104 stories
AI phone agents went from obviously robotic to occasionally indistinguishable from a person in about two years. The reason is not one breakthrough. It is that three separate components got good at the same time, and the engineering problem of connecting them got solved well enough.
What Happened When DealMind Could Recall Past Objections Building persistent deal memory with Hindsight changed how I thought about context, retrieval, and the boundary between application state and agent memory. The first version of DealMind could answer questions about a deal.
OpenAI DevDay 2026 ran on 29 September at Fort Mason in San Francisco. Sam Altman opened at 10:00 AM Pacific, with Romain Huet, Tejal Patwardhan and Holly Li following him on stage.
Every instrumentation layer is an interrogation. You add a trace, and the trace changes the testimony. When we wrap an agent's reasoning chain with logging, we are not passive observers. We are inserting variables into the system's decision surface.
Building ProductOps Memory: Teaching an AI Agent What a Team Learned Most AI agents are good at answering questions. The harder problem is remembering what a team has already learned.
The Problem Competitive information can appear in many forms: Feature and product launches Pricing changes Marketing campaigns Promotional offers Product announcements Changes in messaging Other market signals When these activities are viewed independently, it can be difficult to understand the…
Memory-Enabled Customer Support Agent — Dev-Ready Specification Project Overview 1.1 Problem Current customer-support systems are commonly stateless at the interaction level.
Sales conversations rarely happen in isolation. A customer might discuss pricing in one call, raise an objection in another, introduce a new stakeholder later, and finally ask for a specific next step several conversations after the first meeting. The problem is that these details are easy to lose.
Deal Oracle:Giving Sales Deals a Memory Sales teams work with a huge amount of information across their deals — customer requirements, stakeholders, competitors, objections, deal stages, risks, notes, and follow-ups.
The worst call my voice agent ever made went like this: "Hi Imran, Aisha from TableTap. Our billing app starts at just nine hundred ninety nine rupees a month." Imran runs a cloud kitchen.
Building Stateful AI Workflows: Moving Past Raw Logs with Long-Term Agent Memory If you've ever watched an LLM-powered assistant forget a critical workflow decision three turns after you made it, you already know the wall that stateless agent architectures hit.
In modern B2B sales cycles, account executives juggle multiple conversations with a single prospect over weeks or months. From initial discovery calls to deep-dive technical reviews, every interaction builds a complex web of specific requirements, budget constraints, security concerns, and…
For a long time, every invoice my agent saw was the first invoice it had ever seen from that vendor. It read the PDF, pulled out the fields, made a call, and forgot everything the moment the page reloaded.
What Should an AI Coding Agent Remember? Building Persistent Domain Memory An AI coding agent can write code today, solve a debugging problem, and understand an architecture decision — and then appear to forget all of it when the next session begins. That creates a practical problem.
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