TECHNOLOGY COMPARISON
Agent memory, RAG and caching solve different problems.
These technologies can all reduce waste, but they operate at different points in an AI request. The useful question is not which one wins; it is which layer should retrieve evidence, reuse an approved result, lower repeated computation, or manage the remaining model call.
RAG retrieves source material. A vector database stores and searches representations. Prompt or semantic caching reuses matching computations or responses. A gateway manages model traffic. Shared agent memory keeps reusable results with the source, scope, approval, freshness, and history needed to decide whether another agent may use them.
RAG retrieves evidence
Retrieval-augmented generation finds relevant documents or passages and sends them to a model. It improves grounding, but usually still asks the model to reason over the retrieved material for each request.
Caches reuse a technical result
Prompt caching reduces repeated computation for shared prompt prefixes. Exact and semantic response caches can return an earlier response for a matching request. They are useful optimizations, but a match alone does not explain ownership, approval, purpose, or reuse boundaries.
A gateway manages calls that still happen
An LLM gateway centralizes provider access, keys, retries, limits, routing, logging, and cost controls. A memory layer sits before or alongside it to determine whether a model call is needed and what focused context should be sent.
Shared memory governs reuse across agents
Governora treats a useful result as a governed company record. It connects answer, evidence, owner, team, purpose, approval, freshness, policy, and prior reuse so another permitted agent can decide whether to reuse it safely.
- Use RAG to retrieve current evidence.
- Use caches to optimize repeat computation or matching requests.
- Use a gateway to manage model traffic.
- Use shared memory to govern durable reuse across agents.
HOW IT WORKS
Four clear steps.
Each step stays connected to the same execution. This makes authority, external work and the final outcome explainable.
- 01
Retrieve
Find source material with search, RAG, or existing knowledge systems.
- 02
Check memory
Look for a previously approved result that fits the new user and purpose.
- 03
Route
Reuse it, or send focused context through the approved gateway and model.
- 04
Record
Save an approved new result and its lineage for future agents.
QUESTIONS
Simple questions. Direct answers.
What Governora does, what it does not do, and where it fits.
Does shared agent memory replace RAG?
No. RAG can supply current evidence while shared memory can reuse an approved result from earlier work. They can be used together.
Is semantic caching enough for enterprise reuse?
It can be enough for a narrow, low-risk workflow. Cross-team reuse often needs additional source, scope, freshness, approval, and policy checks.
Can Governora sit in front of an existing LLM gateway?
That is the intended architectural role: check memory and build context first, then use the existing gateway for model calls that remain necessary. Exact integration depends on the environment.
Map one governed AI execution.
We will map the actor, decisions, capabilities, evidence, external calls and proof with you.
Plan a pilot review →