01 /
Handling requests
Read an incoming request, find the customer’s data, and prepare a reply or an action for review.
AI / agents
AI agent development
for your business.
An agent that completes tasks in your systems: it finds data, drafts replies and documents, and creates or updates records — within granted permissions and with approval for consequential actions.
LLM
MCP
Tools
Evaluation
Agent in a workflowrazrabotchik.com / AGENTS
Task01 / REQUEST
Agent and tools02 / CONTROL
Your systems03 / EXECUTE
Access and approvalsAction log
Starting point
Your systems and data
Boundaries
Allowed actions
Control
Quality and cost
When it helps
An agent helps where the task repeats but the steps differ each time.
An AI agent is a language model with tools: data search, API calls, record creation. The model chooses the next step itself, so what it may do and where a person decides are defined up front.
If a process fits a fixed script, conventional automation is more reliable and cheaper. That is checked before development starts.
02 /
Working with documents
Extract data from contracts, invoices, and forms and enter it into your business system.
03 /
Work across systems
Gather information from a CRM, a database, and external services, then carry out an agreed action.
Delivery
Boundaries first, autonomy second.
01 /
Choose the process
Tasks, sample inputs, and criteria for a good result.
02 /
Define tools and access
Which actions the agent may take and what needs a person’s approval.
03 /
Build the agent
Model, tools (including via MCP), context, and error handling.
04 /
Test on real tasks
A test set, quality, cost, and an action log.
Example approach
An agent for first-line support.
The agent reads a request, finds the order and customer history, and proposes a reply. If a refund is needed, the agent prepares it, and the refund runs only after a staff member approves it. Every action is logged; when data is missing, the request goes to a person.
This is a possible solution pattern, not a completed client project.
Task → action
Alone where safe. With a person where it matters.
Tools · access · logs
Questions
How is an agent different from a chatbot?
A chatbot answers questions. An agent takes actions: it searches data, calls APIs, and creates or updates records in your systems — within the permissions it is given.
Which model should we use?
It depends on the task, data requirements, and budget. The model is chosen after testing on your examples; the agent’s tools do not depend on the model, so it can be replaced.
How does the agent access our systems?
Through tools built on your existing API, often as an MCP server. Each tool checks the user’s permissions, and consequential actions require approval.
What if the agent makes a mistake?
Mistakes are contained up front: tool permissions, approval for important actions, and logging. Quality is measured on a test set before launch and after every change.
Next step
Describe your challenge.
Describe the system, goal, and constraints. You will receive a proposed next step, delivery format, and plan for technical oversight.