ModelRefs / Agentic RAG — Canonical Workflow
Agentic RAG — Canonical Workflow
Agentic RAG: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.
Overview
Agentic RAG upgrades a standard single-shot retrieval pipeline with a planning agent that can reformulate queries, switch retrieval strategies, validate the retrieved evidence against the question and iterate until the answer meets a confidence threshold. The agent maintains a scratchpad of retrieval attempts, surfaces the chain of retrieval decisions alongside the final answer, and falls back gracefully when no confident answer can be constructed. This pattern is recommended over single-shot RAG for complex multi-hop questions and research-grade applications where source traceability is critical.
Implementation profile
| Category | agentic-models |
|---|---|
| Implementation maturity | production |
| Primary use cases | rag, agents, reasoning |
| Deployment options | managed-api, hybrid |
| Architectures | serverless-api, managed-container, self-hosted-cluster |
What this evidence does not establish
- No target corpus, permission model, query distribution, multi-hop test set, or acceptance threshold is registered.
- No measured loop rate, stale-source behavior, citation precision, or human-escalation outcome is attached.
Candidate models with published references
- BGE-M3
- GPT-5
- GPT-5 Mini
- Claude Sonnet 4.5
- Claude Opus 4
- Gemini 3 Pro
- Llama 4 Scout
- DeepSeek R1
- Mistral Large 2
- Command R+
- o3
- o4 Mini
Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.
Benchmarks relevant to this workflow
- τ-bench — Tool-augmented agent benchmark on retail & airline customer-service tasks.
- GAIA — General AI assistant benchmark — multi-tool real-world questions.
These are the benchmarks whose task shape bears on this workflow. Each one only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Agentic RAG — Canonical Workflow.