Foundry IQ Turns Enterprise Data into a Shared Knowledge Brain for Multi‑Agent AI

What changed

Building an AI assistant today still hinges on two separate problems: the model’s reasoning ability and the data it can reference. Most teams solve the second problem by wiring a retrieval‑augmented generation (RAG) pipeline into each agent. The result is a collection of siloed indexes, duplicated code, and divergent answers to the same question. Microsoft’s Foundry IQ flips that model on its head. Instead of each agent owning its own retrieval stack, Foundry IQ provides a centralized knowledge brain that multiple agents can query. The service connects to SharePoint, OneLake, Azure Blob, Fabric, or any custom source, builds a single index, and exposes an agentic retrieval API that breaks complex queries into sub‑questions, iterates across sources, and returns a grounded response.

Key capabilities include:

• One‑time indexing of enterprise data, eliminating per‑agent re‑ingestion.
• Agentic retrieval that plans multi‑step searches and assembles concise answers, reducing token consumption.
• Unified governance for permissions, data residency, and audit logging.
• Plug‑and‑play integration via the Foundry SDK, so agents only need to call foundryIQ.ask(question).

Provider comparison

Feature	Microsoft Foundry IQ	AWS Bedrock + Kendra	Google Vertex AI Search
Central knowledge store	Single index managed by Foundry IQ, shared across all agents	Kendra creates a separate index per data source; agents must reference the same index manually	Vertex AI Search builds a corpus per project; sharing requires explicit data set duplication
Agentic retrieval	Built‑in multi‑step planning, sub‑question generation, iterative refinement	Bedrock models can call Kendra, but orchestration is left to the developer (Lambda, Step Functions)	Vertex AI Search returns ranked results; higher‑level reasoning must be added in the agent code
Integration cost	Included in Foundry subscription; additional storage billed per GB	Kendra pricing: $0.005 per 1,000 queries + storage; Bedrock usage billed per token	Search pricing: $0.001 per query + storage; Vertex AI model usage billed separately
Governance	Central policy engine, Azure AD RBAC, data residency controls	IAM policies per index; no unified audit across multiple indexes	Cloud IAM per data set; audit logs separate from search service
Migration path	Export existing indexes via the Foundry SDK, import into a new IQ knowledge base, update agents to point to foundryIQ endpoint	Export data to S3, create a Kendra index, rewrite each agent’s retrieval layer to call Kendra APIs	Move documents to Cloud Storage, create a Vertex AI Search data set, modify agents to use the Search client library

Why the differences matter

• Complexity – With Foundry IQ the orchestration logic lives inside the service; AWS and Google require extra glue code (Lambda, Cloud Functions, or custom orchestration) to achieve the same multi‑step retrieval.
• Cost predictability – Microsoft bundles the retrieval engine with the Foundry platform, so you pay for storage and token usage only. AWS and Google charge per query in addition to the underlying model usage, which can make budgeting harder when query volume spikes.
• Governance – A single policy surface in Foundry IQ simplifies compliance audits. In AWS and Google you must reconcile IAM rules across multiple indexes or data sets, increasing the risk of gaps.

Business impact

Consistency and brand trust

When every agent draws from the same knowledge base, the answers it provides are identical regardless of the channel (HR bot, sales assistant, support desk). This eliminates the customer‑facing confusion that arises when two bots give slightly different policy interpretations.

Faster time‑to‑market for new agents

A product team can spin up a new assistant by reusing an existing IQ knowledge base. The only effort required is to define the agent’s behavior (prompt templates, action hooks). In a fragmented setup, each new bot would need a fresh indexing pipeline, testing, and validation – a process that can add weeks of effort.

Lower operational overhead

Centralized indexing means you run one set of ingestion jobs, one set of monitoring alerts, and one cost model for storage. Teams no longer need to maintain dozens of custom RAG scripts, which reduces the likelihood of bugs and security misconfigurations.

Migration checklist for existing multi‑agent fleets

1. Audit current retrieval pipelines – List data sources, indexing tools, and query costs.
2. Create a Foundry IQ knowledge base – Use the Foundry IQ documentation to connect each source once.
3. Export and re‑ingest – Pull existing embeddings from Elasticsearch, Pinecone, or custom stores and push them into the IQ index via the SDK.
4. Update agents – Replace calls to individual RAG services with foundryIQ.ask(). Verify that token usage drops as the service returns more concise, pre‑filtered excerpts.
5. Validate governance – Confirm that Azure AD groups, data residency tags, and audit logs reflect the new shared model.
6. Monitor – Use the Foundry portal to track query latency, storage growth, and cost per agent.

Example ROI scenario

A mid‑size enterprise runs five bots, each querying a separate Kendra index at an average of 2,000 queries per day. At $0.005 per 1,000 queries, the query cost alone is $50 per day. Consolidating to a single Foundry IQ knowledge base reduces query volume by 40 % (shared caching) and eliminates the per‑index storage overhead. Assuming a storage cost of $0.02 per GB per month, the organization saves roughly $1,200 per quarter while also cutting engineering time for pipeline maintenance by an estimated 300 hours.

Closing thoughts

Foundry IQ moves knowledge management from the per‑agent level to a platform capability. By providing a single, governed index and an agentic retrieval engine, it lets teams focus on what each assistant should do rather than how it should find information. For organizations that already run multiple AI agents, the shift is less an optimization and more a prerequisite for scaling responsibly.

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Further reading

• What is Foundry IQ? – Microsoft Learn
• Foundry IQ knowledge integration guide
• AWS Kendra pricing
• Google Vertex AI Search documentation