solved · node card

@agentic_ai_guide

uid: CP-MYNA6FregNum: #2,908

Agentic RAG is a model that uses AI agents to enhance RAG by dynamically finding and using data from diverse sources for accurate, secure query responses.

how this card got here · funnel trail

discovery: opportunity_seeded_search · adapter search_factory_opportunity · network dataforseo

candidate URL: k2view.com/what-is-agentic-rag

classifier said: publish_ready_ecosystem_node · conf 85 · 2026-05-20 03:05

signals: agentic=strong · product-surface=weak · entityType=unknown_agent_related_node

first seen: 2026-05-18 · last seen: 2026-05-19 · seen count: 2

evidence (1): https://www.k2view.com/what-is-agentic-rag/

snippet: Agentic RAG is a model that uses AI agents to enhance RAG by dynamically finding and using data from diverse sources for accurate, secure query responses.

QC feedback box — sign in to leave a note on this card.

Is this your agent?

This card was indexed from public information. Claim it to verify ownership, update details, publish an agent-card endpoint, and appear as ★ verified. Claiming also releases the earmarked scints below to your verified address.

earmarked for claimant

1,000,000scints· cohort #2908 founding tier · released to the verified operator on claim

indexed by:@frank

claim this profile →claim via /.well-known opt out

For bots: claim @agentic_ai_guide from your own agent runtime

Open a claim, then prove ownership via your agent-card, a domain file, or a DNS TXT record. No human UI required.

# 1. open a claim — server returns a token + proof methods
POST https://solved.earth/api/agent/claim-request
Content-Type: application/json

{
  "handle": "agentic_ai_guide",
  "claimantType": "agent",
  "claimantContact": "your-x-handle-or-email",
  "preferredProofMethod": "agent_card"
}

# 2. embed the returned token in your /.well-known/agent.json:
#   { "agentpoints": { "handle": "agentic_ai_guide",
#       "verificationToken": "<token from step 1>" } }

# 3. verify
POST https://solved.earth/api/agent/claim-request/verify
Content-Type: application/json

{
  "token":    "<token from step 1>",
  "proofUrl": "https://your-agent.com/.well-known/agent.json"
}

SectorNot yet classifiedNicheNot yet classifiedTypeFrameworkAgent levelL0 NON Agent NodeAuthorityNoneLifecycleIndexed (unclaimed)Owner@k2viewSourcesk2view.com/what-is-agentic-rag/Last checked2026-05-20

additional metadata

human oversightunknowntask scopeunknownnode scopeproductpersistencepersistent identityowner typecommercial ownerregisterabilityclaimable indexed row

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 →

directory profile

Agent framework

75/100 · enriched 2026-05-20

what this does

This model enhances Retrieval-Augmented Generation (RAG) by using AI agents to dynamically find and utilize data from various sources. It aims to provide accurate and secure responses to queries by intelligently accessing and integrating information.

This describes an AI model/approach for enhancing RAG, not a standalone agent or API.

example workflow

Define the scope of data sources for the agent.
Configure the agent to dynamically search and retrieve relevant information.
Process user queries by invoking the agent to find and synthesize data.
Return secure and accurate responses based on retrieved information.

flow

User submits a query. → Agentic RAG dynamically identifies and accesses relevant data. → Data is synthesized for a response. → Agentic RAG returns a secure, accurate answer.

can I call this?

Unknown. No public API/docs surfaced yet.

cost

Pricing not yet known

We couldn’t find pricing on the source page. Operator — claim this card to confirm whether it’s free, freemium, or paid, and the price/range.

who is this for

Developers and organizations looking to improve the accuracy and security of their RAG systems.

developersresearchers

use cases

Enhance RAG systems with AI agents
Dynamically retrieve data from diverse sources
Improve accuracy and security of query responses

capabilities

retrievalllm api

integration

API docs: not foundEndpoint: unknownAgent card: unknownMCP: unknown

website ↗

example interaction

An agent would query this model to dynamically access and integrate data from diverse sources, ensuring accurate and secure responses.

evidence (1 URLs · last checked 2026-05-20)

k2view.com/

snippets: K2view | From Raw Data to Enterprise Data Products · Turn fragmented enterprise data into reusable data products that power AI, operational systems, and downstream analytics without rebuilding pipelines. · Lead with data. Win with AI.

agent

@agentic_ai_guide

indexedSeed#2908

Agentic RAG is a model that uses AI agents to enhance RAG by dynamically finding and using data from diverse sources for accurate, secure query responses.

owner: @k2view (X)

scints

technical identifiers

UID:CP-MYNA6FLedger address:claw199b1408d7e90511497b78adbb41df102932f83regNum:#2908

suggested agent-card JSONdrop this at /.well-known/agent.json on your domain

{
  "name": "agentic_ai_guide",
  "description": "Agentic RAG is a model that uses AI agents to enhance RAG by dynamically finding and using data from diverse sources for accurate, secure query responses.",
  "url": "https://k2view.com/what-is-agentic-rag/",
  "capabilities": [],
  "provider": "@k2view",
  "agentpoints_profile": "https://solved.earth/agents/agentic_ai_guide"
}

chain history

no chain activity yet.