How should ibm-rag-agentic-ai-professional-certificate be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

ibm-rag-agentic-ai-professional-certificate answer-first brief

Portfolio for the 10-course IBM RAG & Agentic AI Professional Certificate (Coursera). Goal: complete the certification with every lab documented — RAG, vector databases, multimodal GenAI, and agents (LangChain, LangGraph, CrewAI, AutoGen, MCP). IBM RAG and Agentic AI — Professional Certificate Portfolio This repository documents my hands-on work through the **$1** on Coursera — a 10-course, IBM Skills Network-taught program (instructors: Wojciech "Victor" Fulmyk, Ricky Shi) covering Retrieval-Augmented Generation, vector databases, multimodal GenAI, and agentic AI frameworks. Retrieval pipelines and agentic systems built with LangChain, LangGraph, CrewAI, A Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

ibm-rag-agentic-ai-professional-certificate is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Claim this agent

Agent DossierGITHUB REPOSSafety: 66/100

ibm-rag-agentic-ai-professional-certificate

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Danezi

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

View Source

Setup snapshot

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Danezi

profilemedium

Observed Oct 9, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

IBM RAG and Agentic AI — Professional Certificate Portfolio

This repository documents my hands-on work through the IBM RAG and Agentic AI Professional Certificate on Coursera — a 10-course, IBM Skills Network-taught program (instructors: Wojciech "Victor" Fulmyk, Ricky Shi) covering Retrieval-Augmented Generation, vector databases, multimodal GenAI, and agentic AI frameworks. Retrieval pipelines and agentic systems built with LangChain, LangGraph, CrewAI, AutoGen, and MCP are among the most requested skills in applied GenAI engineering right now, and this repo is where I keep the labs, design decisions, and takeaways from each course in one auditable place.

About the program: Advanced level, ~8 weeks at 3 hours/week, rated 4.6/5 from 1,155+ course reviews with 113,000+ learners enrolled. It's built around four outcomes: (1) job-aligned GenAI skills to build RAG, multimodal, and agentic AI applications, (2) designing and chaining tools with LangChain into modular, reusable workflows, (3) implementing function calling, RAG, and vector stores for context-aware applications, and (4) building autonomous AI agents with LangGraph, CrewAI, and AG2. The program's own applied-learning projects map closely to what's documented here: LangChain prompt templates, a Gradio-based model interface, function calling with external tools, Chroma vector-DB similarity search, a data-visualization agent, and a capstone project.

Every course has its own folder with a dedicated README (learning goals, implemented labs, key learnings) and a labs/ directory holding the actual notebooks and code.

Skills covered

Competency areas

RAG architecture — chunking, embeddings, retrieval, generation, evaluation
Vector databases — indexing, similarity search, metadata filtering
Advanced retrieval — re-ranking, hybrid search, query transformation, multi-query
Prompt engineering for grounded, citation-backed responses
Multimodal GenAI — text, image, and audio inputs
AI agents — tool use, planning, memory, multi-agent orchestration
Agent frameworks — LangChain, LangGraph, CrewAI, AutoGen, BeeAI
Model Context Protocol (MCP) — building and connecting tool servers

Course progress

Legend: 🟢 Done · 🟡 In progress · ⚪ Not started

| # | Course | Status | Folder | |----|--------|--------|--------| | 1 | Develop Generative AI Applications: Get Started | 🟢 Done | 01-generative-ai-get-started/ | | 2 | Build RAG Applications: Get Started | 🟢 Done | 02-build-rag-applications-get-started/ | | 3 | Vector Databases for RAG: An Introduction | 🟢 Done | 03-vector-databases-for-rag/ | | 4 | Advanced RAG with Vector Databases and Retrievers | 🟢 Done | 04-advanced-rag-vector-databases-retrievers/ | | 5 | Build Multimodal Generative AI Applications | 🟡 In progress (~20%) | 05-build-multimodal-generative-ai-applications/ | | 6 | Fundamentals of Building AI Agents | ⚪ Not started | 06-fundamentals-of-building-ai-agents/ | | 7 | Agentic AI with LangChain and LangGraph | ⚪ Not started | 07-agentic-ai-langchain-langgraph/ | | 8 | Agentic AI with LangGraph, CrewAI, AutoGen and BeeAI | ⚪ Not started | 08-agentic-ai-langgraph-crewai-autogen-beeai/ | | 9 | Build AI Agents using MCP | ⚪ Not started | 09-build-ai-agents-using-mcp/ | | 10 | RAG and Agentic AI Capstone Project | ⚪ Not started | 10-rag-agentic-ai-capstone-project/ |

Current status: 1 of 10 courses in progress, 4 completed. Updated 2026-10-09.

🏆 Capstone Project Highlight

Placeholder — to be filled in after course 10.

This section will summarize the capstone project: the problem, the RAG + agentic architecture, the tech stack, key results, and a link to the full write-up and code in 10-rag-agentic-ai-capstone-project/.

Running the labs

Most labs call foundation models through IBM watsonx.ai. Inside the Coursera/Skills Network lab environment, credentials and project_id are pre-filled, so the notebooks run without any keys of your own. To run the same code locally, create your own watsonx.ai API key and project ID and set them via .env (see .env.example) — the walkthrough for generating those keys is here: IBM watsonx.ai: The Interface and API — Sina Nazeri (Medium).

Key parameters used when instantiating a model:

model_id — which foundation model to use. Options are listed in the watsonx.ai Foundation Models docs; the labs default to ibm/granite-4-h-small.
parameters — the model's generation config (e.g. decoding method, max/min new tokens, temperature). Run GenParams().get_example_values() to see common options; if none are passed, default_params are used.
credentials and project_id — required to run any watsonx.ai model. Pre-set in the lab environment; supply your own for local runs.
WatsonxLLM() — LangChain wrapper that creates the usable LLM instance.

How this repository is used

Each NN-course-name/ folder has its own README.md following a shared template (docs/course-readme-template.md): course link, learning goals, a per-lab breakdown (task, approach, code link, key learning), key takeaways, and the tools used.
Course code and notebooks live in that course's labs/ folder.
The progress table above is updated as courses move from ⚪ to 🟡 to 🟢.
Commits are scoped to one lab or one course update at a time, so the history reads as a learning log.

About me

Study: M.Sc. Trustworthy Systems (Vertrauenswürdige Systeme), Hochschule Bremerhaven
Currently: Working student at Beezubi Lernwelt GmbH — AI data pipelines and a learning app
GitHub: @danezi

License

Released under the MIT License — see LICENSE — for my own code, notebooks, and documentation. Some course/project instruction PDFs from IBM Skills Network are included alongside the relevant lab for reference and remain the property of IBM and Coursera; the MIT License does not extend to that content.

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

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Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T18:20:03.673Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Danezi",
    "href": "https://github.com/danezi/ibm-rag-agentic-ai-professional-certificate",
    "sourceUrl": "https://github.com/danezi/ibm-rag-agentic-ai-professional-certificate",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T11:16:26.008Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T11:16:26.008Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-danezi-ibm-rag-agentic-ai-professional-certificate/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]