Prepare customer replies
An agent gathers an enquiry, the relevant customer record and approved guidance, then drafts a reply with sources for a team member to review.
11 · AI & Automation
AI agent development: Give your team help that can take the next step.
Lumaivo builds custom AI agents for business: tools that read approved information, work across your systems and help complete a defined task. From preparing an answer to gathering the details for an approval, each agent has a clear purpose and knows when to involve a person.
What this service is for
AI agents for business, built around a real task.
An AI agent combines a language model with access to selected information and tools. That makes it useful when a task involves understanding an email, finding context across documents or deciding which approved step to take next. A chatbot usually starts with a conversation; an agent can also carry out work through connected systems.
We start with one job your team can describe and check. We identify the information it needs, the actions it can take and the decisions that need review. We then test it against representative examples, including incomplete records and ambiguous requests. The result is a business tool with visible progress, source references and a route back to a person.
AI is one part of the workflow. Fixed calculations, permissions and repeatable checks belong in conventional software. Frontier models can interpret less structured information, while the surrounding application controls access, records actions and handles failures. That separation makes the whole process easier to understand and maintain.
Where it becomes useful
Common use cases, framed around the decision or hand-off.
Triage incoming work
Read incoming requests, identify missing details and suggest a category, priority and owner before the work enters your queue.
Find the context for a decision
Bring together the relevant documents, records and exceptions so a person can make an informed decision without searching several systems.
Method
A controlled route from discovery to an accepted output.
- 01
Choose one useful job
Agree who the agent helps, the task it handles and what a correct result looks like.
- 02
Connect approved knowledge
Map sources, permissions and available APIs. Keep each answer linked to the information behind it.
- 03
Set action boundaries
Decide which actions can run automatically and which require confirmation, with clear stopping conditions.
- 04
Test with real scenarios
Check accuracy, failure handling, response time and operating cost using representative, approved examples.
- 05
Launch and improve
Introduce the agent to a small group, review its work and agree how knowledge, models and connections will be maintained.
Typical deliverables
What the review-ready work can contain.
- A task definition and agreed success measures
- Connected knowledge sources and approved tool access
- A working agent with human review where required
- Evaluation examples and recorded test results
- Action history, failure handling and operating guidance
- Handover and an agreed support scope
Boundaries
What stays explicit instead of being over-promised.
- Access follows the permissions agreed for the task.
- Important actions can require a person’s approval.
- Uncertain or unsupported answers are flagged for review.
- Model and infrastructure usage costs are scoped separately from development.
Questions before the first conversation
Plain answers, with the limits left in.
What is the difference between an AI agent and workflow automation?
Workflow automation follows defined rules: when a form arrives, validate the fields and create a record. An AI agent can interpret less structured information and choose among approved tools or steps. Many useful business systems combine both approaches.
Can an AI agent work with our existing CRM and documents?
Often, provided the systems offer suitable APIs, exports or approved access. We assess the connections and permissions during discovery. Access to a system is never assumed just because an AI model is available.
Will the agent make decisions without asking us?
That depends on the agreed task. It can prepare a recommendation while a person approves the outcome. Routine, reversible actions can be automated where the rules and testing support it. Approval requirements are designed into the workflow.
How much does custom AI agent development cost?
The main factors are the number of systems involved, the quality of the available information, the actions permitted and the testing required. We scope a focused first version before quoting, and separate development from ongoing model, hosting and support costs.
Do we need a custom AI agent or a ready-made tool?
Start by checking whether a tool you already use can handle the job with appropriate access and review. A custom agent becomes useful when the task spans several systems, needs your own business rules or requires a specific approval process. Bring one repeated task and a few redacted examples so we can compare the options before proposing a build.
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Send the systems, reports, constraints and decisions involved. Lumaivo will help define a bounded first step and the evidence needed to accept it.
Map the first useful step