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Solutions Hub: Context Engineering for AI Agents

Give your AI agents the context they need, and only the context they should see

One catalog of data and metadata, with every agent held to its identity, role and approved intent.

AI agents do better work when they receive context alongside their instructions and goals. For an agent that uses customer data, the most useful context is the metadata around it: what the data means, where it came from, how sensitive it is, who owns it and how current it is. BigID indexes structured and unstructured data, catalogs that metadata behind APIs and MCP, discovers each agent and what it is approved to do, and controls which data and metadata every agent can see.

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What it indexes
Données structurées et non structurées
Files, email, code and tables across hundreds of data stores and formats, including the content language models are built on
What it catalogs
Context for every object
Sensitivity, ownership, history, activity and inferred meaning, exposed through APIs and MCP
What it controls
Each agent sees what its task needs
Access to data and metadata set by identity, role and approved intent, with an audit record of every request
Why agents need more than data

Context turns data into something an agent can use correctly

Data is ambiguous on its own and incomplete without the meaning, purpose, provenance and age around it. That raises a practical question for any organization putting agents to work: where does the authoritative source of context live, covering everything that may be used to train or instruct them? BigID answers it from the data estate itself. It indexes the structured data and the unstructured files and email that feed language models, and keeps the context alongside each object. It builds on découverte et classification des données et gouvernance de l'accès.

Pour les équipes IA et plateforme

Better answers from the same agent. Metadata delivered with the data gives an agent the meaning, ownership and freshness it needs to reason correctly.

For security and data leaders

Each agent scoped to its task. Identity, role and approved intent decide which data and metadata an agent receives.

For governance and audit

A record of what each agent saw. Every metadata request is logged against the agent that made it, with the policy that decided it.

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Ask Your Data Security Platform Anything

Questions about the data estate asked in plain language from Claude over MCP, and answered by BigID with the same permissions and audit trail as the console.

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See an agent registered with a sponsor and a purpose, then watch two agents ask the same question and receive different answers.

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From data estate to governed context

BigID covers every job that context engineering depends on: indexing the data, cataloging the metadata, resolving each agent's identity and intent, and enforcing access to both data and metadata. Each one feeds the next, so the context an agent receives is complete for its task and limited to it.

Index the data Catalog the context Resolve the agent Control access Tables and warehouses Files and documents Email and chat Code Vector stores and AI data Structured and unstructured, at petabyte scale Sensibilité Crown jewel location Business meaning History Accès et activité Possession Alternative data Read through APIs and MCP Agent registry who the agent is, what it is for Identity and role Sponsor and purpose Intent from skills Authorizations Found across agent platforms Training pipeline Receives the data cleared for model training Support copilot Receives the case data its purpose needs Same request, task-sized answers Every request is checked and recorded Agents reach the catalog through APIs and BigID's RBAC-controlled MCP server, under the identity, role and intent they run with Identité de l'agent Rôle Approved purpose Metadata policy Audit record
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Where context engineering starts

Where does an authoritative source of context for our agents come from? From the data estate itself. BigID indexes and inventories structured and unstructured data, then catalogs the metadata for each object in one place that agents reach through APIs and MCP. Index and inventory → Does it cover files and email, or only databases? Both. Files, email, code and tables across hundreds of data stores and formats, which includes the unstructured content that language models are built on. Index and inventory → What metadata does BigID capture for each object? Sensitivity, the location of crown jewel data, business meaning, creation, modification and last access history, access and activity, ownership, and alternative copies, plus meaning and ownership inferred by AI. Catalog the metadata → How does an agent read this context? Through secure APIs and BigID's MCP server, where each request is checked against the agent's identity, role and intent before any metadata comes back. Catalog the metadata → Which agents exist, and who is accountable for them? BigID finds agents built on diverse platforms, maps each one to its identity, roles and permissions, and records the sponsor and the purposes it is approved for. Agent identity → How is an agent's intent decided? From its configuration and supporting skills. BigID assesses what the agent is meant to do and determines the authorizations that fit. Agent identity → Can two agents ask the same question and get different answers? Yes, by design. A training pipeline and a support copilot receive different results from the same request, because their identity, role and intent differ. Access to data and metadata → What can we show an auditor about agent access to metadata? The policy behind each decision, and an audit record of every agent's metadata access history. Access to data and metadata →
Capacités

From the first scan to the audit record, on one platform

Context engineering needs the data indexed, the metadata cataloged, each agent identified, and access controlled. BigID runs all of it on one platform, so the context an agent receives is the context its identity and intent allow.

Index and inventory structured and unstructured data

Context starts with knowing what data exists. BigID builds one inventory across the tables and the unstructured files and email that language models learn from, and captures sensitive content and business context as the data is found.

See discovery and classification →
  • Hundreds of data stores and data formats, from code to files to email
  • Structured and unstructured data in one catalog, including the feedstock for LLMs
  • Rapid assessments from metadata-only and sampled scans
  • Deep and full scans at petabyte scale, with no volume or frequency caps
  • Vector stores and AI data sources inventoried alongside everything else
  • Sensitive content and business context captured as data is found

Catalog the metadata that gives data its context

Data on its own is ambiguous. BigID captures the metadata around each object, keeps it aligned to that object, and holds it in a catalog built to be read by AI.

See data governance automation →
  • Sensitivity details and the content found in the data
  • Location and type of crown jewel information
  • Business context and meaning
  • History, including creation, modification and last access
  • Operational metadata such as access and activity
  • Technical, age and freshness metadata, harvested or captured and aligned to specific data objects
  • Ownership, and AI-inferred meaning and ownership where none is recorded
  • Alternative data: duplicate and derivative copies
  • Secure APIs and an MCP server that make the catalog readable by AI agents

Discover agent identity, role and intent

An agent can be given the right context only once it is known. BigID finds the agents, maps each one to an identity, role and permissions, and works out what it should be authorized to do.

