A virtual meetup exploring practical AI agent architecture. Talks cover multi-agent coordination, guidance and context, deployment choices for regulated environments, and combining MCP interfaces with reusable agent skills.
When
Oct 8, 2026
Where
Online event
Attendance
Virtual
Status
scheduled
Event type
Meetup · Published by organizer
Registration
See the organizer for current access, pricing and registration details.
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.
Multi‑agent systems are powerful but choosing the wrong interaction pattern can quickly lead to fragile, slow, or expensive AI systems.
In this talk, we explore the core multi‑agent design patterns enabled by ADK, including sequential, parallel, and more advanced coordination models. Rather than focusing on tools alone, we’ll look at how to think architecturally about agent collaboration.
You’ll learn: - When sequential agents are the right choice and when they become a bottleneck - How parallel agents improve speed and coverage (and the trade‑offs they introduce) - Common failure modes in poorly designed agent interactions - Practical criteria for choosing the right pattern based on task, latency, and reliability
By the end of the session, you’ll have a clear mental model for designing multi‑agent systems that are intentional, scalable, and production‑ready.
This talk shows how MCP and Agent Skills work together in practical agent systems. MCP gives agents a standard interface to tools, data, and workflows; skills encode the operating discipline that makes those connections reliable. Using a sanitized field-operations ledger as the case study, the talk walks through source intake, normalized state, uncertainty labels, role prompts, QA gates, and share-safe status drafting.
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
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sponsors
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exhibitors
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attendees
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agenda
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Sources
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Published schedule: 9:00 AM - 11:00 AM PST. Exact time and timezone withheld because organizer abbreviations are not independently reconciled. Full biographies/abstracts retained in immutable HTML receipt. Sponsor, exhibitor and attendee lists not inferred from speaker employers. Session local day: the published single-day event page groups these agenda items/talks on 2026-10-08. Exact times remain unknown where timezone labels are unresolved.