7 free guides — no email required
Read what actually breaks before you deploy anything.
These guides cover the real failure modes of AI agent integrations — auth failures, duplicate sends, cost explosions, broken orchestration, and security gaps. Free to read, practical to apply, and honest about the risks.
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7practical guides available now
From $3Kwhen you are ready for implementation
FreeOperator Playbook Edition · 18 minute brief + implementation worksheet
Agent Setup in 60 Minutes
Low-code operator playbook for first-time builders
Build a production-safe AI workflow with human approval gates in under 60 minutes — without writing code.
The one finding that will change how you approach this
“Your workflow ran exactly as designed — and sent six identical emails to the same customer. No deduplication key, no volume cap, no test/prod separation: the three safeguards that take 20 minutes to add and are the difference between a smooth launch and an automation program that gets shut down by leadership.”
Best for
- ▸First workflow launch
- ▸Low-code stack selection
- ▸Approval-gated automations
FreeSystems Architecture Edition · 24 minute architecture brief + deployment blueprint
From Single Agent to Multi-Agent
How to scale from one assistant to an orchestrated team
Architect a coordinated multi-agent system with proper memory layers, role separation, and production-safe failure handling.
The one finding that will change how you approach this
“Adding parallel execution first — the instinctive move when scaling — is the mistake that kills multi-agent projects: teams spend more time debugging coordination failures than the parallelism saves, because the correct sequence is reviewer first, then planner, then parallel execution, and almost every team does it backwards.”
Best for
- ▸Framework selection
- ▸Multi-agent migration
- ▸Memory architecture design
FreeEmpirical Strategy Brief · 44 minute strategy brief + governance scorecard + red team worksheet
Agent Architecture: Empirical Research Edition
Production-grade evaluation, reproducibility, and governance
Build a defensible, reproducible evaluation protocol and governance framework for production AI systems — with real statistical grounding, not benchmark theater.
The one finding that will change how you approach this
“With n=50 — the most common evaluation set size — you cannot statistically distinguish 76% accuracy from 71%: the minimum detectable difference is 20 percentage points, which means most teams presenting model comparison results are presenting noise dressed as evidence, and the framework decisions that follow from that noise compound into months of architectural debt.”
Best for
- ▸Enterprise evaluation design
- ▸Governance reviews
- ▸Procurement-grade evidence packs
FreeSecurity Operations Edition · 28 minute security brief + threat model worksheetNew
MCP Security: Protecting Agents from Tool Poisoning
The definitive operator guide to Model Context Protocol threats and defenses
Understand every known MCP attack vector, implement prompt injection defenses, and build a tool trust model that holds under adversarial conditions.
The one finding that will change how you approach this
“A third-party MCP server's tool description field — the text that tells your AI what a tool does — is a direct write path into your agent's execution context, and no content filter catches it because hidden instructions inside documentation text look identical to legitimate documentation to every automated scanner.”
Best for
- ▸MCP server operators
- ▸Platform security reviews
- ▸Pre-deployment threat modeling
FreeIncident Intelligence Edition · 35 minute brief + incident response templatesNew
Production Agent Incidents: Real Post-Mortems
8 documented production failures — root causes, blast radius, and what actually fixed them
Learn from 8 real production incidents before they happen to you — exact failure modes, root cause trees, remediation timelines, and the governance changes that followed.
The one finding that will change how you approach this
“Every incident in this report was visible in the logs before it became an incident — the bulk-send spike was a 47x volume anomaly, the auth cascade was a wall of 401 errors for four days, the $47k cost explosion was 8x expected cost per session for 72 hours — and every signal was missed because nobody had written down what normal looked like.”
Best for
- ▸Pre-launch incident planning
- ▸Post-incident learning
- ▸Governance framework design
FreeSecurity Engineering Edition · 28 minute security brief + 12-item go/no-go checklistNew
OpenClaw Security Hardening for Production
Six threat surfaces, twelve controls, and the NemoClaw architecture that addresses all of them
Harden your OpenClaw deployment against the six documented threat surfaces — from plaintext API key exposure to indirect prompt injection — with controls mapped to specific incident classes.
The one finding that will change how you approach this
“The most common OpenClaw security failure is not a sophisticated attack — it is an indirect prompt injection via a retrieved webpage combined with plaintext API keys in environment variables. The attacker does not need to access your infrastructure: they need to control one document your agent reads. CVE-2026-25253 is reproducible with techniques that require no special expertise, and most production OpenClaw deployments are fully exposed to it right now.”
Best for
- ▸Production hardening
- ▸Enterprise procurement readiness
- ▸Regulated industry deployment
FreeEnterprise Infrastructure Edition · 32 minute deployment guide + security checklist + team onboarding worksheetNew
NemoClaw Enterprise Deployment Guide
Secure, production-ready deployment of OpenClaw & NemoClaw for teams and companies
Deploy OpenClaw and NemoClaw with production-hardened security, secrets management, and team access controls — without the CVEs that kill enterprise AI programs in their first quarter.
The one finding that will change how you approach this
“The most common enterprise AI security failure is not a sophisticated external attack — it is an engineer adding a production API key to their .env file "just for testing," which then gets committed. The fix is a developer vault instance with synthetic credentials configured before the first engineer joins the project, so the path of least resistance is also the secure path.”
Best for
- ▸First enterprise AI deployment
- ▸Regulated industry rollouts (finance, healthcare, legal)
- ▸Teams inheriting an existing insecure deployment