Blog
Insights & perspectives
Thinking on AI agents, the agent economy, trust by design, and what comes next. 218 articles across 19 topics.
AI Agent Tool Use: Giving Agents Access to the Real World
AI agent tool use connects agents to external systems — search engines, databases, APIs, code execution environments, file systems, and communication services — enabling agents to take real-world actions beyond generating text and to access information that post-dates or falls outside their training.
2026-06-15
AI Agent Testing and Evaluation: How to Measure What Matters
AI agent testing and evaluation requires measuring output quality, task completion rate, error handling behavior, safety and scope adherence, adversarial robustness, and cost efficiency — across a diverse set of test cases that covers the full distribution of inputs the agent will encounter in production, not just the easy ones.
2026-06-15
Deploying AI Agents in Production: What Changes at Scale
Deploying AI agents in production introduces challenges that prototype environments do not reveal — latency management under concurrent load, cost control at scale, error handling that degrades gracefully, monitoring systems that surface real behavioral issues, and human oversight structures that work under operational pressure.
2026-06-15
AI Agent Observability: Monitoring Agents You Cannot Fully Predict
AI agent observability provides the visibility into agent reasoning steps, tool calls, output quality, performance characteristics, and behavioral patterns that operators need to detect problems, diagnose failures, and maintain meaningful oversight of systems whose behavior cannot be fully predicted in advance.
2026-06-15
AI Agent Cost Optimization: Running Agents Efficiently
AI agent cost optimization reduces inference costs, tool call overhead, and infrastructure spend through model routing, context compression, prompt caching, batch processing, and agent capability right-sizing — maintaining output quality while making agent operations economically sustainable at scale.
2026-06-15
AI Agent Iteration and Improvement: Getting Better Over Time
AI agent improvement requires systematic analysis of production failures, structured prompt and architecture iteration driven by specific deficiency diagnoses, test set expansion from production edge cases, and deliberate capability development — not random experimentation or reactive patching of whatever broke most recently.
2026-06-15
The Future of the Agent Economy: What Comes Next
The future of the agent economy will be defined by agents transacting autonomously, holding persistent verified identities, participating in organized labor markets, generating measurable economic output, and becoming core infrastructure for industries that today rely entirely on human operators — a shift that is already underway.
2026-06-15
AI Agent Labor Markets: How Work Is Being Reorganized
AI agent labor markets are emerging systems where agents are selected, contracted, and compensated for specific tasks based on verified capability, performance history, and specialized domain competence — creating a new kind of market for work that operates at speeds and volumes no human labor market could support.
2026-06-15
Autonomous Agent Commerce: When Agents Become Buyers
Autonomous agent commerce occurs when AI agents conduct purchases, negotiate service contracts, and settle transactions on behalf of their principals — creating market dynamics, trust infrastructure requirements, and legal questions that do not arise when all commerce participants are human.
2026-06-15
Agent-Native Businesses: Companies Built to Operate with AI Agents
Agent-native businesses are organizations designed from the start to operate with AI agents as core participants — not as productivity tools layered onto existing human workflows, but as primary operators of key business processes — enabling a fundamentally different cost structure, operating speed, and scalability ceiling than conventional business models.
2026-06-15
AI Agents and Economic Output: Quantifying the Productivity Shift
AI agents are contributing to measurable economic output through productivity gains in knowledge-intensive work, the automation of tasks previously requiring expensive human expertise, and the creation of entirely new economic activities — the agent economy's aggregate contribution to GDP is early but growing, with specific industries already seeing quantifiable shifts.
2026-06-15
Regulating the Agent Economy: What Frameworks Are Emerging
The regulatory landscape for the agent economy is taking shape through AI-specific legislation, consumer protection framework updates, financial regulation adaptation, and emerging liability frameworks for autonomous agent actions — a patchwork that is developing faster in some jurisdictions than others but will ultimately determine how the agent economy is permitted to operate globally.
2026-06-15
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