techone --guide=ai-agents
How to Deploy an AI Agent in Your Business
We start with one task whose correct outcome can be checked. The agent receives only the data and tools it needs, and we define in advance which steps require human approval.
TL;DR
- Task
- An agent receives a defined task, an expected outcome, and rules for situations that must be handed to a person.
- Rule or agent
- A fixed workflow handles predictable steps. An agent helps with free text, documents, finding relationships, or proposing a classification.
- Context and tools
- The agent works only with the data sources and operations that the application exposes for its task.
- Permissions
- We separate reading, proposing an action, and writing to a system. Material actions may require approval from the responsible person.
- First validation
- We assess results on real cases, including errors, incomplete data, and situations in which the agent must stop.
How an AI Agent Works
An AI agent connects a language model with a defined task, data, and tools. It receives an input, works with allowed sources, and returns an answer or prepares a specific operation.
A chat interface usually generates an answer from the conversation. An agent can retrieve the current status of an order, compare records across systems, or call a prepared function. The application still defines the scope: which data the agent may use, which tools are available, and when the case must be handed to a person.
When to Use a Rule and When to Use an AI Agent
Power Automate and similar tools fit a flow that can be defined in advance: when A happens, do B. The same expected outcome can be tested repeatedly.
An agent may help when the input is free text, a document, or other unstructured content. Its output is probabilistic, so the two approaches often work together: a workflow controls the process while the agent handles one defined step.
Use a Fixed Rule
The input, condition, and result can be defined in advance. Examples include approval by amount, moving a file, or sending a notification. An agent would add complexity without a corresponding benefit.
Use an AI Agent
The input is free text, a document, or several sources whose relationships must be identified. The agent prepares a result and routes it for review according to the impact of the next step.
We first separate predictable steps, work with unstructured input, and decisions that remain a human responsibility.
Context, Tools, and Permissions
An agent works only with what the application exposes for its task. Its context may include a customer, order, document, or incident and links to the source records. The same principle is used in a business digital twin, which connects data and relationships across existing systems.
MCP (Model Context Protocol) standardizes the exchange of context and tool calls between AI applications and servers. Remote HTTP connections may use protocol-level authorization. A project must still define user identity, available tools, application permissions, separation of read and write operations, approval of material actions, and call logging. Before deployment, we also document which data is sent to the model provider and which retention rules apply.
This is how a query with read-only operations may proceed:
>query: how many of item X are in stock and what is the status of order 4471?
- ERP stock 320 units, 80 reserved
- order 4471 in production, date confirmed
- access check read-only operation
→ answer240 units free, order in production, date confirmed
When One Agent Is Enough and When to Split Roles
Several agents make sense when a process has clearly separated tasks, different permissions, or independent checks. One may collect information, another compare it with source records, and a third prepare the output.
An orchestrator then controls order and data transfer. Each role can be tested separately, but the number of calls, operational complexity, and places requiring error handling also increase. A single agent or standard workflow is usually enough for a simple task.
Tasks That Can Be Defined for an Agent
A well-defined task has a known input, specified sources, allowed operations, and a rule for cases that must be handled by a person.
Sales Preparation
The agent collects allowed company data, checks for a CRM duplicate, and prepares a briefing using defined criteria. Sales2Up can be one source of company data. Incomplete or conflicting cases are handed to the salesperson.
Queries Across Systems
A user asks about inventory, an order, or a customer. The agent retrieves only data allowed to both the user and the tool and keeps a link to the source in its answer.
Data Checks Across Systems
The agent compares selected fields across CRM, ERP, and registries and flags possible mismatches. A source record is corrected only after confirmation by the responsible user.
Document Processing
The agent extracts data from an invoice or order and compares it with available ERP records. A clear case continues through the configured workflow; an exception goes to the assigned user. See the document automation guide for details.
How We Validate the First Agent
We structure the first deployment so that result quality, source use, and behavior in cases where the agent must stop can be assessed separately.
Select One Task
We define the input, expected outcome, responsible role, and cases that must be handed to a person.
Prepare Context and Tools
We connect only the required sources and initially limit available operations to the necessary scope.
Test Real Cases
We include correct outcomes, incomplete data, errors, and situations in which the agent must stop.
Expand After Evaluation
We add more sources, users, or write operations only after confirming the results and review rules.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot usually produces a text response from the conversation. An agent has access to defined data and tools and can retrieve information or prepare an allowed operation across several steps. Its scope is determined by the implementation, permissions, and approval rules.
Do we need an AI agent, or is a standard workflow enough?
When the input, condition, and result can be defined in advance, we start with a standard workflow. An agent can help with free text, documents, finding relationships, or proposing a classification. The two approaches often work together in one process.
How does an agent access ERP or CRM?
Access is provided through a prepared API, MCP server, or another integration layer. For the task, we configure identity, available sources, and allowed operations, and separate reading from writing. The agent works with a defined part of the system rather than unrestricted access.
What does MCP handle, and what must be configured in the project?
MCP standardizes context exchange and tool calls, and remote HTTP connections may use protocol-level authorization. The project must still define user identity, application permissions, allowed operations, approval of writes, logging, and rules for data sent to the model.
How do you validate the first agent?
We select one task with a checkable outcome, prepare the required sources, and limit available operations. We then test common cases, incomplete data, errors, and correct handoff to a person. More sources, users, or write operations are added only after evaluating the results.
Related Topics
Start with One Task for an Agent.
In the initial consultation, we select the task, available data, and the conditions used to verify a correct outcome.