Vector search got us the first wave of AI copilots. It’s excellent at finding relevant passages fast.
But business workflows are different.
A contract renewal has to respect policy and clause precedence.
Approval requires a designated role and adherence to established delegation rules.
A SKU update touches dependencies across products and systems.
These tasks rely on relationships: who can do what, which rule overrides another, and how entities connect across apps.
Vector-only retrieval brings back useful fragments. It doesn’t provide the connected state an AI agent needs to complete a multi-step job.
Hybrid memory changes that. It combines vectors (semantic recall) with a knowledge graph (explicit entities, rules, and links).
It helps the AI agent to find the right evidence and follow the connections that determine the next steps.
This GraphRAG pattern transforms answers into completed workflows, leading to fewer escalations and more precise provenance.
In this edition, we’ll walk through why agentic AI needs hybrid memory and where to start.
Stay updated with Simform’s weekly insights.
Use hybrid memory where work is multi-step and policy-bound
What this entails
Choose vector + graph when the agent must chain facts and respect rules, e.g., “renew under clause X, check limits, route to the right approver, update CRM.”
Vectors provide broad semantic recall; a knowledge graph encodes entities, relationships, and policies, allowing the AI agent to follow connections across steps.
Microsoft’s GraphRAG research shows that building a knowledge graph from text enables agents to answer “global” and multi-hop questions that vector-only RAG misses.
Nuances and trade-offs
- Latency and throughput: Following connections in a graph adds work compared with a single semantic lookup; multi-step traversals increase p95 latency and reduce headroom for high-QPS use cases.
- Task fit: For single-passage questions or small, uniform document sets, semantic search with a lightweight re-rank typically meets accuracy and latency goals without a graph layer (a common Azure AI Search pattern).
Case in point
BlackRock’s HybridRAG indexed earnings transcripts are available both as embeddings and as a graph of entities (companies, metrics, and relations).
The hybrid approach outperformed the vector-only approach in terms of retrieval precision, answer quality, and faithfulness, utilizing Azure OpenAI for the LLM, as it could fetch the quote and traverse the linked context that determined its meaning.
How can you decide?
- Target the right workflows first: If a process routinely needs two or more connections (e.g., customer → contract → clause → approver), pilot hybrid on that flow.
- Measure outcomes: Track task completion without escalation and share of answer statements with sources (provenance coverage) before/after. Expand the hybrid only if both improvements are made.
Design the graph to evolve with the business
What this entails
Model the specific workflow you care about, e.g., Customer → Contract → Clause → Approver for renewals. So the agent can traverse decisions in one pass while vectors pull the supporting passages. Keep the graph scoped to the entities and links the workflow actually uses.
Nuances and trade-offs
- Depth vs. agility: Rich models capture more rules but change slowly; lean models adapt faster and are easier to maintain.
- Identity resolution cost: Reconciling the same customer/account/SKU across systems adds computational complexity and can introduce incorrect merges or splits that skew decisions.
How can you decide?
- Start with one flow: Define the minimum data blueprint (entities, relationships, policy links) for that workflow; expand only when usage exposes gaps.
- Set refresh SLOs: Decide how quickly updates must appear (e.g., policy changes <24h) and track graph freshness against that target.
- Capture provenance by default: Store document/section IDs on nodes and edges so answers are explainable to auditors and customers.
- Watch operating signals: Monitor graph coverage (e.g., % of contracts with valid clause→approver links) and update lag (ingest-to-graph time). Drops in either usually predict degraded agent behavior.
Engineer for audit-grade explainability and safe failure
What this entails
Agentic AI in business needs defensible answers. A knowledge graph provides the AI agent with a structural trail of entities it has touched and why, allowing you to show the path from question to decision.
Many firms are now measuring “answer explainability & provenance” (e.g., step-by-step sources and reasoning).
Nuances and trade-offs
Capturing and returning paths adds work, but it also reduces silent errors. Graph constraints lower hallucinations by preventing the model from inventing relations that don’t exist; vectors still ensure broad recall.
When the graph lacks a required link, precision beats guessing. The AI agent should halt with a specific gap rather than proceed on assumptions.
Case in point
A mid-market SaaS vendor on automated contract renewals with hybrid memory. Vectors pulled the MSA, amendments, and pricing pages; the graph mapped Customer → Contract → Clause → DiscountPolicy → ApproverGroup with sources on every edge.
When an AI agent proposed a 22% discount, it produced an audit trail: “found Amendment 3, section 4.2 → matched DiscountPolicy T2 → requires Legal + Finance.”
In runs where the ApproverGroup link was missing, the agent stopped, returned “approver mapping not found”, attached the path and citations, and routed a ticket.
How can you decide?
- Provenance by default. Every answer cites sources and logs the graph path taken (or attempted). Track hallucination rate and an explainability score (e.g., % with step-wise citations).
- Design for safe failure. If a required relation is missing, stop with a clear reason, attach evidence, and route to human review.
The next competitive edge will come from faster policy-to-production. Measure how quickly a rule change shows up in agent behavior. If that lead time is extended, automation will stall. To shorten it, explore our accelerators.
