Course Overview:

Duration: 30 min

Architectural Patterns for Building and Governing Production-Grade Multi-Agent Systems

The transition from experimental LLM chatbots to autonomous enterprise agents represents the next frontier of Generative AI. However, moving from ""thinking"" to ""acting"" introduces significant risks in reliability and governance. To move beyond brittle proofs-of-concept, architects must shift their focus from prompt engineering to standardized agentic architectural patterns.

In this session, we will explore the fundamental anatomy of an AI agent—Sense, Reason, Plan, and Act—and how to orchestrate these components into a distributed reasoning engine. We will specifically examine the shift toward capability-centric design, looking at how frameworks like OpenClaw are democratizing the creation of sophisticated agents and how Anthropic’s Skills.md initiative is standardizing how agents discover and execute complex tasks.

Architectural Patterns for Building and Governing Production-Grade Multi-Agent Systems

Key takeaways include:

The Anatomy of Agency: Understanding how LLMs serve as the cognitive core within a broader architecture of memory, tools, and perception.

The OpenClaw Impact: Analyzing how open-source frameworks are providing the scaffolding for multi-agent coordination, reducing the ""integration tax"" of building custom agentic workflows.

The ""Skills"" Revolution: A deep dive into Skills.md—explaining how markdown-based capability definitions allow agents to self-document, share, and scale their toolsets across different environments.

Essential Coordination Patterns: A look at Supervisor vs. Swarm architectures and the Agent Router pattern for intent-based task delegation.

Reliability & Compliance: Strategies for implementing Instruction Fidelity Auditing and Self-Correction loops to mitigate hallucinations in high-stakes environments.

Attendees will leave with a practical blueprint for building AI systems that are not only intelligent but also interoperable, portable, and production-ready.

Instructor:

Dr. Ali Arsanjani

Director of Applied AI Engineering | Head of AI Center of Excellence | Google Cloud

Dr. Ali Arsanjani is the Director of Applied AI Engineering at Google Cloud, and Head of the Global AI Center of Excellence, leads GenAI strategic co-innovation, thought leadership & alliances. His team specializes in co-innovation with ISV and GSI partners as they run, integrate and build on GCP across the ML Lifecycle. Ali also works closely with product management to shape the direction of Google's AI and analytics offerings from a cloud perspective. Ali is an Adjunct Prof at San Jose State University & the University of California, San Diego, and advises students in the Masters in Data program and the Data Science Institute, respectively.

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