When it is a good fit
An agent may help when a task involves multiple information sources and the next step varies according to what it finds. One example is collecting information, spotting gaps and preparing an action for a colleague to review.
OXIAE / AI AGENTS & CHATBOTS
AI agent development.
Build an AI agent that performs defined tasks using your data and systems.
Discuss your projectUNDERSTAND THE SERVICE
What does a business AI agent include?
An AI agent uses information to choose next steps and call approved tools. A business implementation therefore requires more than a prompt: permissions, integrations, tests, logs and human approval are part of the solution. Oxiae starts with a defined task and checks whether an agent offers a practical advantage over a simpler workflow.
When to take a different approach
For a fixed sequence, a conventional workflow is often easier to test and maintain. A first agent should not receive unrestricted authority to spend money, send customer messages or change critical records.
PRACTICAL DECISION GUIDE
An AI agent starts with a bounded assignment
A useful agent has a bounded task, available tools and a clear endpoint. For example: find an existing customer, collect missing information and prepare a draft request. We specify which actions it may suggest, which it may execute and which require a person. [1]
The application checks each tool request independently of the model. A text instruction is not access control. We also test attempts to alter the task through documents or user messages. Cost, time and action limits prevent an ambiguous assignment from continuing indefinitely. [2]
Does the task require new research rather than only retrieving existing company documents? An AI research agent has a different brief: gather sources, preserve uncertainty and make the findings reviewable.
Questions to settle first
- Which concrete result means the assignment is complete?
- Which tool permissions does this exact task need?
Make acceptance measurable
- A permissions matrix and tests of allowed and blocked actions.
- A traceable run that stops at the agreed limit or handoff.
Illustrative workflow — not a client case study
From a customer enquiry to a controlled follow-up proposal
Suppose an email asks about connecting a CRM to an accounting package. The agent helps prepare the enquiry for review. It does not independently decide on a quotation, commitment or message to the customer.
Record the input and task
The enquiry receives a unique ID. The agent receives the email, sender details and permission to look up the relevant customer information. Its intended output is a proposed next step with the facts supporting that proposal.
Identify missing information
The agent separates information supplied by the customer from unknowns. If the accounting package is not named, it prepares a specific follow-up question. It does not guess a product, budget or delivery date.
Use only approved tools
The agent may read the matching CRM record and selected integration documentation. Findings retain their source references. Tools available during preparation cannot change customer records or send emails.
Ask a colleague to approve the action
The reviewer sees the facts found, missing information and a draft response. Adding a CRM note and sending a message each require explicit approval. Changes to the content or recipient require another review.
Stop on uncertainty or a reached limit
The agent stops when several customer records match, information conflicts or an agreed execution limit is reached. It hands the enquiry to a colleague with a reason and the available context. Nothing is changed or sent.
Execute only the approved action
After approval, a separate step processes only the approved content for the selected customer. The enquiry, approval and execution remain linked. Repeating the instruction must not send another message or add a duplicate note.
What the process delivers
The outcome is a supported follow-up proposal or a visible handover to a colleague. A system action follows only after the required approval. This is a possible design, not a reported result from a completed client project.
How to test this example
- Without approval, CRM records and outgoing communication remain unchanged.
- An unknown accounting package appears as an open question, not an invented fact.
- An ambiguous customer match or execution limit produces a handover with a reason.
- Repeating an approval does not execute the action twice.
Compare your options
Choose based on the task, the people involved and what you can maintain.
| Approach | Useful when | What to consider |
|---|---|---|
| Chatbot | The main task is answering questions in a conversation. | Producing an answer differs from carrying out a business action. |
| Workflow | The sequence of operations is largely known in advance. | Less flexible, but usually easier to inspect and test. |
| AI agent | Actions depend on intermediate findings. | Requires bounded permissions and tests for unexpected task paths. |
What I need to start
- A task description and an explicit list of prohibited actions.
- API access with the minimum permissions required.
- Examples of successful work, uncertainty and unwanted outcomes.
How to assess the result
- The agent requests approval where it is required.
- System actions can be traced to the task and relevant inputs.
- The agent stops or escalates when information is missing or a limit is reached.
SCOPE & INVESTMENT
What determines the cost?
A useful estimate starts with the scope. I distinguish development work from recurring usage and operating costs. These are the main drivers:
- A focused pilot: one task, a limited set of tools, representative test cases and human approval.
- Production operation: live integrations, role-based permissions, logging, stopping conditions and a controlled rollout.
- Usage volume, model steps per task, hosting and operator support.
- Maintaining evaluations and instructions when models, sources or business systems change.
APPLICATION AND SCOPE
Your challenge.
A practical application.
Illustrative scenario; not a published client case.
An employee gathers information from several systems for the same task every day.
An agent has more room to decide than a fixed AI workflow and therefore needs explicit limits on actions and access. If it must ground answers in business documents, a RAG system can supply relevant passages; that does not determine which actions the agent is allowed to take.
THE QUESTION I ASK FIRST
Which actions may the agent perform independently, and which need approval?
Agree the first step
Use the prerequisites, acceptance checks and cost factors above to define your assignment. Agree who can approve the outcome and who will maintain the solution. The project approach explains discovery, delivery and handover.
Read the project approachQUESTIONS ABOUT THIS SERVICE
Your questions.
Clear answers.
How is an agent different from a chatbot?
A chatbot focuses on a conversation. An agent can also select next steps and use tools to carry out a task. The functions can overlap; the actions and permissions determine the controls needed.
Can the agent update our CRM?
Only when the integration and permissions allow it. Changes can first be presented as suggestions. Independent updates also require validation, traceability and an agreed recovery approach.
Can AI agents be completely reliable?
Absolute reliability is not a responsible promise. The task must be bounded, evaluated with representative cases and monitored. Human review and clear stopping conditions are important for consequential actions.
Sources and technical basis
These primary sources explain the technical principles behind this guide. The project recommendations are OXIAE’s interpretation; they do not imply endorsement by the publishers. Product documentation applies to the named platform.
- LLM06:2025 Excessive AgencyOWASP GenAI Security Project
Risks from excessive functionality, permissions and autonomy.
Link and relevance checked: - LLM01:2025 Prompt InjectionOWASP GenAI Security Project
Basis for tests using manipulated instructions.
Link and relevance checked:
PREPARE YOUR PROJECT
Prepare a brief for this service.
Describe the scope below and download a draft for your project conversation. Unknown details remain open for discussion.
For your exploration of: AI agent development.
Make your request concrete
These optional fields turn the download into a brief for this service. Use general descriptions. Do not enter personal data, passwords, API keys or other secrets.
For example: prepare draft replies based on a support question.
Distinguish reading, proposing, writing and sending.
For example: before any external message or customer record change.
Describe an exception and who reviews it afterwards.
Your answers stay in this browser and are not saved. Refreshing clears them. The download is created locally.