Salesforce is pivoting toward a headless model with its Headless 360 initiative, allowing users to access CRM data through external tools like Claude, ChatGPT, Slack, and WhatsApp rather than relying on the traditional user interface. This strategy aims to reduce context-switching for knowledge workers by integrating Salesforce directly into their existing workflows. The approach has already seen significant adoption, with Anthropic increasing its Sales Cloud usage fivefold after accessing it via headless interfaces.
This article explores the critical architectural decision of where to store conversation history when building AI agents. It examines how different storage strategies impact user experience, privacy, cost, and portability. The author compares service-managed versus client-managed storage models and details how modern APIs support both linear threads and forking/branching capabilities.
Key topics include:
* Service-Managed vs. Client-Managed storage tradeoffs
* Linear (single-threaded) vs. Forking-capable conversation models
* Strategies for context window management and compaction such as truncation, summarization, and sliding windows
* How Microsoft Agent Framework abstracts these patterns using AgentSession and ChatHistoryProvider to ensure provider-agnostic code
* Practical implementation examples for the Responses API in different modes
Key topics include:
* Service-Managed vs. Client-Managed storage tradeoffs
* Linear (single-threaded) vs. Forking-capable conversation models
* Strategies for context window management and compaction such as truncation, summarization, and sliding windows
* How Microsoft Agent Framework abstracts these patterns using AgentSession and ChatHistoryProvider to ensure provider-agnostic code
* Practical implementation examples for the Responses API in different modes
This article details the first day of the OpenClaw Mastery course, focusing on installation and security. It explains the evolution of AI tools – from simple chat interfaces to agent harnesses and finally to proactive, always-on assistants like OpenClaw. The core idea is to set up OpenClaw on a VPS for isolation and security, emphasizing a cautious approach to capability and the importance of verifying the setup. The article highlights past security issues within the OpenClaw community and outlines a strategy to avoid them, prioritizing a slow and deliberate addition of features.
This article details a coding implementation of ClawTeam, an open-source Agent Swarm Intelligence framework. It demonstrates how to orchestrate multi-agent systems using OpenAI function calling, focusing on a leader agent that decomposes tasks, specialized worker agents for execution, a shared task board with dependency resolution, and an inter-agent messaging system. The implementation is designed to run seamlessly in Colab, requiring only an OpenAI API key, and showcases key components like task management, agent communication, and team registry. The tutorial provides a practical example of building and running a multi-agent swarm.
This position paper addresses the growing memory demands of multi-agent systems powered by large language models (LLMs). It frames multi-agent memory as a computer architecture problem, drawing parallels to traditional computer systems where memory hierarchy and bandwidth are critical bottlenecks. The authors distinguish between shared and distributed memory paradigms for agents and propose a three-layer memory hierarchy – I/O, cache, and memory – tailored for agentic systems. Key challenges identified include the need for protocols for cache sharing and memory access, and, crucially, establishing multi-agent memory consistency to ensure coherent and reliable operation.
OpenClaw is an open-source project that allows users to build and control AI agents. It provides a gateway for building agents and can be integrated with messaging apps such as Telegram. The article details the setup process, capabilities, and potential risks of using OpenClaw.
OpenClaw utilizes "skills" (packages of tools and instructions) and a "SOUL.md" file to define the agent's behavior and personality. The agent stores conversation history to improve its responses over time.
OpenClaw utilizes "skills" (packages of tools and instructions) and a "SOUL.md" file to define the agent's behavior and personality. The agent stores conversation history to improve its responses over time.
