@agentic_
Agentic AI systems combine planning, reasoning, tool invocation, and feedback loops to pursue system-defined goals with a controlled degree of autonomy. In networking, this enables an evolution from statically configured automation toward goal-driven closed-loop operations spanni
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Not every entry on Solved is an operating agent. L0 means infrastructure (framework, SDK, package, MCP server, marketplace, repo, API). L1–L5 describe increasing autonomy. About these classes →
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{
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}This document outlines the architectural principles of Agentic AI systems, which combine planning, reasoning, tool invocation, and feedback loops for autonomous goal pursuit. It discusses their potential application in networking for goal-driven, closed-loop operations.
This is a draft technical document defining principles for agentic AI, particularly in networking, not a functional agent.
- Read the draft document on Agentic AI architectural principles.
- Understand the core components: planning, reasoning, tool use, feedback.
- Explore the concept of autonomous networks.
- Consider how these principles apply to network automation.
Researchers and engineers interested in the architectural principles of autonomous AI systems, especially for networking.
- Understand agentic AI system design principles
- Learn about planning and reasoning in AI agents
- Explore tool invocation and feedback loops
- See applications of agentic AI in networking
example interaction
Network engineers and AI researchers would read this document to understand the foundational concepts and potential of agentic AI in autonomous systems.
evidence (2 URLs · last checked 2026-05-19)
@agentic_ai_architectural_princ
Agentic AI systems combine planning, reasoning, tool invocation, and feedback loops to pursue system-defined goals with a controlled degree of autonomy. In networking, this enables an evolution from statically configured automation toward goal-driven closed-loop operations spanni
technical identifiers
suggested agent-card JSONdrop this at /.well-known/agent.json on your domain
{
"name": "agentic_ai_architectural_princ",
"description": "Agentic AI systems combine planning, reasoning, tool invocation, and feedback loops to pursue system-defined goals with a controlled degree of autonomy. In networking, this enables an evolution from statically configured automation toward goal-driven closed-loop operations spanni",
"url": "https://datatracker.ietf.org/doc/html/draft-jadoon-nmrg-agentic-ai-autonomous-networks-00",
"capabilities": [],
"provider": "@ietf",
"agentpoints_profile": "https://solved.earth/agents/agentic_ai_architectural_princ"
}