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  Session Handoffs: The Missing Piece of AI Productivity Estimated reading time: 6 minutes Image Placeholder: A clean editorial illustration showing one AI session handing context to another through a structured handoff document. Your AI Isn't Forgetting. Your Handoffs Are. One of the biggest misconceptions about working with AI assistants is believing that productivity is lost because the model forgets previous conversations. It doesn't. Every new session begins with exactly the amount of context you provide. If that context is incomplete, the next session spends valuable time rediscovering decisions, retracing investigations, and repeating work that has already been completed. A good session handoff prevents that from happening. What Is a Session Handoff? A session handoff isn't a meeting summary or a project status update. Instead, it's a context document designed specifically for the next AI session. Its purpose is simple: Preserve the reasoning behind the work—not...

  How to Use OpenAI Codex Inside Claude Code with MCP Claude Code is already capable of inspecting repositories, editing files, running commands, and coordinating complex engineering work. But there are times when you may want Claude to delegate a focused task to OpenAI Codex—for example: Ask Codex to implement a change while Claude reviews it. Use Codex for a second independent code review. Let Codex clean up a technical review document. Run a repeatable Codex skill from a Claude Code session. Use Claude as the orchestrator and Codex as a specialist execution agent. The cleanest way to build this workflow is through the Model Context Protocol , or MCP. However, the phrase “install Codex inside Claude” can create the wrong mental model. Codex is not inserted into the Claude model. Instead, Claude Code launches Codex as a separate local process and communicates with it through an MCP tool interface. Claude orchestrates. Codex executes. Claude verifies. Alt text: Claude Code connecte...

The Five-Gate Method: A Practical Framework for Building Reliable AI Agents AI models have become remarkably capable at writing code, generating content, and solving complex problems. Yet even the most advanced models can still fail in surprisingly simple ways. They may solve the wrong problem, rely on an incorrect assumption, verify the wrong thing, or present uncertain conclusions with unwarranted confidence. These aren't intelligence problems—they're workflow problems. The Five-Gate Method introduces a structured process that improves reliability by guiding AI agents through five checkpoints before declaring a task complete. Instead of asking an AI to simply "think harder," the method encourages disciplined execution. Why Reliability Matters In production systems, correctness matters more than impressive reasoning. A beautifully written answer still fails if it: misunderstood the objective never inspected the real data fixed the wrong issue assumed success...

  Claude Automatic Context Compaction: Keep Long-Running AI Agents Working Beyond Context Limits Long-running AI agents can perform impressive multi-step tasks, but they face a practical limitation: their conversation history keeps growing. Every instruction, model response, tool request and tool result occupies space in the context window. In a tool-heavy workflow, this information can accumulate quickly. Eventually, the agent may become slower, consume significantly more tokens or approach its context limit. Anthropic’s Claude Cookbook presents a solution called automatic context compaction . Instead of carrying the complete conversation history through every step, Claude can summarize the important information, remove unnecessary historical details and continue the task using a smaller, focused context. What Is Automatic Context Compaction? Automatic context compaction is a context-management technique for long-running Claude agent workflows. It monitors the number of tokens bei...