How We Got to 446 Tests: Our Approach to AI Safety Quality
SafeClaw has 446 tests across 24 files. Here is how we built a testing culture around AI agent safety and why we treat test coverage as a feature.
AI Agent Safety Is Not Optional: A Call to Action
The industry is deploying AI agents with file, shell, and network access faster than it is deploying safety controls. That has to change.
Understanding Your AI Agent's Behavior with SafeClaw Analytics
SafeClaw's analytics dashboard reveals patterns in your AI agent's behavior, helping you tune policies and understand risk.
How We Built Budget Controls for AI Agent Spending
SafeClaw's budget control system lets you set hard spending limits on AI agent API calls, token usage, and compute costs.
Building Trust in AI Agents: It Starts with Transparency
Trust in AI agents requires transparency — into what they do, why they do it, and what happens when they make mistakes.
How We Built SafeClaw's Action Classifier
A deep dive into how SafeClaw classifies AI agent actions into allow, deny, and escalate categories using deterministic pattern matching.
Open Source Contributions: How the Community Improves SafeClaw
How community contributors have shaped SafeClaw through bug reports, feature suggestions, policy templates, and code contributions.
Compliance Without Friction: Making Safety Easy
How SafeClaw makes AI agent safety compliance effortless — automated auditing, policy-as-code, and frictionless reporting.
Container Mode: Running AI Agents in Complete Isolation
SafeClaw's container mode runs AI agents inside Docker or Podman with read-only root, resource limits, and workspace isolation. Here is how it works.
Exporting AI Agent Data: CSV and JSON Reports
SafeClaw's export system lets you generate CSV and JSON reports of AI agent activity for compliance, billing, and analysis.
From Internal Tool to Open Source: SafeClaw's Journey
SafeClaw started as internal safety tooling at Authensor. Here is how an internal need became an open-source product used by the community.
Dark Mode for Developer Tools: Our Design Decisions
The design thinking behind SafeClaw's dark mode: contrast ratios, status colors, and making safety information readable at 2 AM.
Building SafeClaw's Dashboard: A PWA for Agent Safety
How we designed SafeClaw's browser dashboard with swipe approvals, real-time SSE streaming, and a mobile-first PWA that works offline.
Our Philosophy: Why Deny-by-Default Is the Only Safe Choice
Allow-by-default is dangerous when the principal is a language model. We explain why SafeClaw denies everything that is not explicitly permitted.
Great Developer Experience in Safety Tools
Why developer experience matters for safety tools, and the design principles behind SafeClaw's UX decisions.
SafeClaw Doctor: Self-Diagnosing Configuration Issues
SafeClaw Doctor automatically detects misconfigurations, policy conflicts, and setup issues before they cause problems.
The Future of AI in Developer Workplaces
Our vision for how AI agents will reshape developer workflows, and why safety infrastructure is essential for that future.
Getting Started with SafeClaw: From Zero to Safe in 5 Minutes
A step-by-step guide to installing SafeClaw, configuring your AI provider, running your first gated task, and understanding the safety dashboard.
Designing Tamper-Proof Audit Logs for AI Agents
How we designed SafeClaw's SHA-256 hash chain audit ledger to create tamper-proof, verifiable records of every AI agent action.
Introducing SafeClaw 1.0 Beta: Safe-by-Default AI Agents
SafeClaw 1.0 Beta is here. Action-level gating for AI agents with deny-by-default policies, tamper-proof audit logs, and a mobile-first dashboard.
What AI Agent Safety Can Learn from Traditional InfoSec
Decades of information security research have produced principles that apply directly to AI agent safety. Here's what we borrowed.
Why We Chose the MIT License for SafeClaw
The reasoning behind our decision to release SafeClaw under the MIT license — maximum adoption, minimum friction, full trust.
Multi-Profile Support: Different Policies for Different Projects
SafeClaw's multi-profile system lets you define different safety policies for different projects, environments, and risk levels.
Designing Fail-Closed Offline Mode for Agent Safety
How SafeClaw maintains full safety enforcement when offline, ensuring AI agents can't bypass guardrails by losing connectivity.
Why SafeClaw Is Open Source and Always Will Be
SafeClaw is MIT licensed and fully open source. We explain why transparency is non-negotiable for AI agent safety tooling.
