Agentic AI · Agentic RAG systems
Your RAG answers the easy question once — the hard one trips it every time
Agentic RAG replaces the single-pull retrieval loop with an agent that plans which sources to query, decides when one answer is not enough, re-queries with a sharper filter, and stitches a cited response from what it actually found — not what the model interpolated to fill the gap.
Banao designs, builds, and evaluates agentic RAG pipelines for production: the retrieval planner, the multi-source connectors, the grounding check, the citation layer, and the eval suite that proves the system is right before it talks to users.
Banao— We run a retrieval-grounded pipeline on our own hiring tools; every candidate summary cites the role document it was generated from.
What a Banao agentic RAG build includes
Production agentic RAG is more than a vector search call. We build the full stack: retrieval planning, connectors, grounding, citation, and the evals that catch a regression before your users do.
Retrieval planning and query decomposition
An agent layer that reads the question, decides which sources are relevant, rewrites the query for each one, and schedules the pulls — so multi-hop questions get the right retrieval strategy, not a single generic embed search.
Multi-source connector wiring
We connect the retrieval planner to your document stores, internal wikis, databases, and APIs so the agent pulls from the authoritative source for each sub-question, not a single flat index.
Adaptive re-retrieval
When the first pull is thin or contradictory, the agent re-queries with a refined filter before generating — catching the cases where a one-shot RAG system answers confidently on partial data.
Grounding and citation layer
Every claim in the generated answer is pinned to a retrieved passage, with source and position, so the system stops when it lacks evidence rather than drawing from model weights.
Conflict detection and source ranking
When two retrieved passages say different things, the agent surfaces the conflict and defers to the source you designate as authoritative — instead of silently averaging the contradiction.
Evaluation suite and regression harness
A retrieval-quality and answer-quality eval suite built from your real queries and expected answers, run before launch and after every model or index change.
Hallucination audit and acceptance threshold
We measure citation coverage and hallucination rate on your data before go-live and define an acceptance threshold with you — a number that decides whether the system ships, not a subjective demo impression.
Observability and trace logging
Full traces of every query plan, retrieval call, and grounding check — so your team can see why the system gave a particular answer and intervene when the retrieval strategy breaks.
Why agentic RAG outperforms static retrieval on enterprise data
Static RAG was designed for documents that are clean, flat, and well-indexed. Enterprise data is none of those things: it spans siloed systems, uses inconsistent terminology across departments, and answers most questions only if you query two or three sources and reconcile what they say.
Agentic RAG handles that reality. The retrieval planner treats a question as a task — it decomposes the question, routes sub-queries to the right sources, checks whether what came back is sufficient, and re-queries if not. The result is a system that earns citation coverage instead of hoping a single embed pull finds the right passage.
Multi-hop questions answered, not evaded
Questions that require stitching two facts from different documents are the ones static RAG fails on most. The agentic loop handles them by treating each sub-question as a separate retrieval task.
Index drift handled at query time
When source data changes faster than a bulk re-index can keep up, the adaptive re-retrieval step finds the gap and flags it — rather than returning a stale passage with no indication it is out of date.
Auditable by design
Every answer includes the retrieval trace: which queries ran, which passages were used, which were discarded, and why. That audit trail is what enterprise legal and compliance teams need before they trust a system that reads their documents.
The retrieval failures agentic RAG is built to fix
The most common complaint we hear about RAG in production is not that it is slow or expensive — it is that it answers confidently on cases where the retrieved context was wrong, thin, or missing. The generation model fills the gap with plausible text, and the user has no way to know the ground was not there.
The second failure mode is false negatives: the answer exists in the corpus, the user asked for it directly, and the system returned nothing useful because the query phrasing did not match the indexed text. Both problems share the same root: retrieval with no feedback loop and no replanning when the first pull falls short.
Confident answers on thin retrieval
We add a grounding check that measures citation coverage before the answer is returned — and routes low-coverage responses to a fallback or a human rather than emitting them as confident.
Query-index phrasing mismatch
The retrieval planner rewrites the user query into the vocabulary the index actually contains, tested against your real document set during the Discovery Sprint before we write the production planner.
Silent retrieval failures
We instrument every retrieval call so a failed or thin pull is observable, not silent — giving your team the data to know when the pipeline needs a different retrieval strategy.
We depend on retrieval-grounded AI on our own team
InterviewGod, the hiring tool Banao runs across its own ~300-person engineering operation, uses a retrieval-grounded pipeline: candidate summaries are generated against the retrieved role description and must cite their source passages. We built the citation discipline into the tool because we are the people reading the outputs.
