Agentic search as a new substrate for GenAI systems

What if the search plus agent layer is not just a better widget, but a new substrate for GenAI systems?

Here are three insight shifts you should consider.

1. Agentic search becomes the control plane for your knowledge layer

Classic workflow: query → retrieve → rerank → feed LLM.

Now: a query planner decomposes the request, executes parallel sub queries, then synthesises results. (See Agentic Retrieval in Azure AI Search.)

That means the search layer is not passive. It orchestrates and becomes the substrate agents operate on.

2. The right experiment is not “can it beat a reranker” but “does it change your system design”

Do not pit agentic versus classic retrieval only for accuracy.

Ask instead:

• Can the agent decide how many sub queries to issue
• Which sources to call
• In what sequence

Do latency, cost, and observability regimes change

Microsoft reports up to around 40 percent better relevance for complex queries using agentic retrieval.

3. If you only benchmark accuracy, you miss the real value and the real risk

Gains in agentic retrieval come from:

• Query planning
• Orchestration
• Dynamic tool sourcing

But they also introduce:

• New failure modes
• New cost and latency trade offs

Experiments should measure:

• Query planning quality
• Workflow breadth, including tools, sources, and modalities
• Cost and latency complexity

Mini case

A global help desk replaced a standard RAG stack with an agentic pipeline. They decomposed a multi part query (failure cause, patch list, incident intel) into three simultaneous sub queries.

Time to answer dropped by roughly 30 percent.

That is not just better search. It is a redesign of the retrieval knowledge interface.

In short: if you treat search plus agent as merely better retrieval, you will design experiments incorrectly. If you treat it as a new substrate, with its own architecture, cost model, observability, and control surface, you will ask deeper questions and sketch different systems.

So here is the key question: instead of asking “will agentic search beat retrieval plus reranking”, ask:

In which system context does agentically controlled search become the foundation layer rather than a plugin

What re architecture are you willing to explore to treat retrieval as the orchestration plane, not just the data fetcher?

Continue the discussion over on LinkedIn.