Agentic RAG
Also written as Agentic Retrieval, Agentic Search
RAG where an AI agent decides how to look things up instead of running one fixed search: it can rewrite the question, search several sources or tools, check whether the results answer it, and search again before replying. Better for complex questions, but slower, costlier and harder to test.
Think of it like
A researcher who keeps digging and cross-checking until satisfied, versus a librarian who fetches the first book that matches.
Junior or senior?
Newer than basic RAG, so long claimed experience deserves a closer look.
Senior sounds like
Can explain when the extra searching was worth the cost and delay, and how they measured whether answers improved.
Ask them
“Why did your RAG system need an agent rather than a single search, and how did you show the answers actually got better?”
Reading Agentic RAG on a resume
- High signal
- Agentic RAG genuinely separates candidates, so it is worth leading a screening call with it. Its presence is a reason to talk; the question above tells you whether the experience is real.
- Emerging
- Agentic RAG is new enough that few people have deep experience with it. Don't screen out a strong candidate for missing it, and be careful with big claims.
Sourcing for Agentic RAG
Every way it's written
("Agentic RAG" OR "Agentic Retrieval" OR "Agentic Search")Paste into LinkedIn Recruiter, Naukri or your ATS. Combine with a job title and location.
Job titles to search alongside it
Machine Learning Engineer · ML Engineer · AI Engineer · Applied Scientist · MLOps Engineer · Data Scientist
Where this talent works in India — AI & Machine Learning
Target companies with models in production and their own training or inference spend — hyperscaler AI teams, consumer platforms with ranking systems, and funded model startups. Analytics-only shops produce the job title without the delivery.
For example: Google (Research and DeepMind India), Microsoft (including Microsoft Research India), NVIDIA, Adobe, Amazon, Salesforce, Uber, Sarvam AI.