Deep dives on AI agent governance, MCP security, compliance, and enterprise agentic architecture.
AI agents are software systems that reason, plan, and act on your behalf. Learn what AI agents are, how they work, the main types, real examples, and how to run them safely.
AI governance is the framework of policies, controls, and accountability for using AI safely and in compliance. Learn the pillars, NIST/ISO 42001/EU AI Act frameworks, and how to govern autonomous AI agents.
Autonomous AI agents plan and act toward goals on their own. Learn how they work, the levels of autonomy, real examples, risks, and how to govern them safely.
What an AI agent platform is, the capabilities and architecture that define one, build vs buy, an evaluation checklist, and why identity, access, and governance decide which agents reach production.
The NIST AI Risk Management Framework (AI RMF 1.0) is voluntary U.S. guidance for managing AI risk. Learn its four functions (GOVERN, MAP, MEASURE, MANAGE), the Generative AI Profile, how it compares to ISO 42001 and the EU AI Act, and how to adopt it.
AI risk management is the continuous practice of identifying, assessing, and controlling the risks of AI systems and agents. Learn the risk categories, frameworks (NIST AI RMF, ISO 42001), program lifecycle, and best practices.
AI observability is how teams see, evaluate, and govern LLM and AI agent behavior in production. Learn the core pillars, key metrics, challenges, and how to choose an approach.
What an enterprise AI platform is, its reference architecture, how to evaluate build vs buy, and how to secure and govern autonomous AI agents.