MCP/Agents/Skills Meetup - October 8, 2026

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. View more Computer Vision events here.

In this session, I will discuss how teams can identify repetitive manual workflows and turn them into reliable automation or AI-assisted skills. I will share practical lessons from building internal tools for CAD and engineering workflows, including how structured inputs, validation steps, and process standards help teams improve accuracy and reduce manual effort.

The talk will also cover why AI and agent-based workflows need clear boundaries, human review, and workflow context to be useful in production. Attendees will leave with a practical way to think about automation, agents, and internal tools as part of real team operations.

Garbage Inn. Is garbage out? This is true. For many input and output processes. In biological life and in computer systems, and equally true when working with LLM’s. The better the prompt, the better the context, the better the focus, And the better the contextual awareness, the better the quality of the output the LLM’s generates.

This is the governance, art and practice of what we like to call agentic engineering, something I've been practicing over the last year.

Most enterprise AI today is private by promise - a DPA, a SOC 2 report, or a contract clause that says, "we won't train on your data". For a regulated buyer, these are remedies after a breach, not controls that prevent or contain one. For organizations in healthcare, finance, defense, and government, privacy often requires stronger guarantees: data residency, customer-controlled execution, and, in some cases, operation within air-gapped environments.

This session demonstrates how agentic AI can automate a localization workflow while operating within these constraints. Using a real-world localization pipeline as an example, we will show how agentic systems can coordinate translation, review, quality assurance, and content preparation tasks while incorporating human checkpoints for approval and oversight.

We will also walk through the architectural patterns that enable these workflows to run inside customer-controlled and air-gapped environments without transferring sensitive content outside the customer boundary. The session includes a live product demonstration.

Key Takeaways

- Architectural patterns for deploying agentic AI in air-gapped and customer-controlled environments
- How agentic systems can automate localization workflows while preserving critical human review and approval processes
- Practical considerations for operating agentic workflows in regulated environments with auditability and governance requirements