A live, hands-on implementation masterclass for AI engineers, developers, solution architects, and GenAI practitioners — 120 minutes, start to finish. We set up a self-hosted Hermes agent from scratch, deliberately break it the five ways it actually breaks, and fix each failure in front of you.
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The quickstart question: “How do I get an agent running?”
The production question: “Why is it behaving differently than it did last week?”
Getting to a working agent takes about fifteen minutes. What takes weeks is everything after: a context limit nobody set on purpose, memory that confirms a save and then forgets it, compression that drops the one detail that mattered, a model that quietly changed underneath you, and a sandbox that turns out not to be a boundary.
This masterclass is about the part after the install works.
Built For You If
You're an AI engineer, software developer, solution architect, technical lead, or GenAI practitioner
You've got a self-hosted agent running and you're not confident it survives a month of real use
You're putting an autonomous agent somewhere it can touch real files, real credentials, or real customers
You can read and write code, and you'd rather see a system built than watch slides about one
You're the person who gets asked why the agent 'got worse', and needs a better answer than a shrug
Not For You If
We don't stop at the quickstart. We start where it ends.
Why this is different
Almost everything published about Hermes stops at Tier 1 or Tier 2. We start at Tier 3.
Fifteen minutes, a running agent, a working hello world. Genuinely useful for seeing the shape of the thing — and where almost all Hermes content stops.
A walkthrough of skills, memory and gateways exactly as advertised. Fine until one of them behaves differently in week three and the tour never covered why.
What you need after the install works: state you can debug, a context limit you set, a model you can prove, and a blast radius you chose. For engineers running an agent against something that matters.
Outcomes
Five concrete things you walk away with — designed to be applied to your own AI application on Monday morning.
Installed and configured on infrastructure you control — model wired up, config.yaml understood line by line, a gateway connected, and an agent doing real work. Built in front of you, not handed over as a link to a repo.
Wrong context detection, memory that reads as amnesia, compression loss, a model that changed underneath you, and a sandbox that isn't the boundary you assumed. You'll recognise each from the symptom instead of guessing for a week.
Active context, durable memory, session history, and profiles are four separate systems that fail differently. Which one to inspect first, what each command really tells you, and why fixing the wrong layer makes the problem harder to find.
What a laptop install genuinely exposes, what a container backend does and does not check on your behalf, and how to scope credentials, filesystem and network access before the agent has a bad day rather than after.
A practical pre-launch checklist covering context limits, memory hygiene, model pinning, tool permissions, monitoring, and recovery. Written to be applied to your own deployment on Monday morning.
Live Implementation · Not Slides
Once the agent works, we break it — the same five ways real deployments degrade after the install. Each one gets reproduced, diagnosed, and fixed in front of you, with the reasoning spelled out.
Failure 1
The CLI detects a context length from the model, and it is sometimes wrong. We watch an agent degrade against a limit nobody set on purpose, then pin context_length by hand and show the difference in the same session.
Failure 2
You tell the agent to remember something. It confirms. Ten minutes later it has no idea. We show why a mid-session save lands on disk but not in the running prompt, and where the size caps quietly truncate what you assumed was stored.
Failure 3
Long sessions get summarised so they can keep going, and summaries lose things. We watch a detail vanish that the agent needed, look at what protect_last_n and protect_first_n actually guarantee, and tell this apart from a memory bug.
Failure 4
A session-only switch, a restart, a fallback — and the agent is running something weaker than you configured. It looks exactly like the agent getting dumber. We reproduce it, then make the running model impossible to be wrong about.
Failure 5
The failure underneath the other four. What the agent can reach when it runs where you run, why a container backend skips dangerous-command checks by design, and how to make the blast radius a decision rather than an accident.
Nothing to install beforehand — we build everything live · What you'll see in the stack
Why Learn From Me
AI Consultant & Architect, IdeaToMVP · 12+ years in AI & enterprise systems
linkedin.com/in/surya-pratap-singh095I've trained engineering teams at TCS, IBM, and other enterprises on production AI — agents, RAG, and MCP — and run industry workshops for teams shipping AI into real products. As a consultant, I've helped companies rearchitect how they deploy AI, shipped 15+ systems to production, and trained 300+ professionals.
Every one of the five failures in this session is one I've debugged in a live system — usually at the point where the demo had already convinced everyone it worked. This masterclass is the session I wish existed before those debugging weeks.
Agenda
Tight, rehearsed, and ending exactly when promised. Questions are welcome throughout, not just at the end.
The gap between fifteen minutes to hello world and something you'd leave running. What we're setting up, and the five failures we're going to cause on purpose.
Install, model selection, config.yaml line by line, gateway connection, tools and skills, and a first agent doing real work — from scratch, live.
Wrong context detection, memory that reads as amnesia, compression loss, a silent model change, and a sandbox that isn't a boundary. Reproduced, diagnosed, and fixed in front of you.
What to watch, which command answers which question, how to bring a degraded agent back — and the production checklist you take with you.
Bring the agent problem you're actually stuck on. Questions are welcome throughout, and we close on the hardest ones.
FAQ
Yes — this is an implementation session. You should be comfortable reading Python, editing a YAML config, and following along in a terminal. You don't need prior Hermes experience; we set it up from scratch.
No. We set everything up live, so you can watch or follow along, whichever you prefer. If you want to build along, have an editor open, a terminal ready, and access to a model — local or an API key, either works.
Hermes Agent from Nous Research — open source, MIT-licensed, self-hosted. We keep the rest deliberately plain: Python, a config.yaml, one chat gateway, and a container sandbox. The failure modes are architectural, so everything we cover maps onto whichever self-hosted agent you're already running.
That's the ideal case. The setup is the warm-up; the value is the five failure modes and the diagnostic order of operations. Bring the behaviour you're seeing and we'll place it in the Q&A.
Yes — recording is sent to all confirmed attendees. The live Q&A is reserved for those who attend live.
None, and no. The session is free. At the end I'll mention the work we do at IdeaToMVP for teams who want help shipping this, but you're under zero obligation. The 90 minutes stand alone.
Registration takes under a minute. We confirm seats within 24 hours.
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