By Sagar Shankaran, Founder of CallSphere
Traditional RAG is one-shot retrieval-then-generate. Agentic RAG plans, retrieves, evaluates, re-retrieves. It costs 3-10x more tokens and 2-5x more latency — and earns it on multi-hop and ambiguous queries.
Key takeaways
TL;DR — Traditional RAG is a function: query in, answer out. Agentic RAG is a controller: it plans, calls tools, evaluates retrieval confidence, re-retrieves on miss, and self-critiques before answering. It costs 3–10x more tokens and 2–5x more latency. Use it for multi-hop, ambiguous, or high-stakes domains; stick with one-pass RAG for everything else.
Traditional (naive) RAG: retrieve(query) -> generate(query, context). One-shot, no feedback. Works well on factual single-hop questions over a clean corpus.
Agentic RAG inserts a planner and a self-critic. A planning agent decomposes the query, picks tools (vector DB, SQL, web search, internal API), routes results through a retrieval evaluator, and either generates or loops back. LangGraph and LlamaIndex Workflows are the dominant 2026 frameworks; both expose the loop as a state graph.
flowchart LR
Q[Query] --> P[Planner]
P --> T{Tool}
T -->|vector| V[Vector DB]
T -->|sql| S[SQL]
T -->|api| API[Internal API]
V --> EV[Retrieval evaluator]
S --> EV
API --> EV
EV -->|low conf| P
EV -->|high conf| G[Generator]
G --> SC[Self-critic]
SC -->|fail| P
SC -->|pass| A[Answer]
The planner sees the query plus chat history and emits a JSON plan: subqueries, tool selections, parallelism, success criteria. Each subquery hits the assigned tool. A small retrieval-evaluator model scores each result for relevance. If any subquery falls below threshold, the planner gets a "retry" signal with the failed subquery and the evaluator's reason. After generation, a self-critic checks for citation grounding and constraint satisfaction (e.g., "did we answer all 3 parts?"). The critic can re-trigger the planner.
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This costs more — every loop is one LLM hop — but is the only viable architecture for compound queries like "compare the cancellation policies of plans A and B for users in California, and tell me which one is better for a freelancer."
Every CallSphere voice agent is agentic: gpt-realtime as the planner, hybrid retrieval as one tool, 90+ specialized tools (book, verify_insurance, get_benefits_breakdown, escalate_to_human, etc.) as the others. 115+ Postgres tables are reachable via typed SQL tools. The Healthcare agent loops up to 3 times when an eligibility check fails the first time; UrackIT IT helpdesk loops on ticket-search misses; OneRoof real estate replans on ambiguous "which neighborhood" queries.
37 agents · 6 verticals · pricing $149 / $499 / $1499 · 14-day trial · 22% affiliate. Compare verticals on /industries/it-services and /industries/real-estate.
from langgraph.graph import StateGraph
def plan(state):
return {"plan": llm.complete(PLAN_PROMPT.format(q=state["query"]))}
def retrieve(state):
results = [tools[s.tool](s.subquery) for s in state["plan"].steps]
return {"results": results}
def evaluate(state):
scores = [eval_llm.score(s.subquery, r) for s, r in zip(state["plan"].steps, state["results"])]
return {"scores": scores, "low_conf": any(s < 0.6 for s in scores)}
def generate(state):
return {"answer": llm.complete(GEN_PROMPT.format(q=state["query"], ctx=state["results"]))}
g = StateGraph(dict)
g.add_node("plan", plan); g.add_node("retrieve", retrieve)
g.add_node("evaluate", evaluate); g.add_node("generate", generate)
g.add_edge("plan", "retrieve"); g.add_edge("retrieve", "evaluate")
g.add_conditional_edges("evaluate", lambda s: "plan" if s["low_conf"] else "generate")
g.add_edge("generate", "__end__")
Always go agentic? No — for one-shot factual lookups, traditional RAG is faster and cheaper.
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LangGraph or LlamaIndex Workflows? LangGraph for general agentic; LlamaIndex for retrieval-heavy single-pipeline.
Voice or chat? Both, but voice tightens the latency budget.
Self-critic worth it? Yes for high-stakes (legal, medical, billing). Skip for casual chat.
See it on /demo? Toggle "advanced reasoning" — you will see the loop in the trace.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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