The article argues that large language models lack the structured data, integrity constraints, and deterministic processes that business software provides. Instead of replacing software, LLMs will plug into existing systems via protocols like MCP, serving as a flexible front end while databases and rule engines remain the backbone.

Researchers at Tenet Security showed how anyone with a public Sentry DSN can inject a fake error report that coding agents like Claude Code, Cursor, and Codex will treat as a fix instruction. The agent fetches the malicious payload via the Model Context Protocol and runs arbitrary commands on the developer’s machine, exposing environment secrets and credentials. Sentry won’t close the write endpoint, leaving the fix to agent runtimes to filter untrusted data.

Stash is an open-source service that adds continuous memory to any AI model by storing and organizing session data in PostgreSQL with pgvector. It transforms raw conversations into structured facts, relationships, and patterns, so agents recall preferences, avoid repeated errors, and track long-term goals. Integration via MCP makes it model-agnostic, self-hostable, and free of vendor lock-in.

Stash lets your AI agent keep memory across conversations, so you don’t have to repeat context every time. It’s an open-source, self-hosted layer that uses Postgres, pgvector, and an eight-stage consolidation pipeline to turn raw observations into structured knowledge. It plugs into any MCP-compatible agent with a single Docker command.

The article argues that the Model Context Protocol (MCP) offers a more effective way to connect large language models (LLMs) to services compared to Skills. While Skills can help with knowledge transfer, they create unnecessary complications, especially when they require command line interfaces (CLIs). The author advocates for using MCP to streamline service integration and improve user experience.