RISK-01
Hallucination & Inaccuracy
Without access to your actual data, agents rely on training patterns. They invent customer details, misquote pricing, suggest products already purchased.
Resolved by
Direct data references + provenance metadata
How assistents.ai builds unified business context from 300+ systems, so every agent understands your data, your rules, and your workflows.
Without unified context, AI agents operate in isolation. Most AI platforms treat each query as independent: no knowledge of your customer history, your contract terms, your compliance rules, or your business logic.
Without access to your actual data, agents rely on training patterns. They invent customer details, misquote pricing, suggest products already purchased.
Direct data references + provenance metadata
Agents return textbook answers instead of solutions tailored to your business. A support agent can't contextualize why a ticket happened or which contract applies.
Semantic graph relationships + cross-system joins
Without explicit permission boundaries, agents either over-share sensitive data or under-serve users by withholding data they should access.
Query-time ACL enforcement + audit logging
The context engine grounds every agent decision in actual business data: filtered by permissions, linked across systems, and auditable back to source.
Four integrated layers. Each adds intelligence; together they deliver real-time, permission-aware, semantically unified context.
From task assignment to agent execution: how the context engine delivers unified business context in real time.
e.g., "Summarize Q4 pipeline for enterprise accounts"
<5msCRM deals, account records, activity logs, contract details.
~20msFilters to data the requesting user can access. Respects role-based controls across all source systems.
~15msConnects related entities across systems. Links deals to accounts, accounts to companies, products to SKUs.
~80msIncludes provenance metadata: source system, data freshness timestamp, confidence score.
~60msCites sources. All decisions auditable back to original data. No hallucination drift.
TOTAL <200ms6 steps TOTAL <200ms end to end provenance attached at delivery
Real-time, unified, permission-aware context defeats point-in-time retrieval.
6 criteria context engine vs traditional RAG vs search-first
Supported systems, data formats, and deployment architectures.
How the context engine responds when things go wrong. Every failure mode has an explicit, documented handling strategy.
When a source system becomes temporarily unreachable, the engine returns last known good data with a staleness flag and timestamp.
When source permissions change between ingestion and query time, the engine re-validates at query time. If permissions were revoked, data is excluded.
When a source system goes offline during a live query, the engine returns partial results with degradation flags and source status indicators.
When context exceeds token limits for the requesting agent, the engine ranks by relevance and truncates with a continuation token.
Watch how assistents.ai agents access unified context from your Salesforce, Slack, and data warehouse: in real time, with zero hallucinations, and full permission compliance.