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Build, ship, and operate Autonomous AI Agents at production scale. Master Model Context Protocol Connections, current systems automation, third-party application integrations, ReAct loops, multi-agent orchestration, and observability — the complete Autonomous Agent AI mastery program.
865+
Composio apps covered
MCP
Model Context Protocol native
2wk
First production agent
24/7
Post-program support
The Problem
The same patterns kill 80% of autonomous AI agent projects. Hermes Mastering breaks every one.
Your prototype is impressive. Then users hit it and it falls over. Without proper architecture, evals, observability and durable execution, every agent ships with this curse. Hermes Mastering breaks the cycle.
Stripe changes a webhook, HubSpot deprecates an endpoint, Salesforce updates its API. Every change breaks your agent. MCP Connections solve this — and we teach how to build, secure, and operate them.
Your agent runs 47 tool calls when it should run 3. Without proper planner-executor separation, loop budgets, and cost monitoring, every customer costs you $4 in API spend. We teach the patterns that fix this.
Embeddings + cosine similarity isn't enough. Without re-ranking, hybrid search, and proper chunking, your agent confidently quotes the wrong policy or makes up a fake source. We teach production RAG that actually works.
What you'll master
The complete skill stack required to ship Autonomous AI Agents that work in production — not just in demos.
The fundamentals of Autonomous AI Agents: agent loops, planner-executor separation, ReAct vs ReWoo vs Plan-and-Execute, when each pattern is the right choice, and how to avoid the most common failure modes — runaway loops, token explosion, and hallucinated tool calls. We start from first principles and build to production-grade architecture.
Model Context Protocol is Anthropic's open standard for connecting LLMs to tools, data sources, and third-party apps. We cover everything: scaffolding MCP servers in TypeScript and Python, designing secure tool surfaces, schema versioning, authentication, rate-limiting, audit trails, and debugging stale connections in production deployments at scale.
Most organizations have 20+ years of accumulated systems: ERPs, CRMs, custom internal tools, legacy databases. Autonomous agents that ignore this reality fail. We teach how to wrap current systems with MCP servers, build safe automation flows that respect existing business rules, and migrate manual processes to agentic workflows without breaking what already works.
Coordinated agent teams that solve problems no single agent can handle: a planner, a researcher, a coder, a reviewer, a verifier. We cover LangGraph, AutoGen, CrewAI, the Anthropic /team pattern, and the AISB Matrix-themed orchestration. Plus the failure modes — race conditions, context corruption, infinite loops — and the patterns that prevent them.
Retrieval-Augmented Generation as it is actually built in 2026: hybrid search (BM25 + dense), re-ranking with Cohere or Voyage, chunking strategies, semantic caching, contextual compression, and metadata filtering. We cover Pinecone, Weaviate, Qdrant, pgvector, and the trade-offs of each. The agent that retrieves the wrong context confidently is worse than no agent at all.
Real autonomous agents run for hours, sometimes days. Trigger.dev v4, Inngest, Temporal, and AWS Step Functions handle the durability problem: agents that survive crashes, retries, deployments, and infrastructure failures. We cover task definitions, batching, queues, retries, realtime streams, and human-in-the-loop patterns for risky operations.
An agent without observability is a liability. We cover LangSmith, Helicone, OpenTelemetry traces, custom dashboards, drift detection, regression suites, and cost monitoring per agent / per user / per workflow. Plus eval frameworks — Promptfoo, Phoenix, custom — that catch quality regressions before users see them.
Autonomous agents have superpowers: they can spend money, send emails, modify databases, and deploy code. Without guardrails, that is catastrophic. We cover permission scoping, approval flows, audit logging, prompt injection defenses, data residency, and the policy frameworks that let CISOs sign off on deploying agents to production.
Curriculum
Sequential, structured, capstone-graded. Each module builds on the previous one. By the end, you have shipped an autonomous agent to real users.
What you'll build
These agents are running in production right now, deployed by past Hermes Mastering cohorts.
An autonomous agent that monitors HubSpot, enriches leads via Apollo, scores them with custom ML, drafts personalized outreach, schedules cadences via Outreach.io, and updates Salesforce — all without a human touching a CRM. Built and shipped by Hermes Mastering cohort #3 in 4 weeks.
