What is an AI agent?
An AI agent is software that uses a language model to decide how to complete a task. Unlike a fixed script, it can choose among available tools and adapt its next step to the result of the previous one.
The model does not work alone. A production agent also needs instructions, tool definitions, application state, access controls, stopping rules, and code that handles failures. These parts determine whether the agent behaves like useful software or an open-ended experiment.
A typical agent loop
- 01
Receive the task
Collect the request and the information needed to begin.
- 02
Choose an action
Select a tool, ask a question, or prepare a response.
- 03
Check the result
Read the tool output and update the task state.
- 04
Continue or stop
Finish, retry within limits, or hand control to a person.
Agent, chatbot, or ordinary automation?
The label matters less than the control flow. If the model does not decide what happens next, the system may be useful, but it is probably not an agent.
AI agent
The model chooses steps and tools while working toward a defined outcome. The path may differ between cases.
Investigate a support case · Prepare a supplier review · Resolve an internal request
Chatbot or assistant
The model answers or drafts content, but it does not control a workflow or act in another system.
Answer a policy question · Summarize a document · Draft an email
Ordinary automation
Code follows predetermined steps. This is usually the better option when the rules and inputs are stable.
Copy a field on submission · Send a scheduled report · Validate a known format
When an agent is a good fit
Agents are most useful when a task has a clear outcome but the route depends on language, documents, intermediate results, or exceptions. The task also needs enough feedback to tell whether the work was completed correctly.
Useful signals
- People currently interpret emails, documents, or free-form requests before choosing the next step
- The task uses several systems and the correct sequence depends on what each system returns
- A fixed rules engine has become difficult to maintain because exceptions keep growing
- A person can review uncertain or high-impact cases
- Success can be checked through records, tests, required fields, or another observable result
Use a simpler solution when
- The same known steps apply to every case
- A search or question-answering interface is enough
- There is no reliable way to check the result
- A wrong action would be unacceptable and cannot wait for approval
Agent tasks that can be built
Each agent should own a specific task, not a vague department-wide goal. These examples show the level of scope that can be designed, tested, and controlled.
Support case investigation
Read the request, find the account and recent activity, search approved documentation, propose a resolution, and route exceptions to the correct team.
CRM, ticketing, product database, knowledge base
Document intake
Classify an incoming document, extract required fields, check them against company rules, request missing information, and prepare the record for review.
Email, document storage, OCR, ERP or case system
Internal service requests
Understand an employee request, collect the required details, check policy, create the right ticket, and report its status in the same conversation.
Chat, identity, HR, IT service management
Research and reporting
Break a research question into searches, use approved sources, keep source references, compare findings, and produce a report in a defined format.
Search, internal documents, databases, reporting tools
Operations follow-up
Review incomplete or delayed work, gather the current state from connected systems, contact the responsible party, and update the record after a response.
ERP, project management, email, messaging
How the task is defined before development
A broad instruction such as "handle customer operations" cannot be tested or permissioned safely. Development starts by turning the work into a task contract with an observable beginning and end.
The task contract
- Trigger
- What starts the task and who is allowed to start it?
- Inputs
- Which information must be present before work begins?
- Allowed actions
- Which systems and operations may the agent use?
- Completion
- What record, answer, or state proves that the task is done?
- Escalation
- Which uncertainty, failure, or risk sends the task to a person?
Tools and integrations
APIs, databases, documents, browser-based software, MCP servers, and company functions become narrowly defined tools.
Instructions and state
The application keeps the task rules, relevant history, intermediate results, retry limits, and current status.
Model and routing
Model choice can vary by step. Simple classification does not need the same model as planning or document analysis.
User interface
The agent may work through chat, an internal application, an inbox, a background queue, or an existing product interface.
How much can the agent do on its own?
Autonomy is set per action, not for the agent as a whole. Reading a product catalogue and issuing a refund should not have the same approval rule.
| Level | Agent behavior | Human role |
|---|---|---|
| Read and explain | Reads approved sources and returns information without changing another system. | Checks the answer when needed. |
| Prepare | Drafts a message, form, decision, or system update but does not submit it. | Reviews and submits the action. |
| Act with approval | Plans and prepares an action, then waits before a defined high-impact step. | Approves, edits, or rejects. |
| Act within limits | Completes selected low-risk actions under value, scope, and retry limits. | Reviews logs and handles escalations. |
Permissions and safety controls
- Identity and access
- Every tool checks the user or service identity and applies the same permissions as the underlying system.
