AI Agent Development — Ask in Plain Language, Get Evidence and Next Actions
An AI agent is an AI that finds the data needed to answer when someone asks in plain language, and proposes — or carries out — what to do next. TECH2030 builds agents that answer questions about production data on manufacturing sites, as well as business agents for internal document search and customer service. Agents act only within set rules and approval procedures.
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- StepsAssessment · Design · Build · Operation & adoption
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- Related topics
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- Related insights
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- Languages
What TECH2030 does — on the manufacturing floor
- It takes questions about production, quality, staffing, and planning in plain language.
- It breaks shortfalls or problems down by cause and shows the data behind the answer.
- It proposes next actions — for example, it creates a layout plan, has it checked in the digital twin's edit mode, and asks a manager for approval.
- Frequently used questions are kept as suggested questions that can be run right away.
Questions it answers on the manufacturing floor
- “Why did today's output fall short of the target?”
- “How should we arrange the equipment to raise output?”
- “Which line should we fix to lower the defect rate?”
- “Make this week's production plan from last week's data.”
Business agents beyond the plant floor
- Internal document search and Q&Asearches regulations, manuals, contracts, and technical documents and answers with sources. Access control by permission is key.
- Customer servicehandles inquiry classification, first replies, and conversation summaries, and sets the criteria for handing off to a person.
- Multi-step workcarries out lookup → judgment → execution on your behalf within rules and approval procedures.
People approve the execution
Layout plans and production plans made by the agent are applied to the floor only after a manager reviews and approves them.
How it connects to other services
On the manufacturing floor, the agent answers on top of the smart factory's production analysis and planning, and its layout plans flow into the digital twin's edit mode.
Process — Assessment → Design → Build → Operation & Adoption
STEP 1Assessment
We identify which questions need answering in which work, and what data exists.
STEP 2Design
We decide the question types, the data to connect, and the approval flow.
STEP 3Build
We build the agent, the evidence view, and the suggested questions.
STEP 4Operation & adoption
We refine the answers and suggested questions based on the questions that actually come up.
Deliverables
- AI agent for questions on business data
- Answer view with causes and supporting data
- Layout plan creation and approval request flow
- List of suggested questions
What determines the scope
- The kinds of questions it must answer
- The data and systems to connect
- The approval flow
Checklist before you start
- The questions often asked on the floor or in the work
- Where the data needed for the answers lives
- Who approves execution
Latest on AI Agents
Frequently asked questions
What questions does it answer?
On the manufacturing floor, it answers questions about production, quality, staffing, and planning.
Can we check the basis for its answers?
It shows the data behind each answer together with the answer.
Does the agent change the floor directly?
No. It creates a layout plan and asks a manager for approval.
Can it be used for work other than manufacturing?
Yes. We also build business agents such as internal document search and Q&A, and customer service.