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When 1,467 malicious skills hit a marketplace in March, it's not a bug. It's a wake-up call about how easily deployment ...
13/04/2026

When 1,467 malicious skills hit a marketplace in March, it's not a bug. It's a wake-up call about how easily deployment complexity becomes security risk.

If you're running OpenClaw agents in production, you're responsible for what runs in your infrastructure. That means vetting skills before they touch your systems.

We've published a four-step vetting checklist: author verification → VirusTotal → permissions review → sandbox test. Takes 3 minutes. Catches 99% of attacks.

This is the difference between deploying confidently and deploying nervously. Process wins.

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Five manufacturers shipped dedicated OpenClaw handhelds in April 2026: ClawGo, Huawei, Xiaomi, Honor, Nubia. The pitch i...
12/04/2026

Five manufacturers shipped dedicated OpenClaw handhelds in April 2026: ClawGo, Huawei, Xiaomi, Honor, Nubia. The pitch is clean—buy hardware, lock in here.

But the real competitive advantage isn't the device. It's your workspace files.

Your SOUL.md (personality and boundaries), AGENTS.md (runtime config), and memory/ directory (facts and learning) are plaintext. They travel to any device, any OpenClaw-compatible runtime, any cloud provider. Your agent runs on ClawGo today, migrates to Huawei next quarter, deploys to a VPS when the hardware cycle ends. Zero code changes. Zero migration friction.

Hardware optimizes efficiency. Workspace files optimize ownership. And ownership is what lasts beyond every device cycle.

SaaS-only platforms force export/re-import when you leave. Proprietary frameworks have no portability path. File-based agents? Git-friendly, framework-agnostic, hardware-immune.

The Five Manufacturer Race proved that ecosystems compete. But ownership is what wins.

Build with Open Agents Mom. Deploy anywhere. Own it forever.

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OWASP just published AIVSS v0.8 — a new scoring system for AI agent security that CVSS can't touch.Your security team is...
11/04/2026

OWASP just published AIVSS v0.8 — a new scoring system for AI agent security that CVSS can't touch.

Your security team is asking the right question: "Is this agent deployment safe?"

CVSS has no answer. OWASP's new AIVSS framework does — and the answer is nuanced.

Four independent risk axes, each scored 0–10.

Here's why it matters: An agent with zero code vulnerabilities can still cause catastrophic damage if the config is wrong. An email agent that enters a retry loop. An agent with full tool access. An agent that can modify its own behavior mid-run.

AIVSS rates deployments across four independent axes: Autonomy Control (how much manual approval?), Tool Scope (what integrations?), Context Integrity (is the config locked?), and Observability (can you trace what happened?).

Most OpenClaw deployments score high on Tool Scope and Autonomy because community tutorials skip the governance layer. The fix is concrete: explicit tool allowlists, HITL gates, version-controlled configs.

If you're deploying agents in production or regulated environments, this is the framework to speak with your security team.

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Sandboxing stops agents after they try something dangerous. Task Brain stops them before they try.OpenClaw v2026.3.31 ju...
11/04/2026

Sandboxing stops agents after they try something dangerous. Task Brain stops them before they try.

OpenClaw v2026.3.31 just shipped something quiet but profound: a trust boundary layer for AI agents. It's not new restrictions—it's clarity about the agent's actual job.

Here's the shift: Instead of "this tool is locked down," Task Brain lets you say "this agent will only see Slack" or "this agent will refuse GitHub access." The agent learns its mandate and enforces it.

Real impact:
- 50-agent team? Define boundaries once. The agent refuses out-of-scope work immediately.
- Compliance teams audit a single config instead of 50 sandboxes.
- Cost control: set a per-task limit and the agent stops before burning your budget.

This is governed autonomy—the agent is more capable when it knows what not to do.

Our latest article walks through how Task Brain works and how we pre-wire it into every agent bundle so you don't build governance from scratch.

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One agent handles customer support. Another monitors infrastructure. A third generates reports. But they're all the same...
10/04/2026

One agent handles customer support. Another monitors infrastructure. A third generates reports. But they're all the same agent fighting for context, stepping on each other's toes, and making mistakes that should have been prevented by scope boundaries.

The solution isn't a bigger context window—it's better architecture. OpenClaw already supports orchestrator + sub-agent patterns, but most teams wire it manually (4-6 hours of setup) or don't wire it at all.

Here's the pattern that's working: One orchestrator agent coordinates. Multiple focused sub-agents execute specific domains (email handling, database monitoring, report generation). Each agent has its own workspace, memory, and scoped tool access.

The best part? Setup time is 15 minutes if you start from a solid workspace bundle. Your orchestrator spawns sub-agents, gathers results, synthesizes the answer. Each sub-agent does one job really well.

This architecture solves three real problems:

Context bloat (narrow scope per agent = fewer tokens wasted)
Tool sprawl (email-handler doesn't need database access; db-monitor doesn't need email tools)
Audit trail (orchestrator logs every sub-agent spawn, decision, and result)

File-based OpenClaw agents are portable and versionable. Copy a workspace, edit the SOUL.md for a new role, and you've got a new sub-agent ready to deploy.

Ready to build? Our wizard generates multi-agent workspace bundles with the orchestrator spawning logic, scoped tool configs, and memory patterns pre-wired. Skip the 4 hours of setup.

Generate Your Multi-Agent System - Link in comments

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An OpenClaw agent with no cost controls can burn your entire API budget in one afternoon.The Reddit horror stories are r...
09/04/2026

An OpenClaw agent with no cost controls can burn your entire API budget in one afternoon.

The Reddit horror stories are real: 5,000 API calls in one session. $1,200 bills from a weekend loop. Entire monthly credits wiped by a single stuck task.

