Here's a helpful research document describing the different types of memory that you can easily plug into Hermes Agent.

If you work in a regulated data environment, you already know you can't just send your SQL Server query plans to a cloud LLM. After 18 months of building on a single RTX 3090, I'm excited to share the solution: Bob, an autonomous, locally-hosted AI agent for SQL Server self-healing that never lets your data leave the LAN. Today I'm releasing The Birth of Bob a completely free book detailing the architecture, the code, and why local inference is the only viable answer for DBAs drowning in 200-page health reports. Check out the article below to read it, join the AI-for-DBAs community, and drop your own 3 AM database war stories in the comments. https://minsondata.com/birth-of-bob

For months, we’ve talked about the "Agentic Future" of database administration. Today, I’m sharing the raw timeline of how that future became a reality. Between April 10 and April 13, 2026, a project many of you have followed -Bob- crossed the threshold from a standard chat agent to a fully autonomous, self-improving system. https://www.skool.com/ai-for-dbas-7678 Thanks to Vibe Coders

I’ve been watching the absolute madness unfold in the AI space over the last few weeks, and I want to drop some harsh but exciting truth on you: If you are still just building thin wrappers around text-generation APIs, it is time to pivot. We are officially transitioning from "Prompt Engineering" to "Agentic Orchestration." Here is the reality check on where the tech is at right now and how we need to adapt: 1. Models Are Taking the Wheel With the recent drops of models like Claude 4.6 and GPT-5.3-Codex, the focus has shifted entirely to "computer use" and autonomy. These models aren't just giving you Python snippets anymore; they are capable of navigating desktop environments, opening IDEs, and executing multi-step plans. The new meta is building sandboxes and guardrails for AI to act within, not just chat interfaces. 2. Open-Source is Destroying the Cost Barrier Models from DeepSeek, Qwen, and Zhipu (GLM-5) are currently dominating the open-source benchmarks. What does this mean for us? Intelligence is basically free now. Your competitive advantage is no longer the LLM you choose—it’s how efficiently you chain them together and the custom data you feed them. 3. The New Developer "Moat" So, where is the value for us as builders? - Tool Calling & API Integration: Building the bridges that let agents interact with the real world (Stripe, GitHub, AWS). - Multi-Agent Systems: Structuring workflows where a "Researcher Agent" feeds data to a "Coder Agent," which gets reviewed by a "QA Agent." - Eval & Reliability: Agents hallucinate and get stuck in loops. The engineers who figure out how to build reliable error-recovery systems are going to win this cycle. Let’s get a pulse check in the comments: Are you actively building agentic workflows yet? If so, what frameworks are you vibing with right now (LangGraph, CrewAI, AutoGen, or building from scratch)? Let’s build the future, not just chat with it.

In Part 2 of his series, I demonstrates the practical power of a local LLM-driven "AI DBA Analyst" that processed a massive 67,000-character SQL Server health report in just 12 seconds to identify three critical, interconnected performance issues. By utilizing a three-layer architecture collection, storage, and a Python-based intelligence pipeline the system successfully correlated memory pressure with log file growth and job slowdowns, providing immediate, actionable T-SQL fixes. Beyond simple analysis, I Try to highlight the AI's ability to modernize legacy database code by auditing and fixing 34 stored procedures, ultimately arguing that while AI lacks business context, it serves as an invaluable, tireless partner that allows DBAs to bypass manual data parsing and move straight to strategic resolution. ❤️‍🔥This is a real world solution for a real world problem solved by AI integration with legacy tools.🔥 👾👾👾💥👾👾 https://www.linkedin.com/pulse/part-2-my-ai-dba-analyst-found-3-critical-issues-12-ward-minson-6uz6c