---
title: "🎵 Every step you take, every call you make - the reliable agent stack"
category: "talks"
date: "2026-07-01"
time: "1:55pm-2:15pm"
track: "Harness Engineering"
room: "Main Stage"
speakers: ["Giselle van Dongen"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Harness Engineering"
scheduleRoom: "Main Stage"
scheduleLabels: ["Harness Engineering", "Main Stage", "session", "confirmed"]
---
# 🎵 Every step you take, every call you make - the reliable agent stack

## Conference Context
- Date/time: 2026-07-01 · 1:55pm-2:15pm
- Track/room: Harness Engineering · Main Stage
- Speaker(s): Giselle van Dongen
- Session type/status: session · confirmed

- Track: Harness Engineering
- Room: Main Stage
- Session type: session
- Status: confirmed

## Session Description
In this session, we skip past the demos that work only on your laptop, and go straight to how you can build production-ready agents with a stack that covers all the hard bits of backend development that you don’t want to be bothered with when developing your agents: - Failure resiliency: retries, timeouts, and exactly-once execution so a flaky API or a crashed process doesn't corrupt your agent's state or makes them start from scratch - Durable Sessions: a session store with built-in conversation isolation and protection against corruption from concurrent agents - Pause/resume for human approvals: survive human approvals and research that take weeks without building complex infra - Agent-to-agent messaging layer: call agents developed by other teams or running on other infra with resilient HTTP calls - A kill switch: cancel a running agent cleanly at any point, without leaving half-executed work behind We will demonstrate each concept with live code examples, using Python, OpenAI Agents SDK and Restate as open-source Durable Execution engine. All examples are generally applicable: pick your favorite agent SDK (OpenAI Agents, Pydantic AI, Vercel AI, Google ADK,…) or go wild and implement low-level custom agents by just tying together LLM calls with custom logic.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[giselle-van-dongen]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# 🎵 Every step you take, every call you make - the reliable agent stack ## Conference Context - Date/time: 2026-07-01 · 1:55pm-2:15pm - Track/room: Harness Engineering · Main Stage - Speaker(s): Giselle van Dongen - Session type/status: session · confirmed - Track: Harness Engineering - Room: Main Stage - Session type: session - Status: confirmed ## Session Description In this session, we skip past the demos that work only on your laptop, and go straight to how you can build production-ready agents with a stack that covers all the hard bits of backend development that you don’t want to be bothered with when developing your agents: - Failure resiliency: retries, timeouts, and exactly-once execution so a flaky API or a crashed process doesn't corrupt your agent's state or makes them start from scratch - Durable Sessions: a session store with built-in conversation isolation and protection against corruption from concurrent agents - Pause/resume for human approvals: survive human approvals and research that take weeks without building complex infra - Agent-to-agent messaging layer: call agents developed by other teams or running on other infra with resilient HTTP calls - A kill switch: cancel a running agent cleanly at any point, without leaving half-executed work behind We will demonstrate each concept with live code examples, using Python, OpenAI Agents SDK and Restate as open-source Durable Execution engine. All examples are generally applicable: pick your favorite agent SDK (OpenAI Agents, Pydantic AI, Vercel AI, Google ADK,…) or go wild and implement low-level custom agents by just tying together LLM calls with custom logic. ## Media Evidence No related AI Engineer channel video found yet. ## Evidence Graph This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Speaker And Company Context
- [[giselle-van-dongen|Giselle van Dongen]] — Developer Advocate at [[restate|Restate]].

### Topics Covered
- [[agentic-search]]
- [[ai-sandboxes]]
- [[coding-agents]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.