Agent OS
A Safety-First Kernel for Autonomous AI Agents. POSIX-inspired primitives with 0% policy violation guarantee. The operating system layer that treats LLMs as raw compute and provides deterministic governance.
Key Features:
- 4-Layer Architecture: Primitives, Infrastructure, Control Plane, Intelligence
- IDE Extensions: VS Code, Copilot, Cursor, JetBrains plugins
- MCP Server: Works with Claude Desktop, Copilot, and Cursor
🎯 Results: 0% policy violations vs 26.67% for prompt-based safety • 734 tests passing
Python
PyPI
MCP
VS Code
AgentMesh
The Secure Nervous System for Cloud-Native Agent Ecosystems. Identity, Trust, Reward, and Governance for the governed agent mesh. The trust layer that A2A and MCP protocols are missing.
Key Features:
- Identity & Zero-Trust: Agent identity and ephemeral credentials
- Trust & Protocol Bridge: A2A, MCP support
- Governance & Compliance: EU AI Act, SOC 2, HIPAA, GDPR
🔒 Trust: Ephemeral credentials • Per-agent trust scores • Audit logs
Python
A2A
MCP
Trust
Agent SRE
Reliability engineering framework for AI agent systems. SLO engine, chaos engineering, replay debugging, progressive delivery, cost guard, and incident management—purpose-built for autonomous agents.
Key Features:
- SLO Engine: Reliability targets for task success rate, cost per task, hallucination rate
- Replay Engine: Time-travel debugging with deterministic replay and trace diffing
- Chaos Engineering: 9 pre-built fault injection templates with resilience scoring
- Cost Guard: Per-task limits, anomaly detection, auto-throttle
- Progressive Delivery: Shadow mode, canary rollouts, auto-rollback
📊 Scale: 878 tests • 20,000+ lines • 20+ tool integrations (OTEL, Datadog, Prometheus)
Python
OpenTelemetry
Prometheus
Kubernetes
Agentic Architecture
A comprehensive guide to modern agentic system design. Documents revolutionary architectural patterns for building production-grade AI agent systems based on real-world experience.
Core Concepts:
- The Inference Trap: Why "thinking" is technical debt
- The Guardrail Router: Intelligent request routing
- Compute-to-Lookup Ratio: 90/10 rule for performance
- Multidimensional Knowledge Graphs: Semantic firewalls
- The Headless Agent: Silent swarms for coordination
- Recursive Ontologies: Self-updating knowledge systems
"If your agent is 'thinking' for every request, you haven't built an agent; you've built a philosophy major. In production, we need engineers, not philosophers."
Architecture
Documentation
Best Practices
Agent SRE
Reliability Engineering for AI Agent Systems. SLOs, error budgets, chaos testing, deterministic replay, and progressive delivery for multi-agent production environments. Part of the Agent OS + AgentMesh governance stack.
Core Capabilities:
- SLO Engine: Define reliability targets (task success, cost, latency, hallucination rate)
- Replay Engine: Deterministic time-travel debugging for agent decisions
- Progressive Delivery: Shadow mode, canary rollouts, auto-rollback
- Chaos Testing: Fault injection, resilience scoring, 9 experiment templates
- Cost Guard: Budgets, anomaly detection, auto-throttle, kill switch
- Incident Manager: SLO breach detection, circuit breaker, postmortems
🎯 Production Ready: 20,000+ lines • 878 tests • Full OpenTelemetry integration
Python
SRE
OpenTelemetry
Chaos
AgentMesh Integrations
Community integrations for AgentMesh — platform plugins and trust providers. Dify, LangChain, LlamaIndex, Agent Lightning, Nostr Web of Trust, and Moltbook integrations for identity, trust, and governance.
Available Integrations:
- LangChain: Ed25519 identity, trust-gated tools, delegation chains
- LlamaIndex: Trust-verified workers, identity-aware query engines
- Dify Plugin: Packaged .difypkg with peer verification and trust scoring
- Agent Lightning: Agent-OS governance adapters, reward shaping
- Moltbook: AgentMesh governance skill for agent registry
- Nostr WoT: Decentralized trust scoring integration
Python
Integrations
Trust
Identity
Scale by Subtraction
My core methodology for building reliable AI systems. Focus on removing complexity rather than adding features. Via Negativa applied to software architecture and AI safety.
Key Principles:
- Control Planes over Prompts: Deterministic enforcement
- Graphs over Context: Prevent hallucinations structurally
- Silent Swarms over Chat: Structured data communication
- Memory Hygiene: Agents that know how to forget
Methodology
AI Safety
Architecture
Technical Articles
Deep dives into agentic systems, architectural patterns, and the philosophy of building AI that works. Published on Medium, Dev.to, and LinkedIn with practical, battle-tested approaches.
Featured Series:
- The Accumulation Paradox: Why agents degrade over time
- The Mute Agent: Shut up and listen to the graph
- The Agentic Architect: Building AI governance systems
Medium
Dev.to
LinkedIn
Awesome AI Governance
Curated list of tools, frameworks, and resources integrated with the Agent OS + AgentMesh + Agent SRE governance stack. The definitive resource for AI agent governance.
Curated List
Ecosystem
Resources
AgentMesh Integrations
Platform plugins and trust providers for AgentMesh. Includes the Dify Trust Layer plugin (merged into Dify Marketplace) and framework-specific governance adapters.
Dify
Plugins
Trust Providers
🌍 Ecosystem Integrations
Our governance layer is integrated into major AI frameworks, with 3 contributions merged and 10+ proposals under review across 230K+ combined GitHub stars.
✅ Merged Contributions
🟢 Dify (65K ⭐)
AgentMesh Trust Layer plugin merged into the Dify Marketplace. Real-time trust scoring for Dify agent workflows.
PR #2060 →
🟢 LlamaIndex (47K ⭐)
AgentMesh Trust Layer integration for LlamaIndex agent pipelines. Trust-verified tool calls and query routing.
PR #20644 →
🟢 Microsoft Agent-Lightning (15K ⭐)
Agent-OS governance integration for reinforcement learning training pipelines.
PR #478 →
🔄 Open Proposals (10 Frameworks)
Governance integration proposals filed across major frameworks:
CrewAI (44K ⭐)
AutoGen (54K ⭐)
LangGraph (24K ⭐)
Google ADK (18K ⭐)
Semantic Kernel (27K ⭐)
smolagents (25K ⭐)
PydanticAI (15K ⭐)
OpenAI Swarm (21K ⭐)
AGENT.md Spec
Oracle Agent Spec