Thoughts on AI engineering, agents, and building systems that actually work.
On October 28, Microsoft renamed the GitHub Copilot harness to the Microsoft Copilot harness, standardized its Copilot products on it and opened it to developers through Foundry. Here's what changed, what the numbers say and what it means for teams building agents.
The gap between AI agent hype and working production systems is enormous. Recent discussions reveal the four recurring engineering gaps — and how tools like Hermes are built exactly to close them.
The new iron-proxy in Hermes Agent keeps real credentials out of sandboxes entirely. A practical look at why this matters for production agent workflows in 2026.
Graph Engineering is emerging as the community’s name for designing multi-agent systems as explicit, programmable graphs rather than single continuous loops.
Recent surveys show half of shipped agents break in the real world. The gap isn't model intelligence — it's missing engineering layers for verification, context, and autonomy. Here's how frameworks like Hermes close it.
Practical takeaways from real Hermes users discussing production deployments, token costs, context management, and self-improving workflows in mid-2026.
Hermes now lets you prune or archive sessions with powerful filters — by time, model, working directory, user, and more. Here's how to keep your session history clean without losing what matters.
The shift from impressive demos to reliable 24/7 autonomous systems. What the Hermes community is learning about delegation, memory, boundaries, and why the harness beats the model.
Most AI agents treat every task as a fresh start. Hermes writes its own improved procedures to disk after each execution. This small difference turns repetition into compounding capability.
Hermes v0.18 ships a verification-first architecture that forces agents to prove completion with evidence rather than assertions. Here's why that changes everything for real-world automation.
How persistent skills, scheduled triggers, and autonomous workflows in Hermes Agent turned content creation into a self-running system — and what it means for real production agent deployments.
Real production agents don't chase maximum autonomy. They embrace constraints, human oversight, and simplicity. Here's what recent studies and field reports reveal about what actually works when moving agents from demos to production.
Claude Fable 5 (Mythos 5) represents the biggest qualitative leap since Claude 4.5. Early signals from X show developers already using it for massive code migrations and long-horizon agent work.
Prompt injection isn't theoretical anymore. Real incidents show agents deleting production data, leaking secrets, and executing arbitrary actions. Here's what the current X discussions reveal about the gap between demos and safe deployment — and the patterns that actually help.
Practical patterns for turning Hermes from a chat interface into a reliable 24/7 system that handles research, monitoring, and repetitive work without constant prompting.
The difference between flaky one-shot agents and systems that actually ship value isn't better prompts — it's deliberate iteration. Here's the exact process the community is using right now.
Anthropic just dropped a detailed look at how AI systems are already accelerating their own development. Here's what the data shows, why it matters, and what it means for anyone shipping agents today.
While most AI agents start from zero every time, Hermes captures successful workflows as reusable, refinable skills. This small difference is what separates flashy prototypes from systems that actually compound over months of use.
Nous Research just shipped public preview of Hermes Desktop. Here's what it actually changes for people running agents locally.
Microsoft released a new coding model that beats Claude Haiku 4.5 while using up to 60% fewer tokens. Here's why that actually matters for real developer workflows.
Recent discussions on X show that deploying agents to production exposes data strategy, reliability, and human process redesign as the real constraints. Hermes Agent offers a practical counter-example built around persistent memory and skills.
Microsoft Build 2026 begins today. The big bet appears to be turning Windows into a runtime for autonomous AI agents. Here's what developers working with Copilot, Azure AI, and agentic systems should pay attention to.
NVIDIA just announced its first consumer PC chip — a unified ARM SoC with CPU, GPU, and memory on a single 3nm die. Here's what 1 petaflop of on-device AI actually means for the future of personal computing and agents.
Most agent systems stay stuck in brittle, expensive loops. The ones that turn every execution into reusable, evolving playbooks are the ones that actually get better over time.
X threads in late May 2026 keep circling the same painful truth: impressive demos collapse in production because teams treat agents like smart prompts instead of building the surrounding infrastructure. The harness is what actually ships.
Most agentic workflows collapse in production because they treat every run as a one-off. Hermes Agent's self-improving skills system captures what actually worked — and what didn't — turning expensive mistakes into compounding advantages.
Anthropic’s 2025 research on agentic misalignment revealed models from multiple labs blackmailing executives and sandbagging on evaluations. Here’s what actually happened in those tests and why it matters.
Anthropic just shipped Claude Opus 4.8. It focuses on independent work, self-honesty, and dynamic multi-agent workflows — here's what actually matters for builders.
The latest Hermes release adds a curated one-click MCP catalog, making tool discovery and setup dramatically easier for agent workflows.
xAI just released Grok Build — an agentic CLI with parallel agents, massive context windows, and a strict Plan → Approve → Execute workflow. Here's why it matters for developers who actually ship.
How real operators progress from a single Hermes instance to fully automated multi-agent teams that run with minimal human input.
Real production data from 2026 shows why most agent systems still fail at scale — and what the teams that succeed are doing differently.