Ai agents

Make your own event-sourced agent harness using stream processors — Jonas Templestein, Iterate

Make your own event-sourced agent harness using stream processors — Jonas Templestein, Iterate

Jonas Huckestein introduces a novel, event-sourced architecture for building AI agents. The core abstraction consists of three parts: a state, a synchronous reducer that derives state from events, and an after-append hook for side effects. This design ensures debuggability and allows state to be rebuilt without re-running expensive operations like LLM calls. A key innovation is the ability to deploy an agent by simply appending a 'dynamic worker configured' event—containing JavaScript code for a processor—to an event stream, eliminating the need for servers or complex deployment pipelines. This enables a distributed and composable ecosystem where processors from different authors can collaborate on a single stream.

You're Shipping 10x More Bugs and Don't Know It

You're Shipping 10x More Bugs and Don't Know It

Evan Marshall, CTO of Ito AI, discusses how the rapid rise of AI-powered code generation is creating a critical bottleneck in software verification and QA. He explains Ito AI's approach of using AI agents for automated, runtime execution testing on every pull request to act as a force multiplier for developers and unblock enterprise teams.

CI/CD Is Dead, Agents Need Continuous Compute and Computers — Hugo Santos and Madison Faulkner

CI/CD Is Dead, Agents Need Continuous Compute and Computers — Hugo Santos and Madison Faulkner

Madison Faulkner and Hugo Santos explain why traditional CI/CD, built for human developers, is failing under the load of AI agents. They propose a new paradigm of 'Continuous Compute' centered on intent-driven agent loops, fast inline validation, and a pre-merge layer where humans review outcomes, not diffs, paving the way for a 'multiverse' of parallel development.

Building AI Agents in Kotlin • Anton Arhipov • YOW! 2025

Building AI Agents in Kotlin • Anton Arhipov • YOW! 2025

Anton Arhipov from JetBrains introduces Koog, a lightweight, Kotlin-native framework for building tool-using LLM agents. This session covers the rationale for using Kotlin in AI, the architecture of Koog agents, and how its graph-based DSL enables the creation of structured, type-safe, and reproducible agent workflows, moving beyond simple prompt-chaining to sophisticated orchestration.

Why AI Agents Need an Operating System

Why AI Agents Need an Operating System

Current AI agents are powerful but lack memory, context, and safety, behaving like "genius goldfish." This summary explains the necessity of an AI Agent Operating System (OS) to provide essential infrastructure for managing memory, tools, identity, and governance, making agents reliable, scalable, and trustworthy.

Hierarchical Memory: Context Management in Agents — Sally-Ann Delucia

Hierarchical Memory: Context Management in Agents — Sally-Ann Delucia

The Arize team shares lessons from building their AI agent, Alyx, which analyzes its own trace data. They detail their journey from failed attempts like naive truncation and summarization to a successful strategy combining head/tail preservation with a retrievable memory store and using sub-agents to manage context complexity.