- Limited tools
- Tools expose the smallest useful operation. The agent does not receive a general database connection or unrestricted API token.
- Approval rules
- Sensitive, expensive, external, or difficult-to-reverse actions pause for confirmation.
- Untrusted content
- Emails, web pages, documents, and tool outputs are treated as data. Their text cannot grant the agent new permissions.
- Execution limits
- Time, cost, tool-call, and retry limits stop loops and contain unexpected behavior.
- Audit trail
- Logs record the task, tool calls, approvals, results, errors, and final status while avoiding unnecessary sensitive data.
How an agent is tested
An agent cannot be evaluated only by reading a few convincing conversations. Tests need real task examples, difficult edge cases, permission boundaries, tool failures, and cases where escalation is the correct outcome.
The initial test set becomes a repeatable evaluation suite. It runs again when the instructions, tools, model, or connected systems change. Production traces then reveal new cases that should be added to the suite.
- Task success
- Did the system reach the required final state?
- Tool use
- Were the correct tools called with valid arguments and in an acceptable order?
- Grounding
- Can factual outputs be traced to the allowed records or documents?
- Control
- Did the agent stop, ask, or escalate at the defined boundary?
- Operating cost
- How much time, model usage, and external service cost did a completed task require?
What you receive
The result is a deployable application for the agreed task, not a prompt file or a demonstration that only works on selected examples.
- 01 Source code and deployment configuration
- 02 Tool definitions and connected system integrations
- 03 Task instructions, state handling, and stopping rules
- 04 Authentication, permissions, and approval flows
- 05 Repeatable evaluation cases and test results
- 06 Logging, monitoring, and operating documentation
- 07 User or administrator interface included in the agreed scope
AI agent development questions
What is the difference between an AI agent and a chatbot?
A chatbot mainly produces a response. An agent controls part of a workflow: it chooses steps, uses tools, reads the results, and continues toward a defined outcome. A chat interface can be used for either system.
Does an agent need access to company data?
Not always, but most business agents need some approved context. This may come from documents, a database, an API, a search system, or records retrieved for the current user. Access should follow the permissions of the source system.
Can an agent use our existing software?
Yes. APIs are the preferred connection method. Databases, MCP servers, file systems, and message queues can also be used. If no suitable API exists, browser or computer-use automation may be possible, though it usually needs more testing and maintenance.
Do we need a multi-agent system?
Usually not at the beginning. One agent with a small set of clear tools is easier to test and operate. Separate agents make sense when tasks need different permissions, instructions, models, or ownership and the handoff between them is explicit.
Can a person approve actions before they happen?
Yes. Approval can be required for individual tools or conditions such as payment value, data sensitivity, external communication, or low confidence. The agent pauses with the proposed action and relevant context.
How do you prevent hallucinations?
No control can guarantee that a language model never produces an incorrect statement. Risk is reduced by limiting sources, requiring structured tool results, validating outputs, checking business rules in code, and sending uncertain or consequential cases to a person.
Can the agent run in our private environment?
Yes. The application, tools, data stores, and model can run in a private cloud or on company infrastructure when the selected models and dependencies support that setup. Hybrid designs can keep sensitive steps private while using external models for approved tasks.
Which AI model will the agent use?
The choice depends on task accuracy, language, tool use, response time, privacy, deployment, and cost. One application may use different models for classification, planning, extraction, or final writing. The model should be replaceable without redesigning the whole workflow.
How long does AI agent development take?
A narrow agent with documented APIs and clear approval rules takes less time than a workflow spanning several systems and teams. A useful estimate requires the task examples, system access, permission model, success criteria, and deployment requirements.
Can an existing agent be improved?
Yes. An existing system can be reviewed for task design, tool definitions, failure handling, permissions, model choice, evaluation coverage, cost, latency, and production traces. The review can produce a change plan or include implementation.
Technical references
The design principles on this page follow current primary guidance on agent architecture, evaluation, oversight, and security.