AGNTCon + MCPCon Europe 2026

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.

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/