Blog
Insights & perspectives
Thinking on AI agents, the agent economy, trust by design, and what comes next. 218 articles across 19 topics.
AI Agent Safety Principles: Building Agents That Behave Reliably
AI agent safety principles are the foundational design rules that ensure agents behave reliably, stay within their authorized scope, and remain correctable when something goes wrong — the non-negotiable architecture for any agent operating in consequential domains.
2026-06-15
Human Oversight of AI Agents: Why Control Must Remain with People
Human oversight of AI agents means people retain meaningful, exercisable control over what agents do and can do — through authorization structures that limit scope, monitoring systems that surface deviations, and intervention capabilities that work in practice rather than just in theory.
2026-06-15
AI Agent Alignment: Matching Agent Behavior to Human Intentions
AI agent alignment is the challenge of ensuring that an agent's behavior consistently matches the intentions of the humans who deploy it — not just in situations the agent was explicitly trained for, but across the full range of situations it will actually encounter in operation.
2026-06-15
Agent Authorization and Consent: Who Decides What an Agent Can Do
Agent authorization defines who has the legal and operational authority to grant an AI agent permission to take specific actions — and consent defines what the people affected by those actions have agreed to — together forming the governance framework that makes agent autonomy safe and legitimate.
2026-06-15
AI Agent Transparency: Why Agents Must Be Explainable
AI agent transparency means agents can explain their decisions in terms their supervisors and affected parties can evaluate, disclose their identity clearly, and make their reasoning auditable — creating the accountability infrastructure that makes agent autonomy safe, governable, and trusted.
2026-06-15
Responsible AI Agent Deployment: A Framework for Operators
Responsible AI agent deployment means deploying agents with proper pre-deployment safety assessment, clear authorization structures, ongoing monitoring, appropriate disclosure to affected parties, and defined accountability for what the agent does — a practical framework that applies to operators at every scale.
2026-06-15
AI Agent Harm Prevention: Detecting and Stopping Misuse
AI agent harm prevention is the set of technical and operational systems that detect misuse, limit harm from errors, stop bad actors who attempt to weaponize agent capabilities, and ensure that agents cannot be used against the interests of the people they interact with.
2026-06-15
Ethics of Autonomous Agents: Where the Hard Questions Are
The ethics of autonomous AI agents raises foundational questions about responsibility distribution, consent in multi-party interactions, fairness in automated decisions, the appropriate limits of machine autonomy, and the obligations agents and their owners have to the people and communities they affect.
2026-06-15
AI Agent Governance Frameworks: From EU AI Act to Platform Rules
AI agent governance frameworks range from the EU AI Act and national regulations to platform-specific rules and industry standards — together defining what agents are permitted to do, what disclosures they must make, what risks require human oversight, and what accountability structures operators must maintain.
2026-06-15
Human-Agent Trust Design: Building Systems People Rely On
Human-agent trust design is the practice of deliberately creating the signals, structures, and experiences that cause people to calibrate appropriate trust in AI agents — not too much, not too little, but accurately matched to what the agent has actually demonstrated it can reliably do.
2026-06-15
What Are Multi-Agent Systems? How AI Agents Work Together
Multi-agent systems are networks of independent AI agents that communicate, coordinate, and collaborate to accomplish tasks that no single agent could complete alone — combining specialized capabilities, parallel execution, and collective reasoning to solve problems at a scale and complexity that individual agents cannot reach.
2026-06-15
Agent-to-Agent Communication: How AI Agents Talk to Each Other
Agent-to-agent communication uses structured protocols, shared context formats, and defined message types that enable AI agents to coordinate work, transfer intermediate results, request capabilities, and resolve conflicts without requiring human mediation at each exchange.
2026-06-15
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