Workshops

In this hands-on workshop you’ll build a working autonomous agent from the harness up, in a notebook, then see it live in a full working web application and leave with one that can write and run its own automations. You’ll implement every surface area yourself: a set of predefined tools, persistent memory through the Oracle AI Agent Memory package, orchestration with LangChain and LangGraph, and LLM access through OCI GenAI Service, composing the full set of Oracle primitives into one harness you understand end to end.

Most teams assemble that harness from a dozen disconnected services: one store for vectors, another for state, a separate reranker, a bolt-on memory layer. We take the opposite approach, on a single unified memory core. The organizing principle is optionality by default: you shouldn’t have to choose your memory substrate up front. With Oracle AI Database you get file system and database memory in one place, embedding models and rerankers running inside the database kernel, and every retrieval strategy an AI workload needs without leaving the core.

And consolidating onto one core is what keeps the whole thing tractable. You know the drill: a production harness has you holding all those moving parts in your head at once, and most of your attention goes to keeping them in sync rather than improving the agent. Pull that sprawl into a single core and the cognitive load drops. You get to think about what the agent does, not where its state lives. That’s the difference between controlling your harness and renting its pieces.

Speaker: Ignacio Martinez — AI Developer Advocate, Oracle

Most teams made a person the API between their agents and everyone else: copying the output into chat and pasting the reply back into the harness. This hands-on workshop shows the alternative: put the agents where the work is. We use Buzz, Block’s open source, channel-driven workspace where people and AI agents share conversations, repositories, and reviews.

We begin in the middle of a live project, with four people and a swarm of agents working across shared channels and code. Then we rewind and bring the room in. Attendees will join a shared Buzz community, work with an agent, form channels with other participants, and practice delegating, steering, reviewing, and coordinating work as it happens. Along the way, we examine how Buzz approaches portable identity, scoped capabilities, signed work, and collaboration across different agent runtimes and models.

By the end, the room itself becomes part of the workflow: everyone contributes code, assets, and reviews to one shared project in a Buzz-hosted Git repository. Bring a laptop. Leave with practical experience running many agents in one shared workspace, where the whole team can see, steer, and use them.

Pre-work: Please download: https://buzz.xyz/

Speakers:

• Bradley Axen — Tech Lead, Block
• Wes Billman — Engineer, Block
• Morgan Martin — Design Lead, Block
• Tyler Longwell — Engineer, Block

Enterprise adoption of the Model Context Protocol is accelerating, and MCP has become the primary way agents connect to enterprise tools and data. But MCP is only part of how agents act. Agents also run CLIs, execute Skills, and generate code that calls APIs directly. Governing MCP well matters. Governing everything else agents can do matters just as much.

Building MCP servers and writing Skills isn’t particularly hard. The real challenges are deciding which actions agents are allowed to take, controlling who can take them, and proving it all later. These are architectural questions, and they need answers before agents scale across an organization.

In this workshop, we will:

1. Show how to control agent actions with policies that apply across MCP servers, CLIs, Skills, and agent-generated code — including allowlists, access control by users and groups, and human-in-the-loop approvals.
2. Explain why enterprises need managed registries for MCP servers and Skills, and how admin review and approval change the trust model.
3. Work through audit and compliance requirements: capturing complete logs of agent and tool activity, exporting to enterprise storage, and generating reports.
4. Demonstrate how to discover shadow AI — unmanaged agents, MCPs, and Skills already running in your organization — and how to block them or bring them under management.
5. Look at token usage and spend visibility by agent, user, and group.

You’ll leave with a clear picture of the architectural decisions ahead of you, and a better sense of what your security team will require before signing off on scaling AI agents across your organization.

Speakers:

• Shannon Williams — President, Obot AI
• Chris Urwin — VP of Field Engineering, Obot AI

As AI agents move into production, engineering teams face a growing set of challenges. How do you secure and govern MCP servers without modifying them? Route and fail over across multiple LLM providers? Enforce rate limits, access controls, and governance policies? Observe agent traffic, and scale operations across environments?

Rather than embedding these capabilities into every agent, MCP server, and application, organizations can adopt a single architectural pattern: the agent gateway.

An agent gateway acts as a unified control plane for AI systems. It can function as an MCP gateway, LLM gateway, inference gateway, and traditional API gateway, centralizing security, observability, routing, resilience, and policy enforcement across agents, tools, models, and services.

In this hands-on workshop, you’ll learn how to secure and federate MCP servers without code changes, route and fail over LLM traffic across providers, enforce authentication and usage policies, and gain end-to-end visibility into agent interactions. Through practical exercises, you’ll see how a single gateway layer simplifies operations while enabling secure, scalable, and governable AI systems.

Speaker: Lin Sun — Head of Open Source, Solo.io

AI coding agents feel like magic. It is easy to assume there is something exotic inside, some secret sauce that makes agents reliable. Well, there isn’t, the core of every AI coding agent is dead simple: send a message to a model, parse tool calls, execute them, feed the results back, repeat. Everything else (context management, permissions, observability, safety guardrails) is engineering layered on top of it that we should be building.

In this deep dive, we build that engine from scratch (in Java!), live on stage. Not to build the best agent, but to understand how the pieces fit together. We point it at a real codebase, and watch what happens. It compiles. Tests pass. And it violates every convention the team agreed on. So we iterate. We add context, constraints, and feedback, and at each step we examine what changed, why it helped, and what it maps to in the tools you already use.

The goal is a mental model. By the end, you will understand the components inside the AI coding tools you use every day, what you can layer on top to get smoother results and safer expectations, and where the honest limits still are, the gap no amount of engineering has closed yet.

Speaker: Ji Darwish — Data Platform Engineer, Xomnia