# One Man Ops — Full Site Content > Security-isolated OpenClaw deployments for business operations. Built and operated by Andres, who runs a live fourteen-agent production system with role isolation, human oversight, and documented failure modes. --- ## Services and Pricing Your AI agents should not have access to everything. Neither should your bill. Every deployment is security-isolated by design — separate credentials, separate permissions, separate failure boundaries. Pricing is public because buyers deserve to evaluate before they call. ### Foundation — $1,500 setup / $500 mo A security-isolated OpenClaw deployment for operators who want AI agents running with clear permission boundaries — not full access to everything. Monitoring, documentation, and human oversight included. Included agents: - Operator — routes work through approval gates, never acts without permission - Inbox — handles inbound messages and triage - Watchdog — monitors failures, health, and regressions Standard deliverables: - Architecture documentation - Cost transparency report - Failure mode runbook - Monthly operations review ### Growth — $3,000 setup / $1,000 mo More agents, same isolation model. Each role gets only the credentials and tools it needs — content agents cannot touch payments, payment agents cannot touch publishing. Parallel processing without parallel risk. Included agents: - Operator — orchestrates business workflows - Inbox — captures and routes communication - Watchdog — monitors system health and anomalies - Signal — drafts content and outbound messaging - Ledger — tracks tasks, handoffs, and business records - Every agent operates in its own security boundary with documented permissions Standard deliverables: - Everything in Foundation - Parallel processing design - Expanded isolation map - Maintenance review and optimization notes ### Custom — $5K–$15K setup / $2.5K–$5K mo Full architecture consultation for teams that need domain-specific agents with tailored permission boundaries, compliance-grade documentation, and operating procedures built around how your business actually works. Included agents: - Custom agent count and role design - Full architecture consultation - Custom permission boundaries - Deployment planning around your workflows Standard deliverables: - Custom isolation architecture with per-agent credential mapping - Isolation and credential design - Failure response planning - Monthly operations review --- ## How It Works Isolation is the product. Multi-agent is the delivery mechanism. OpenClaw can touch shell access, browser control, email, and automation loops. One Man Ops structures those surfaces into bounded roles so a failure in one place does not automatically spread everywhere else. The problem: A single agent with too many permissions creates an enormous attack surface and makes failures harder to contain. The solution: Bounded roles create clearer accountability, safer credentials, and a system that is easier to monitor and repair. The operating model: Deployments include documentation, cost visibility, and human oversight so the system stays understandable after launch. Separate credentials. Separate permissions. Separate blast radius. --- ## About Andres runs One Man Ops. Not a team. Not an agency. One operator with fourteen AI agents and the uncomfortable conviction that most people giving advice about autonomous systems have never actually run one. The practice deploys security-isolated OpenClaw systems for business operations — the same architecture, the same isolation model, and the same oversight patterns that run this business every day. The sales pitch is the production system. There is no demo environment. ### What is actually running Fourteen agents across three coordinated systems, each with its own job and its own leash: The business operations layer runs as a hub-and-spoke model. One orchestrator holds the approval gates. Dedicated agents handle front-end development, payments, market intelligence, content, customer communication, and quality verification — each with scoped credentials and documented permissions. Nobody touches what they do not need to touch. The intelligence and content layer runs the research and publishing pipeline. Five collection and analysis agents gather intelligence, draft content, and produce daily operational briefs. It runs on its own schedule, mostly while the operator is asleep or doing something more interesting. A strategic coordination layer sits above both, decomposing business strategy into phased execution plans and dispatching work to the systems below. Think of it as the part of the operation that remembers what the business is supposed to be doing this week. Every agent operates with scoped credentials, documented permissions, and its own failure boundary. One agent misbehaving is a Tuesday. One agent misbehaving with access to everything is a catastrophe. The architecture exists to keep Tuesdays from becoming catastrophes. ### Failure modes nobody warns you about Running multi-agent systems in production is an education in all the ways software can fail without raising its hand. Some highlights from the curriculum: - Provider cooldown bugs that silently stall agent execution — no error, no timeout, just silence where work used to be - Coordination loops where agents politely re-trigger each other forever, like two people holding a door open and neither one walking through - Compaction amnesia — the LLM context window fills up, the platform summarizes it, and the agent forgets what it was doing mid-task. Solved with a three-layer memory architecture that writes state to disk before the platform gets a chance to erase it - Edit race conditions during parallel execution, where two agents try to update the same file and the last one to save wins - Silent cron failures — jobs that stop running with no error output, discoverable only when you notice the absence of results These are documented in internal runbooks and inform every client deployment. The runbooks exist because each failure happened at least once in production. Some of them happened more than once, because operators are optimists. ### The oversight model The system runs with roughly three to five hours of daily operator involvement across all three layers. That includes reviewing intelligence briefs, approving execution gates, monitoring deployments, and intervening on exceptions. This works because oversight is architectural, not manual. Approval gates, scoped permissions, failure boundary isolation, and automated monitoring do the heavy lifting. The operator focuses on decisions, not