When PagerDuty fires, the agent pulls logs from Datadog, queries application metrics, correlates with recent deploys via GitHub, identifies the likely cause, drafts a Slack incident channel summary, and proposes a remediation runbook. Humans approve before any production change. Cohort #4 shipped this to a fintech.
Monthly close used to take 8 days across 4 humans. An autonomous agent reconciles QuickBooks transactions, flags anomalies, prepares draft journal entries, and routes for CFO approval. Close now takes 2 days. The agent uses MCP Connections to NetSuite, Stripe, banks, and email — all governed by audit trails.
Not full replacement — copilot. The agent reads incoming tickets, retrieves relevant docs via hybrid RAG, drafts responses, and routes complex cases to the right human. Resolution time dropped 60%, CSAT went up. Built on LangGraph + Zendesk MCP + custom vector DB.
The agent monitors contract expirations in Coupa, drafts renewal negotiations, benchmarks pricing against market data, and routes to procurement for approval. It also pre-fills vendor onboarding paperwork via the Notion + Docusign + Stripe MCP connections.
An agent that watches competitor websites, monitors product hunt and Hacker News, summarizes weekly into a Notion page, and pings the product team in Slack when something material changes. Uses web search MCP + browser MCP + custom RAG over internal strategy docs.
Comparison
Why upskilling your existing engineers via a structured program beats hiring an entire AI engineering team.
| Aspect | Hire / DIY | Hermes Mastering |
|---|---|---|
| Time to first production agent | 3-6 months | 2 weeks (capstone) |
| MCP Connections covered | Rarely taught | Build, secure, ship your own |
| Third-party integrations | Hand-rolled, fragile | Composio + custom MCP |
| Durable execution | Cron + hope | Trigger.dev + Inngest patterns |
| Observability | console.log | LangSmith + Helicone + custom evals |
| Production reliability | Restart when broken | SLO-driven, alert-paged |
| Vendor lock-in | Closed agent platforms | Open source + MCP standard |
| Engineer cost | $200-350K AI team | Fixed program fee |
Tech stack covered
From Model Context Protocol to vector databases to durable execution to observability — every tool that matters in 2026.
Anthropic's open standard for connecting LLMs to tools, resources, and prompts — the foundation of modern agentic systems.
865+ pre-built integrations with built-in auth, retries, and rate limits. The fastest path to multi-app agents.
State machines for production agent workflows. We cover when to use them and when to bypass them.
Streaming, tool use, and edge deployment for web-facing agents — the production standard for Next.js apps.
Durable execution for long-running agents: tasks, queues, retries, realtime streams, and human-in-the-loop.
Event-driven step functions for agents that need to survive crashes and orchestrate complex flows.
No-code orchestration layer for hybrid agent / deterministic workflows. Strong when you need observability and quick iteration.
Vector databases for RAG — we cover the trade-offs and migration paths between each.
Postgres-native vector search for teams that don't want a separate vector DB. Often the right choice for <10M vectors.
Re-ranking is the highest-leverage RAG improvement. We teach when and how to add it.
Observability for LLM apps: tracing, datasets, cost monitoring, drift detection, eval pipelines.
Open-source eval frameworks. The difference between agents that work and agents that work reliably.
Self-hosting open-source models for air-gapped deployments — required in finance, defense, healthcare.
Hyperscaler model platforms with data residency, compliance, and enterprise procurement.
Who this is for
Same curriculum, tuned for different starting points and outcomes.
You want to deploy autonomous agents across sales, support, finance, and ops — and you need a CISO-approvable path. Hermes Mastering delivers the curriculum, the governance framework, and the production playbook for 20-500 engineer organizations.
You ship AI products. Hermes Mastering covers the architecture decisions that separate startups whose agents work in demos from startups whose agents work in production. ReAct vs Plan-and-Execute, MCP vs custom, when to use frameworks, when to bypass them.
You know React, Node, Postgres. You want to add autonomous agents to your toolkit. Hermes Mastering takes you from 'I can call the OpenAI API' to 'I can architect, ship, and operate production autonomous agents.' 6-8 weeks, part-time, capstone-graded.
You bill clients to deploy AI agents. Hermes Mastering means your team delivers faster, more reliably, and with better governance — and your margins improve. We offer a dedicated agency track with white-label playbooks and client-facing artifacts.
Pricing
From senior engineer to 500-engineer enterprise deployment. Same curriculum, different scale.
For senior engineers and AI founders who want to go from API caller to autonomous agent architect.
