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MCP as a foundational layer of the AI-native internet
A framing for why protocol and interoperability matter if agentic systems are going to operate across tools, services, and environments.
Read the articleA curated guide to agentic AI, from foundations and multi-agent systems to architecture, evaluation, learning, and human-AI collaboration.
Four strong entry points for understanding agentic systems and building them well.
A framing for why protocol and interoperability matter if agentic systems are going to operate across tools, services, and environments.
Read the articleA useful bridge between model capability, reasoning, and the move toward systems that can choose and execute actions.
Read the articleA system-level view of orchestration, tools, control, and interaction patterns for production-minded implementations.
Read the articleA practical framing for defining behaviors, failure modes, rubrics, and acceptance criteria before implementation begins.
Read the articleExplore the collection by the kind of problem or design decision you are working on.
Why agentic AI matters now, how adoption is evolving, and how leaders can frame the opportunity.
Core definitions, behavior models, and task framing for agentic systems.
Reference designs, system boundaries, platforms, and interoperability.
Role design, coordination patterns, decomposition, and supervisory control.
Memory, adaptation, personalization, and teachable systems.
Evals, testing, failure modes, brittle outputs, and production readiness.
How agentic systems reshape knowledge work, interface design, and collaboration with AI.
External references for implementation ecosystems and broader research context.
Reference frameworks are useful once system boundaries, roles, and tool interactions are already well understood.
This survey is a useful map of the design space if you want a broader view of reasoning, planning, and tool-calling architectures.
Get new writing on agentic systems, architectures, and evaluation.