babysitting. Full autonomy is not the goal. Observable, bounded autonomy with a human who can pull the brake is. ### Security is the product One Man Ops covers AI agent security as a core practice area: - OWASP Agentic Security Top 10 — not as a checklist, but as operational reality - OpenClaw-specific CVEs and rapid-response analysis — seven disclosed vulnerabilities covered within 48 hours of disclosure - Prompt injection defense, MCP trust model analysis, and ClawHub supply chain risks - Per-agent credential isolation, tool scoping, and monitoring architecture that treats every new permission surface as a risk decision The security content on this site comes from operating a system that would be the first to suffer if the advice were wrong. ### Why this exists This started because the operator needed it. Not as a product idea. Not as a service concept. As a solution to the problem of running a business with AI agents that would not accidentally email a client's payment link to the wrong person, or post draft content to a live channel, or quietly stop working on a Thursday night with no indication anything was wrong. The service exists because the architecture that solves those problems for one operator solves them for others — and most operators building with AI agents are not going to build this themselves. They should not have to. Agencies sell what they can build. One Man Ops sells what it actually runs. --- ## FAQ ### Why multi-agent instead of a single-agent setup? The point is not agent count. The point is isolation. Different responsibilities run with different permissions so one failure does not automatically expose everything else. ### What is the total cost of ownership? The service fee covers architecture, deployment, monitoring, and monthly review. On top of that, expect roughly $150–$350 per month in LLM API costs and $15–$25 for VPS hosting. There are no hidden revision fees, no per-ticket charges, and no annual maintenance surcharges. The price on this page is the price. ### What does monthly operating cost look like? Typical infrastructure and API spend sits on top of the service fee. Typical ranges run roughly $150–$350 per month in API usage plus about $15–$25 for VPS hosting, depending on workload. ### What happens when an agent breaks? Each deployment includes a failure mode runbook, active monitoring boundaries, and human oversight so issues can be contained, diagnosed, and corrected quickly. ### How is my data isolated? Credentials, tools, and scope are separated per role wherever possible. One agent should not have access to every credential, inbox, or execution surface in the system. ### Why should I trust a solo operator over an agency? I built this system to run my own business. The same architecture, the same agents, the same isolation model — I use it every day. Agencies sell what they can build. I sell what I actually operate. ### What is included in the maintenance retainer? Ongoing monitoring, monthly operating review, issue triage, and routine updates to keep the deployment aligned with the way your business actually runs. ### How long does setup take? Timeline depends on the scope and the number of business workflows involved, but the service is designed to get a practical system live without enterprise-style implementation drag. ### What if I only need one agent? If one agent is enough, that may be the right answer. OneManOps is built for operators who need safer task separation and business-grade operating procedures rather than a basic single-agent install. --- ## Blog Posts ### AI Agent Security: The Threat Landscape — What's Actually Happening Part 1 of a 3-part security series covering real AI agent incidents, current threat patterns, and the numbers operators should pay attention to. https://onemanops.com/blog/ai-agent-security-threat-landscape-whats-actually-happening ### AI Agent Security: Practical Hardening — How to Secure Your Agents Part 2 of the security series covering per-agent credentials, scoped API keys, tool scoping, provider isolation, monitoring, and audit trails. https://onemanops.com/blog/ai-agent-security-practical-hardening-how-to-secure-your-agents ### AI Agent Security: OpenClaw-Specific — CVEs, Defaults, and What to Fix Part 3 of the security series covering OpenClaw-specific vulnerabilities, exposure patterns, the ClawHub supply-chain issue, and the versions operators need to patch. https://onemanops.com/blog/ai-agent-security-openclaw-specific-cves-defaults-and-what-to-fix ### Seven Security Flaws in 23 Days — What Every OpenClaw User Needs to Do Right Now OpenClaw has taken seven disclosed security hits in 23 days. Here is what matters, what categories of exposure repeat, and what operators should do immediately. https://onemanops.com/blog/openclaw-safety-essentials-seven-security-flaws-in-23-days ### OpenClaw vs. Claude Dispatch: What Actually Changed Managed mobile-first agents did not make self-hosted agents obsolete. The real difference is trust model, control, and where your data lives. https://onemanops.com/blog/openclaw-vs-claude-dispatch-what-actually-changed ### Your AI Agent Got Hacked and You Didn't Click Anything Zero-click prompt injection is no longer theoretical. Here is what Bargury demonstrated, why MCP makes the problem structural, and what operators should change right now. https://onemanops.com/blog/your-ai-agent-got-hacked-and-you-didnt-click-anything ### Claude Channels Shipped With Its Own Injection Warning Anthropic shipped Claude Channels with an explicit prompt-injection warning. The transport may be secure, but the permission model still defines the real risk. https://onemanops.com/blog/claude-channels-shipped-with-its-own-injection-warning ### The Karpathy Loop: What Happens When AI Starts Running Its Own Experiments What Andrej Karpathy's autonomous experiment loop actually means, why Shopify's overnight replication matters, and how the same pattern could reshape business operations. https://onemanops.com/blog/karpathy-loop-ai-running-its-own-experiments ### Vibe Coding: What It Actually Means That Anyone Can Build Software Now Why vibe coding matters now, what non-technical operators can build with it today, and where human judgment still matters. https://onemanops.com/blog/vibe-coding-anyone-can-build-software-now ### NoteSmith Case Study: $5K Revenue Week One What the NoteSmith launch model gets right: one pain point, immediate value, gated continued access, and distribution through an existing audience. https://onemanops.com/blog/notesmith-case-study-5k-revenue-week-one --- ## Contact Book a discovery call: https://onemanops.com/contact