229 stories
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.
Facts every session needs go in AGENTS.md, procedures go in skills, and rules that must hold go in hooks. How to share one handbook across six agents.
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.
OpenAI's new Dots are always-on agents that run on their own cloud computers, where they fix bugs or send out forgotten invoices, sometimes before anyone asks. Users can reach them through ChatGPT, Slack, and Microsoft Teams.
A mandated clarifying-question rule fired on 0 of 4 arms. Removing the broader rule it lost to restored it on 4 of 4. Why structure beats instruction, measured.
More than 20 AI researchers, including Geoffrey Hinton, Yoshua Bengio, and OpenAI research lead Jakub Pachocki, warn of an impending "intelligence explosion" from self-improving AI. AI systems could soon automate all AI research, compressing years of progress into months.
AGENTS.md best practices for multi-agent setups: root, scope, and role layers, subagent context rules, handoff contracts, and what to delete before adding.
Docker Cloud Sandboxes provide secure, hosted execution environments for running AI coding agents on Docker-managed infrastructure. Built on hardware-enforced microVM isolation, the platform provides a consistent execution environment and unified CLI workflows for seamlessly moving workloads from…
Since 2.1.277, Claude Code reads AGENTS.md only when no CLAUDE.md or CLAUDE.local.md is on the path. Before 2.1.281, telemetry off skipped it silently.
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