Agentic AI / From RAG to Agentic RAG

Agentic RAG Implementation Checklist

A gated interactive checklist designed for development teams to assess and plan their Agentic Retrieval-Augmented Generation (RAG) implementation stages, covering readiness, architecture, tooling, and governance.

This interactive checklist helps enterprise development teams evaluate their readiness and progress toward implementing an Agentic Retrieval-Augmented Generation (RAG) system. It covers core areas including architecture design, tooling selection, data pipeline readiness, agent orchestration, and governance frameworks.

Answer each section based on your current state to identify gaps and next steps. At the end, receive a tailored summary highlighting critical focus points and considerations to advance your Agentic RAG project.

Inputs

Data pipeline readiness: Is your data pipeline automated, scalable, and maintaining quality for real-time retrieval?

Fully automated and scalable with quality monitoringPartially automated, needs improvementManual or non-scalable pipeline

Agent orchestration framework: Which framework or platform are you using or planning for agent coordination?

Examples include Langchain, AgentGPT, custom Kubernetes operators, or others.

Large language model (LLM) integration: Have you integrated LLMs (e.g., GPT-4, Claude 2) into your RAG pipeline with proper API management?

Fully integrated with API key rotation and monitoringIntegrated but lacking API managementNot integrated or plans pending

Knowledge base versioning: Is your document store or vector database versioned and synced with source updates?

Versioned with automated syncPartial versioning, manual updatesNo versioning or sync

Agentic policy and governance: Do you have defined policies for agent autonomy, use-case-specific guardrails, and audit logging?

Comprehensive policies with audit and compliancePolicies in draft or limited scopeNo defined agentic governance

Tooling support for observability: Are monitoring and logging tools in place for RAG and agent performance tracing?

Full observability integrated (e.g., OpenTelemetry, Datadog)Partial observability, limited metricsNo dedicated observability tooling

Team expertise: What best describes your team's experience with Agentic AI and RAG?

Result

Agentic RAG readiness score

(data_pipeline_ready == 'yes' ? 1 : (data_pipeline_ready == 'partial' ? 0.5 : 0)) + (agent_orchestration_framework != 'none' ? 1 : 0) + (llm_integration == 'yes' ? 1 : (llm_integration == 'partial' ? 0.5 : 0)) + (knowledge_base_versioning == 'yes' ? 1 : (knowledge_base_versioning == 'partial' ? 0.5 : 0)) + (agentic_policy_governance == 'yes' ? 1 : (agentic_policy_governance == 'partial' ? 0.5 : 0)) + (tooling_support_for_observability == 'yes' ? 1 : (tooling_support_for_observability == 'partial' ? 0.5 : 0)) + (team_expertise == 'high' ? 1 : (team_expertise == 'medium' ? 0.5 : 0))

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Agentic RAG implementation readiness

Your current setup shows key gaps in data pipelines, governance, or integration that could impact reliable Agentic RAG deployment. Address pipeline automation and policy definition first.

Next step advice

Completing this checklist should inform targeted architecture improvements, selecting mature agent frameworks, and establishing governance processes. Refer to vendor-neutral Agentic RAG frameworks such as Langchain 1.0+ and trial integrations with GPT-4 or Anthropic Claude 2. Incorporate automated pipelines with vector DB versioning for reliable retrieval.

Subsequent sections unlock after submit