Agentic AI: Built on Reliable Retrieval
Building an AI agent that depends on enterprise knowledge or operational data? Searchplex engineers the retrieval, grounding, and access-control systems that agents rely on, from architecture and validation through to integrating retrieval into your agent and tuning it for production.
Reliable agents need reliable access to information.
AI agents may need to retrieve documents, business records, policies, and task-specific evidence across multiple steps. When that information is incomplete, outdated, or outside the user's permissions, even a capable model can produce unreliable outcomes.
Reasoning, planning, tool selection, and execution can each introduce failures. Searchplex designs the retrieval layer that makes relevant, current, and authorised context available to agent workflows, with measurable quality and traceable evidence.
The retrieval layer agents depend on.
Context retrieval and ranking
Identity, permissions, and source authority
Structured and unstructured data
Evaluation, traceability, and production integration
Where Searchplex fits
We focus on the retrieval and knowledge-access foundation of agentic systems, working alongside your application and orchestration architecture. The same retrieval foundation can serve agents, applications, and people searching directly.
An engagement can begin before an agent is built, or address an existing system that needs stronger retrieval and grounding. Orchestration, interfaces, and action execution sit outside this scope unless agreed separately.
New agent or existing agent
Settle the retrieval approach before you build
You are designing an agent that depends on enterprise knowledge.
Test the critical retrieval question on your data, or talk through the project and what to scope first.
Find and fix what is limiting it
Answers are stale, incomplete, or reach people who should not see them.
If the cause is unclear, start with a Search Stack Audit.
If you know retrieval or ranking needs work, explore Relevance Engineering.
Where agentic systems meet retrieval
Many agent programmes start with workplace knowledge RAG: permissioned corpora, versioned sources, and retrieval that has to hold before users or agents rely on the answers.
Vacation-rental concierge agents push further—the system must bind context to the right guest, reservation, unit, and authority boundary. See AI Concierge Agents for Vacation Rentals.
On an Enterprise RAG programme, deep research and synthesis are scoped at Tier 4 — Deep Research & Synthesis (Agentic RAG).
Agentic AI does not replace retrieval. It raises the standard for it. This solution depends on source quality, content preparation, retrieval modeling, query understanding, retrieval, ranking, and evaluation.
Work with us the way that suits your team
Either way, you own the result, and the aim is a team that can run it without us.
Searchplex delivers the project
Specialists paired with your engineers
Building an agent that depends on your knowledge?
Bring one agent workflow and the sources it relies on. We’ll discuss where retrieval fits and what to test first.