Building a grounded retrieval system you stake your own decisions on is a different discipline from shipping one for a client and walking away. That discipline is what we apply to an agentic RAG build for you.
- InterviewGodRetrieval-grounded candidate summaries — run on Banao's own hiring every week.
- VikaasBanao's own demand engine uses retrieved content to ground every outbound message.
When agentic RAG is the wrong answer
Agentic RAG solves a specific class of retrieval problem. We will tell you before you commit a budget if you do not have that problem:
- Your data is well-structured and SQL-queryable: if the facts live in a relational database with a good schema, a direct query returns more reliable results than a retrieval pipeline.
- The question set is narrow and predictable: if users always ask variants of ten questions, a curated FAQ is cheaper to build and cheaper to operate than an agentic retrieval loop.
- Your corpus is too small to justify an index: below a few hundred documents, the overhead of embedding, indexing, and retrieval planning costs more than a direct document scan.
- You need real-time data: agentic RAG works on indexed corpora; if the answer must reflect data updated in the last minute, you need live tool calls rather than retrieval.
How we start — test the retrieval pipeline before we build it
We do not quote an agentic RAG build off a brief. We test your hardest retrieval failure first.
- AI Discovery Sprint2 weeks · fixed price
We map your retrieval failures, test the agentic query-planning approach against your actual corpus and worst-case queries, and hand back a retrieval design, an eval plan, and a citation-coverage baseline — yours to keep. If you proceed, the Sprint fee is credited against the build.
- Build
We build the retrieval planner, multi-source connectors, grounding and citation layer, conflict detection, and the eval harness — all as deliverables, with documented acceptance thresholds.
- Production & continuous improvement
We ship with full trace logging, run the eval suite on each index change or model update, and iterate the retrieval strategy on real query logs so accuracy improves over time.
Frequently asked questions
What is agentic RAG and how does it differ from standard RAG?
Standard RAG runs one retrieval call, takes what comes back, and generates. Agentic RAG adds a planning layer: the agent decides what to query, which sources to use, whether the retrieved passages are sufficient, and re-queries when they are not. The difference is most visible on multi-hop questions and enterprise corpora where a single embed search misses the right passage.
What data sources can an agentic RAG system connect to?
Any source with a queryable interface: document stores, Confluence or Notion wikis, SharePoint, SQL and NoSQL databases, internal APIs, and web search where your policy allows it. We wire the retrieval planner to whichever sources hold the authoritative answers for your use case.
How do you measure whether the system is actually grounded?
We build a citation-coverage metric into the eval suite: for every answer, we measure what proportion of claims are pinned to a retrieved passage. Before launch, we establish an acceptance threshold with you — a number below which the answer is not emitted as confident. That threshold is tested on your real query set, not a synthetic benchmark.
How do you handle documents that contradict each other?
We build a conflict-detection step that surfaces the contradiction rather than silently averaging it. You define a source-ranking policy — which system is authoritative when two passages disagree — and the agent applies it. Unresolvable conflicts are flagged for a human rather than resolved by the model.
What does an agentic RAG project typically cost and take?
The Discovery Sprint is a fixed price and returns a retrieval design, eval plan, and citation baseline — so you can size the build before committing to it. Build scope depends on the number of sources, the complexity of the query planner, and the coverage required by your eval suite — all defined during the Sprint.
Can the RAG pipeline be combined with an agent that also calls tools?
Yes. Retrieval is one tool the agent can call; we can combine it with API calls, database queries, and other function calls in the same agent loop. Banao builds the full agentic loop, not just the retrieval component.
How do you keep the system from returning stale information?
We instrument index freshness and add a staleness check to the retrieval output — so the agent knows when the retrieved passage is from a document that has since been updated, and can flag it or re-query a live source. For data that changes faster than any reasonable re-index cadence, we discuss whether a live tool call is more appropriate than static retrieval.
Do we get the code and eval harness to run ourselves?
Yes. We hand over the retrieval planner, all connector code, the grounding and citation layer, and the full eval harness with documented acceptance thresholds. Your team can run the evals on index or model changes without re-engaging us.
Show us your hardest retrieval failure
Bring the question your current RAG system gets wrong most often. In 45 minutes we will tell you whether an agentic retrieval approach would close the gap — and what it would take to build one your team can trust.
Book a 45-min scoping call