Pepijn Senders, a principal engineer and former AI enablement lead at HelloFresh, explains how his team rebuilt a banking platform in six months with about 800,000 lines of code, mostly written by Sonnet, and why choosing a model that keeps developers involved matters more than raw autonomy. He covers measuring AI ROI through token spend versus output, keeping humans in control of code, the risks of agent recommendations, team refinement sessions, prompt word effects, and a meta harness built on Claude Code.
The AI Content System Behind 200M Views — Sarah Chieng, Cerebras & Cova Lee, OpenAI
Sarah Chieng (Cerebras) and Cova Lee (OpenAI) walk through an agent-powered content system spanning ideation, scripting, recording, editing, repurposing, and publishing, emphasizing swipe files, voice-driven scripts, design systems, API-based editing, and weekly process reviews as ways to free creators to focus on craft over generic AI output.
What Is Loop Engineering? Building Better AI Agents
Martin Keen explains loop engineering as the next step beyond prompt engineering: designing systems where AI agents act, observe, adjust, and repeat toward verifiable goals. The discussion covers goal design, triggers, context and persistent state, tools via MCP and APIs, verifier subagents, and why humans must remain in the loop to manage comprehension debt, intent debt, and cognitive surrender.
Stop Telling Agents How — Dan Barrett, Raymond Weitekamp & Jose Montes de Oca, OpenProse
The OpenProse team presents a declarative approach to multi-agent workflows, where developers and domain experts write contracts in plain markdown that any harness can compile and run. The talk covers event-driven jobs triggered by webhooks and pull requests, a grumpy-senior PR reviewer demo, a bank model-documentation workflow, composing contracts, pinning successful runs into deterministic workflows, and how the approach contrasts with DSPy.
Evals in AI: A Deep Dive — Tejas Kumar, IBM
This deep dive into AI evaluation (evals) starts by clarifying their role in providing ahead-of-time reliability and security for non-deterministic systems, contrasted with just-in-time harnesses. Tejas Kumar defines evals as "fuzzy unit tests" and outlines their core components: test cases, expected behavior, a judge, and aggregation. He explores various evaluation techniques, emphasizing the challenges and biases of using LLMs as judges, such as position bias, sycophancy, self-preference, and verbosity. The presentation details a practical methodology for calibrating LLM judges against human verdicts using a "golden dataset" to achieve an 80-85% agreement threshold, which can then be enforced as a CI/CD gate. A live coding demonstration illustrates the evolution from simple string matching to a policy-aware LLM judge, showcasing how refining policy and incorporating RAG (Retrieval Augmented Generation) via OpenRAG dramatically improves decision-making. The discussion also covers strategies for maintaining living datasets in production and managing evaluation costs.
How a Logistics Giant Keeps AI Data Locked Down
Jason Ward, FinOps for AI lead at C.H. Robinson, shares insights on managing AI costs for enterprises and startups. He details a FinOps strategy centered on comprehensive tagging, leveraging AI for observability (e.g., detecting prompt bloat and context starvation), and pioneering advanced metrics like "cost per thought" and "reasoning ratio" to optimize complex AI workloads beyond simple discrete tasks.