For engineering teams shipping production agents — with governance and observability baked in.
For 20-500 engineer orgs deploying agents in regulated industries with full governance.
Social proof
Anonymized quotes from engineering leaders who completed the program and shipped agents to production.
“Before Hermes Mastering, our agents broke in production weekly and we didn't know why. After the program, we ship agents with the same rigor as any other production service — evals, SLOs, on-call rotations.”
VP Engineering
B2B SaaS, 120 engineers
“The MCP module changed how we build everything. We replaced 14 hand-rolled integrations with 4 MCP servers. Less code, fewer bugs, faster onboarding for new agents.”
Principal Engineer
Fintech, 250 engineers
“I ran my capstone project — an incident response agent — for two weeks during the program. It now handles 40% of our on-call alerts autonomously. That alone paid for the program 10x over.”
Staff SRE
Series C SaaS
Deliverables
Concrete artifacts, not vague promises. Every engagement includes these outputs.
FAQ
Everything you need to know about Hermes Mastering. Still curious? Book a discovery call.
The complete Autonomous AI Agents stack: ReAct loops, planner-executor architecture, Model Context Protocol (MCP), multi-agent systems, RAG with hybrid retrieval, durable execution via Trigger.dev and Inngest, observability with LangSmith and Helicone, and production third-party integrations.
No. Hermes Mastering is for full-stack engineers and CTOs who want to ship autonomous agents in production. We assume strong TypeScript or Python, REST/GraphQL fluency, and a working understanding of LLMs — not deep ML.
LangChain courses teach you a library. Hermes Mastering teaches you the architecture: when to use LangChain vs LangGraph vs raw SDK, when ReAct loops make sense, how MCP changes the design, and how to operate agents in production without 3 AM pages.
Model Context Protocol is Anthropic's open standard for connecting LLMs to tools, data sources, and third-party apps. It replaces fragile hand-rolled integrations with a reusable, secure, versioned protocol. We cover building servers, securing them, and running them at scale.
Yes. We cover Composio (865+ apps), n8n, Make, Zapier as orchestration layers, plus deep dives on Stripe, HubSpot, Salesforce, Slack, Linear, Notion, Google Workspace, and Microsoft 365 — the most-asked-for production integrations.
We teach LangSmith, Helicone, OpenTelemetry, and custom evals. You leave the program with dashboards monitoring latency, token cost, success rate, drift, and hallucination — plus alerting playbooks when SLOs degrade.
Yes for the OSS path. We cover on-prem deployment of open-source agent frameworks, local model serving with vLLM and Ollama, and air-gapped MCP server hosting — important for finance, defense and healthcare clients.
Post-program Slack support, a monthly agent code review, regression suite updates as model providers change, and emergency paging if your production agents misbehave. Think of it as a fractional AI SRE.
Partly. It dramatically increases the productivity of the engineers you already have — most teams need fewer net-new AI hires after going through the program. Some clients use it specifically to upskill existing senior engineers rather than competing in the AI talent market.
Anthropic Claude (Opus 4.7, Sonnet 4.6, Haiku 4.5), OpenAI GPT-5, Google Gemini 3, plus open-source models via vLLM and Ollama. We are model-agnostic — the architecture matters more than the specific provider.
Claude Code Mastering is about using Claude Code as a developer productivity tool. Hermes Mastering is about building autonomous agents your users interact with. Many teams take both — they cover complementary skill sets.
You ship a real autonomous agent to real users. Past capstones include incident response agents, sales ops agents, financial close automations, customer support copilots. Code-reviewed by senior engineers, defended live to the cohort.
Keep exploring
More ways to go deep on autonomous agents, MCP, and the Agentik {OS} ecosystem.
Master Claude Code as a developer productivity tool — complements Hermes for user-facing agents.
Done-for-you Model Context Protocol server deployment for your existing systems.
24/7 autonomous AI agent deployment runtime with audit trails and rollback.
The complete written guide to building Autonomous AI Agents with MCP Connections.
Durable background tasks, Trigger.dev workflows, and current systems automation.
Real autonomous agent deployments by past Hermes Mastering cohorts.
Book a free 30-minute discovery call. We will walk through the curriculum, review your existing agent architecture if you have one, and design a delivery plan that fits your stack, your industry, and your engineering culture.
Or read the full autonomous agents guide first.