Oct 06, 2026

Harness Engineering: How to Build a Software Factory — Dru Knox, Tessl

Dru Knox of Tessl discusses harness engineering, the discipline for building software factories with AI agents. He outlines a factory's three pillars—autonomy, automation, quality—and introduces inner, outer, and meta loops. Knox explains why this field is challenging and proposes a three-layer architecture: control plane, agent-ready stack, and improvement loops, concluding with key metrics to track progress towards an agent-driven software development future.

Oct 06, 2026

How AI Agents Pay: Checkout in ChatGPT and Google AI Mode — Sam Parsons, PayPal

Sam Parsons from PayPal Enterprise Payments explores three distinct ways merchants can accept payments initiated by AI agents: Instant Checkout in ChatGPT (Agentic Commerce Protocol), Google AI Mode (Universal Commerce Protocol), and a custom MCP app with an external checkout page. He details the technical flows, emphasizing tokenization for secure transactions and outlining the trade-offs between in-agent experiences and full merchant control.

Oct 05, 2026

The Transcript Looked Fine. The Call Wasn't. — Debugging Voice Agents, Arize

Fuad Ali from Arize AI explains why voice agents are uniquely challenging to debug, as critical issues like latency, interruptions, and tone are invisible in text logs. He presents a solution combining unified audio, transcript, and trace views with OpenInference semantic conventions and audio-native evaluations. The talk culminates in a vision for self-healing voice agents through automated observation, evaluation, and improvement cycles, demonstrating how to achieve robust, continuously improving AI voice systems.

Oct 05, 2026

Your AI Agent Is Confidently Wrong About Production — Willem Pienaar, Cleric

Willem Pienaar, CTO of Cleric, details why AI agents struggle with production debugging compared to code generation. He explains the unique challenges of dynamic, unverified production environments and LLMs' inherent confidence bias. Pienaar outlines four common agent failure modes (symptom/cause confusion, missing baselines, abstraction loss, anchoring) and introduces grounding techniques—like generating multiple theories, using baselines and dependency graphs, temporal analysis, and evidence propagation—to enhance diagnostic accuracy. He also emphasizes the critical need for verifying fixes against real outcomes to calibrate agent confidence, ultimately aiming to bring production signals into the development environment for proactive failure prediction.

Oct 05, 2026

Agent Memory Is Solved. Agent Learning Isn't. — Karthik Ranganathan, Yugabyte

Karthik Ranganathan of Yugabyte discusses the critical challenge of shared learning among AI agents, arguing that while per-agent memory is solved, true collective intelligence is not. He details Yugabyte's struggles in building agentic systems, highlighting the "lost context" problem and debunking five common misconceptions about agent memory. Ranganathan introduces Meko, an agent-native persistence layer designed to provide shared, governed, and traceable context through "data packs," drastically improving RAG pipeline faithfulness while reducing context size and token costs. Heather Downing demonstrates Meko's resumability and sharability features, showcasing its ability to foster collaborative learning and capture tribal knowledge across agents.

Oct 05, 2026

The Current State of Consumer AI

The latest a16z Top 100 Consumer AI Apps report uncovers a power-user dominated economy, where a small percentage of users drive the majority of AI spending, primarily on productivity and creative tools. It highlights the rapid emergence and challenges of personal AI agents like Muse and Instinct, emphasizing privacy and high serving costs. The discussion also covers the limitations of subscription models, the burgeoning role of advertising (with OpenAI's $1B run rate), the competitive landscape of leading AI labs (ChatGPT, Claude, Gemini), and the vast untapped consumer AI white space in areas like social, dating, and entertainment, suggesting a shift from "save time" to "spend time" applications for future growth.