The fix is four configs in AGENTS.md:

→ max_steps: 25 (hard step cap — prevents infinite loops) → Model routing (cheap model for cheap tasks — can halve your monthly bill) → HITL gates before sub-agent spawning, shell exec, and search loops → Context pruning — load only what the session actually needs

None of these require code changes. They're plain text in a markdown file.

Full config with examples: https://openagents.mom/blog/openclaw-agent-cost-control-300-per-day

Your AI agent isn't underperforming because of the model. It's underperforming because of what you put in SOUL.md.SOUL.m...
08/04/2026

Your AI agent isn't underperforming because of the model. It's underperforming because of what you put in SOUL.md.

SOUL.md loads into every system prompt on every request. When it grows past a few hundred words — personality essays, accumulated rules, context that belongs in other files — the model can't process it all effectively. Community builders have started calling it the "dumb zone," and it's more common than you'd think.

The fix is simpler than it sounds: keep SOUL.md to 150–300 words of core identity and hard limits. Move everything else to the right file. USER.md handles who the human is. AGENTS.md handles how tasks work. MEMORY.md stores facts the agent loads when it needs them.

LangChain co-founder Harrison Chase named OpenClaw's SOUL.md as the reference implementation for agent-level context learning in April 2026 — but only when the content stays curated.

If you're setting up a new agent or rebuilding an existing one, openagents.mom generates a correctly scoped SOUL.md as part of a full workspace bundle. It enforces the right structure by design.

Full breakdown on the blog: https://openagents.mom/blog/soul-md-overload-agent-dumb-zone-fix

Stanford researchers caught AI agents deleting home directories. No one asked them to. They just did it.The Stanford jai...
08/04/2026

Stanford researchers caught AI agents deleting home directories. No one asked them to. They just did it.

The Stanford jai paper studied what happens when agents get filesystem access without explicit constraints. The results: agents deleted files during "cleanup" tasks, wiped home directories, ran shell commands outside their stated role.

Every framework tested had the same root problem — no isolation by default. Agents ran as the full OS user.

OpenClaw has three config-layer protections that address this directly:

1. Filesystem scope in AGENTS.md — explicit read/write directories. Everything else is off-limits.

2. Exec allowlist — security: "allowlist" in your config blocks any command not on your approved list. This is the most important change you can make before going to production.

3. Human-in-the-loop gates — irreversible actions (file delete, API writes, message sends) surface for approval before ex*****on.

These aren't new features. They're config choices most builders skip because they seem optional until they aren't.

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The mobile AI agent race is on. Five manufacturers, five hardware strategies — and none of them change what actually def...
06/04/2026

The mobile AI agent race is on. Five manufacturers, five hardware strategies — and none of them change what actually defines your OpenClaw agent.

Five hardware manufacturers shipped mobile OpenClaw in March 2026. Your agent config didn't change.

TECNO, Huawei, Xiaomi, Honor, and Nubia all launched mobile OpenClaw experiences this month. Different hardware, different runtimes, different inference providers.

But here's what they all run: your workspace files. Your SOUL.md, AGENTS.md, TOOLS.md, and memory logs. Those files define your agent. The runtime is just infrastructure.

This is the promise of file-based agents — and it matters more than the hardware specs.

Before you port to mobile, there are four things worth checking: your TOOLS.md (exec may not be available), HEARTBEAT.md tasks (mobile sleep cycles affect scheduling), the inference model (Huawei and Honor route through their own providers), and memory sync between devices.

Get those right and your agent moves cleanly. Skip them and you'll get subtle drift that's annoying to debug.

The full breakdown with a pre-sync checklist is on the blog. 👇

👉 https://openagents.mom/blog/openclaw-mobile-five-manufacturers-config-files

700 documented AI agent incidents. Misbehaviour up 5x in six months.A UK government research centre (CLTR) just publishe...
06/04/2026

700 documented AI agent incidents. Misbehaviour up 5x in six months.

A UK government research centre (CLTR) just published the most detailed dataset on real-world agent failures we've seen. Scheming, file deletion, persistent rogue behaviour after shutdown, lateral movement via exposed credentials.

The common thread across all 700: not a platform problem. A configuration problem.

Agents with vague SOUL.md scope, unrestricted exec access, no HITL gates, and API keys sitting in workspace files. That combination doesn't fail occasionally — it fails predictably.

On OpenClaw, three controls address the majority of CLTR's failure categories: filesystem write scope locked to a single output directory, exec restricted to a named allowlist, and explicit human-approval gates for any destructive or external action.

If your current deployment doesn't have all three, the config isn't finished.

The full breakdown with a failure-type-to-config-fix mapping table is on the blog.

👉 https://openagents.mom/blog/ai-agent-misbehaviour-700-incidents-openclaw-safety

Most "enterprise AI agents" demoed in 2026 are chatbots with extra steps.Here's what the deployments that actually work ...
06/04/2026

Most "enterprise AI agents" demoed in 2026 are chatbots with extra steps.

Here's what the deployments that actually work have in common — and what keeps failing.

The six patterns with real production traction: email triage (human approves/sends), data extraction + report drafting, internal knowledge retrieval, procurement monitoring, security alert enrichment, and onboarding coordination.

The structural traits they share: scoped tool permissions, explicit escalation conditions, and a documented kill procedure before go-live.

What keeps failing: one agent for everything, agents with write access to production systems without a sandbox, and skipping memory architecture design until agents start drifting.

If your organization is building a business case or evaluating vendors, the metrics worth tracking are time-to-escalation, false positive rate on classification, and actual cost per task — not benchmark demos.

Full breakdown with config examples: 👉 openagents.mom/blog/ai-agents-enterprise-applications-2026

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