matters to the company and escalate the critical items instantly. NewsTracker and Social Reputation Management are our solutions in this area.
Enterprise knowledge agents that answer “what were this customer’s contract terms over the last three years” by searching the CRM, file server and email archive with sources cited, then prepare and share the requested summary.
The most common cause of failure in agent projects is not technology but unclear scope. “An agent that does everything” does nothing reliably. So we start narrow and widen by measurement.
Which process, which systems, which decisions? Which steps are fully autonomous, which need human approval? What happens on error? The output of this week is the agent’s authority map.
An agent running on your real data, in the test environment of your real systems. The aim is to see whether the approach works before a large investment. Model choice (cloud LLM, open source or on-premise), tool integrations and the first guardrails are settled here.
The agent takes over a slice of real work; every decision is logged, and human intervention rate, accuracy and time saved are measured. The confidence threshold is tuned here: when the agent proceeds alone and when it asks.
Monitoring dashboards, alerting, a model update procedure and training for your team. We deliver the system so that your own team can operate it; you do not stay dependent on us.
Model independence: agents are not locked to a single LLM provider; the model layer is swappable. Tool standard: integrations are built with MCP (Model Context Protocol) and open API definitions, so adding a new tool does not mean a new project. Data stays where it belongs: when required we build agents that run fully on-premise with open-source models. Every decision is traceable: what the agent did and why is recorded in an auditable form.
Because agents act inside your systems, security design sits at the centre of the project, not as a layer added afterwards.
A good fit: if you have processes repeated dozens of times a day, spanning several systems and currently consuming staff time. Quoting, order processing, invoice matching, routing customer requests, price updates, reporting.
Not yet: if the rules of the process are still unclear, your data is not digital, or the job happens a few times a month. In that case we recommend process clarification or a simpler automation first; there is no need to rush into an agent.
Agent projects usually go hand in hand with AI integration: the tools an agent uses are the integrations into your existing ERP and CRM systems.
A chatbot answers a question with text and stops there. An AI agent takes a goal, plans the steps, does the work by using tools inside your systems and verifies the result. A chatbot explains how to make a return; an agent starts the return, creates the shipping label and notifies the customer.
For a narrowly scoped agent: a working prototype in 2-4 weeks, a pilot in 3-6 weeks, and 2-3 months in total including production rollout and handover. The biggest factors are the number of systems to connect and how ready the data is.
Irreversible actions (payments, contracts, outbound communication) are identified at the start and tied to human approval. For other actions the agent cannot exceed defined limits; every step is logged and a rollback procedure is defined. During the pilot the error rate is measured and the confidence threshold tuned accordingly.
That is your decision. In projects using cloud LLMs we apply data minimisation and masking; for sensitive processes we build agents that run fully on-premise with open-source models or on servers hosted in Türkiye.
Yes. The agent’s tools are integrations into your existing systems. Where an API exists we use it; otherwise we connect at database or file level. We have experience with Logo, Netsis, SAP, Salesforce, Trendyol and Hepsiburada, and you do not need to replace your systems.
Cost depends on scope, the number of systems to connect and the level of autonomy. The prototype phase is fixed-scope and low-budget; once you see that the approach works, the pilot and production budget is defined. Ongoing costs, LLM usage fees or on-premise infrastructure, are calculated and shown separately in the proposal.
In a thirty-minute call we will work out together which of your processes is suitable for an agent, which systems it needs to connect to and what the prototype requires. No commitment needed.
