Why Choose Agentic AI for Engineers and Technical Teams Training Course?
Agentic AI for Engineers and Technical Teams Training Course provides engineering professionals with a comprehensive operational framework to design, build, and evaluate multi-step AI agents for complex technical workflows. Modern technical teams often spend excessive time piecing together answers from equipment manuals, operating logs, inspection reports, and maintenance records. This specialised training course addresses those bottlenecks by teaching participants how to automate document retrieval, tool execution, and drafting processes while maintaining rigorous engineering standards.
Throughout the five-day curriculum, participants engage in practical, hands-on labs covering technical documentation, equipment troubleshooting, inspection follow-up, and shift handovers. Rather than relying on basic chatbots, engineers learn to build sophisticated AI agents that handle missing information, reconcile conflicting manual revisions, and execute precise calculations. Every exercise is grounded in real-world engineering challenges, ensuring that participants master both the theoretical principles and the implementation details required for deployment.
By the end of this training course, attendees will have successfully built and tested an autonomous agent capable of reading equipment manuals, examining operating logs, and drafting evidence-based troubleshooting briefs. The curriculum emphasises human oversight, source traceability, and rigorous safety escalations, equipping technical leaders to safely introduce agentic workflows into their operations.
What are the Goals?
This training course is structured to bridge the gap between emerging artificial intelligence capabilities and rigorous engineering workflows. Participants will develop practical competencies in designing, testing, and governing AI agents tailored for technical operations.
- Identify engineering tasks suitable for an AI agent and define clear boundaries where human decisions and approvals are strictly required.
- Map complex technical workflows into structured inputs, tools, decision points, outputs, and verification steps.
- Build advanced AI agents that retrieve relevant technical information from approved manuals and accurately cite their source documents and revisions.
- Connect agents securely to structured data sources, operating logs, and calculation tools while incorporating automated checks for units and data quality.
- Deploy AI agents to streamline equipment troubleshooting, inspection reviews, shift handovers, and technical reporting.
- Test agent outputs rigorously for accuracy, traceability, and appropriate handling of missing, outdated, or conflicting technical evidence.
Who is this Training Course for?
This training course is suitable to a wide range of professionals but will greatly benefit:
- Mechanical, electrical, and operations engineers seeking to automate repetitive document reviews and troubleshooting tasks.
- Maintenance managers and reliability professionals looking to streamline equipment log analysis and shift handovers.
- Technical project managers and digital transformation leads responsible for implementing AI tools in engineering environments.
- Systems engineers and technical specialists tasked with building, testing, and governing automated technical workflows.
- Technical leads and operations supervisors who oversee inspection follow-ups and technical reporting accuracy.
How will this Training Course be Presented?
This training course is delivered through a balanced blend of expert-led instruction, architectural frameworks, and intensive hands-on technical labs. Participants actively build and test functional AI agents across five progressive days of learning.
- Interactive instructional sessions exploring agent fundamentals, retrieval-augmented generation (RAG), and engineering tool integration.
- Practical configuration labs where participants build document assistants, log-analysis agents, and troubleshooting brief generators.
- Collaborative peer discussions and workflow mapping exercises tailored to real-world engineering environments.
Participants leave with fully tested agent prototypes and actionable deployment strategies ready for implementation in their respective technical teams.
The Course Content
- How AI agents use instructions, tools and feedback to complete multi-step tasks
- Choosing between a chatbot, a fixed automation and an agent for an engineering task
- Identifying use cases in design, maintenance, operations, inspection and project delivery
- Mapping inputs, outputs, constraints and review responsibilities for a technical workflow
- Writing agent instructions and defining what to do when information is missing
- Practical lab Build an agent that turns a sample engineering request into a structured task brief
- Preparing manuals, specifications, procedures and inspection records for retrieval
- Using retrieval-augmented generation (RAG) to answer questions from approved documents
- Handling document revisions, applicability, equipment identifiers and conflicting requirements
- Citing source documents, sections and revisions in technical answers
- Managing access to proprietary information and testing resistance to malicious instructions embedded in documents
- Practical lab Build an equipment-manual assistant that cites its sources and flags questions requiring clarification
- Designing tool calls for spreadsheets, databases, calculators and read-only APIs
- Reading structured data from equipment logs, alarm histories and inspection tables
- Checking units, timestamps, asset tags, operating ranges and missing values
- Using calculation tools for threshold checks, trend summaries and engineering conversions
- Handling tool failures, limiting retries and validating results before using them downstream
- Practical lab Build an agent that reviews a sample operating log and reports exceptions against documented limits
- Combining alarms, operating trends and maintenance records into an event timeline
- Developing candidate fault explanations and identifying the evidence needed to test them
- Reviewing inspection records for incomplete evidence and items that need follow-up
- Drafting troubleshooting briefs, shift handovers and technical summaries from source records
- Separating observations, hypotheses and proposed actions, with escalation for safety-critical cases
- Practical lab Produce a troubleshooting brief with ranked hypotheses, supporting evidence and proposed diagnostic checks
- Defining acceptance criteria for technical accuracy, source traceability and escalation
- Testing with missing readings, conflicting manuals, incorrect units and failed tool calls
- Configuring human approval steps, access permissions and audit records
- Planning a pilot with a defined scope, review owner and performance measures
- Capstone build Connect a technical document source and an operating dataset to produce an evidence-based troubleshooting brief
Certificate
- AZTech Certificate of Completion for delegates who attend and complete the training course
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