Our Security Model: Why Open Source Is Safer
Why we believe open source security tools are inherently more trustworthy than proprietary alternatives, and how our model works.
How We Designed SafeClaw's Policy Engine
A technical deep-dive into SafeClaw's policy engine: first-match-wins evaluation, regex safety, time-based rules, versioning, and simulation mode.
Applying Principle of Least Privilege to AI Agents
How SafeClaw implements the security principle of least privilege for AI agents, granting only the permissions each task requires.
SafeClaw as a PWA: Install It Like a Native App
SafeClaw's Progressive Web App brings the full dashboard experience to your home screen with offline support and push notifications.
Designing a Rate Limiter for AI Agent Safety
How we designed SafeClaw's rate limiter to prevent runaway AI agents from executing too many actions too quickly.
How SafeClaw Detects Risk Signals in Tool Calls
Learn how SafeClaw analyzes AI agent tool calls for risk signals like destructive operations, privilege escalation, and data exfiltration.
SafeClaw Architecture: How Action Gating Works Under the Hood
A technical deep-dive into SafeClaw's gateway, classifier, policy engine, and audit chain. How we intercept and evaluate every AI agent action.
SafeClaw Roadmap: What's Coming in 2026
Our 2026 roadmap for SafeClaw: team policies, RBAC, API integration mode, plugin system, and more. Here is what we are building next.
Safety as an Enabler: How Constraints Make AI Agents More Useful
Counter-intuitive insight: adding safety constraints to AI agents makes teams more willing to use them, increasing overall productivity.
Building a Cron Scheduler for AI Agent Tasks
How we built SafeClaw's task scheduler to safely manage recurring AI agent operations with time-based policies and guardrails.
Inside SafeClaw's Secrets Redaction Engine
How SafeClaw automatically detects and redacts secrets, API keys, and credentials before AI agents can exfiltrate them.
Our Security Disclosure Process: How to Report Vulnerabilities
How to responsibly report security vulnerabilities in SafeClaw, and how we handle disclosures from initial report to public fix.
Session Management in SafeClaw: Tracking Agent History
How SafeClaw's session management tracks every AI agent action, building a complete audit trail for review and debugging.
The SafeClaw Setup Wizard: Zero to Safe in 60 Seconds
How SafeClaw's interactive setup wizard gets you from installation to full AI agent protection in under a minute.
Real-Time Agent Updates with Server-Sent Events
How we use Server-Sent Events to stream real-time AI agent activity, decisions, and alerts to the SafeClaw dashboard.
Swipe to Approve: Mobile-First Agent Safety
SafeClaw's swipe-to-approve interface lets you review and approve AI agent actions from your phone with a single gesture.
Our Testing Philosophy: Why 446 Tests Isn't Enough
Our approach to testing safety-critical software: why test count is vanity, what coverage actually means, and how we test SafeClaw.
Thank You to Our Early Adopters: What We've Learned
A thank you to SafeClaw's early adopters and the lessons their feedback has taught us about building AI agent safety tools.
Time-Based Policies: Different Rules for Different Hours
SafeClaw's time-based policies let you apply stricter rules outside business hours when fewer humans are available to supervise.
Trust But Verify: Our Approach to AI Agent Autonomy
How SafeClaw balances AI agent autonomy with verification — giving agents freedom to work while maintaining human oversight.
SafeClaw Webhook Integrations: Slack, Discord, and More
Connect SafeClaw to Slack, Discord, email, and custom endpoints to receive real-time notifications about AI agent activity.
Why Action Gating Is the Most Important Layer of AI Safety
Action gating — intercepting AI agent actions before execution — is the single most effective safety layer. Here's why.
Why We Built SafeClaw: The Problem Nobody Was Solving
AI agents can delete files, run shell commands, and make network requests. Nobody was gating those actions. We built SafeClaw to fix that.
How SafeClaw Enforces Workspace Boundaries
A technical look at how SafeClaw confines AI agents to their designated workspace, preventing unauthorized access to the broader filesystem.
Why SafeClaw Has Zero Third-Party Dependencies
SafeClaw ships with zero third-party runtime dependencies. Fewer deps mean a smaller attack surface, faster installs, and code you can actually audit.