See access governance →
  • Discover AI agents built on diverse platforms, including non-human and ephemeral identities
  • Map each agent's identity to its roles and permissions
  • Record the sponsor and the approved purposes for each agent
  • Assess intent from the agent's configuration and supporting skills
  • Determine which authorizations each agent should hold
  • Give an unregistered agent nothing until it is registered

Control access to data and metadata

Each agent should see the metadata relevant to its task and the specific data shared with it. BigID sets those limits by identity, role and intent, and keeps the record.

See secure architecture →
  • Access policies for data and metadata set by agent identity, role and intent
  • Metadata restrictions enforced through data access governance and RBAC on the MCP server
  • Different agents receive different answers to the same request, so each context fits its task
  • Every decision backed by a readable policy covering sponsor, approved purposes, role scope and clearance
  • Les agents accèdent aux données par le biais des identités qui leur sont associées, jamais directement.
  • Contrôle d'accès basé sur les rôles (RBAC) précis pour chaque API et service MCP
  • An audit record of each agent's metadata access history
Couverture

What the catalog reaches, and who can read it

One platform spans the data an organization holds, the AI data sources built on it and the agents using both, with the catalog exposed to whichever agent platform a team standardizes on.

Données structurées

Databases, warehouses and formats such as Parquet and Avro

Dossiers

Documents and file stores, including binary and CAD formats

Email and chat

The conversations where business data is created and shared

Code

Repositories that hold credentials, IP and configuration

Applications SaaS

Business data held in the applications teams work in every day

AI data sources

Vector stores and AI platforms such as SageMaker, Azure OpenAI and Vertex AI

Agents and their identities

Agents found across platforms, with sponsor, role and approved purpose

Catalog metadata

Sensitivity, history, activity, ownership and inferred meaning for every object

Read through Secure APIs RBAC-controlled MCP server
Agents from Claude Copilote GPT Gémeaux Custom enterprise agents
Preuve

The metadata agents rely on, scored independently

Analyst and independent results

  • Leader du rapport Forrester Wave™ : Solutions de découverte et de classification des données sensibles, 2e trimestre 2026, with the highest possible score for enrichment for classification, integrations and secure-by-design commitments
  • Won the Intuit data classification challenge against twenty vendors, at 96.5% weighted precision and recall on a synthetic dataset of more than 40,000 records
  • “BigID is engineered for performance and petabyte scale,” in the words of The Forrester Wave™
Lire le rapport Forrester Wave →

Du champ

“ Nous devions automatiser les processus et la gestion des données entre les systèmes… BigID était la seule solution qui répondait à ce besoin… ”

Responsable de la protection des données, entreprise mondiale de télécommunications

“ BigID m’offre une meilleure visibilité sur les données sensibles, m’aide à prioriser les protections de sécurité et réduit la surface d’attaque… ”

RSSI, entreprise mondiale du secteur de la santé

FAQ

What AI and security leaders ask before agents get context

What does an agent receive from BigID?

The metadata and classification insight around the data it is cleared to use. It is delivered through APIs and MCP. Agents reach data through the identities associated with them, and BigID limits what comes back by identity, role and intent.

How does BigID decide what an agent's intent is?

From the agent's configuration and its supporting skills. BigID reads those to assess what the agent is meant to do, determines the authorizations that fit, and checks each request against them. An agent that has not been registered receives nothing until it is.

We already run a catalog. Where does this fit?

Alongside it. BigID pushes findings and context to the catalogs you run through Metadata Exchange, and adds what agents need on top: sensitivity, activity, inferred ownership, and an identity-aware way to expose all of it over MCP.

What is context engineering?

Context engineering is supplying an AI agent with the right information, in the right form, for its task. For agents that work with enterprise data, the most useful context is the metadata around it: meaning, source, sensitivity, ownership, and freshness. BigID catalogs that metadata, exposes it through APIs and MCP, and limits what each agent receives by identity, role, and approved intent. It draws on data governance automation.

What is MCP, and how does BigID use it?

The Model Context Protocol is a standard way for AI assistants and agents to call tools and read context. BigID's MCP server lets agents such as Claude, Copilot, GPT, and Gemini query the catalog, with role-based access control on every request and an audit record of each interaction. The same server powers AgentIQ.

How does BigID keep each agent to the data its task needs?

It decides access by identity, role, and approved intent. Agents reach data through the identities associated with them, BigID checks each request against a readable policy, and an unregistered agent receives nothing until it is registered. Gouvernance de l'accès aux données et Gouvernance de l'IA extend the same controls across employees and agents.

Ressources

Reading on MCP, agent context and governed access

Livre blanc / Commencez ici
Context Engineering for AI

What context engineering is, why the metadata around data is the most useful context for agents, and how to deliver it under identity, role and approved-intent controls.

Lire le livre blanc →
Résumé de la solution Le serveur BigID MCP pour l'IA Comment les agents obtiennent un contexte de données gouverné au lieu de données brutes, grâce à une authentification par jeton, au RBAC, à des politiques ciblées et à un enregistrement d'audit de chaque interaction. Lire le résumé → Solution Automatisation de la gouvernance des données AI classification against your glossary, one catalog for billions of objects, and the catalogs you already run. Explore the solution → Solution AgentIQ The agentic automation layer that runs on this context: ask, investigate, decide and act from a prompt. Explore the solution →

Bring one agent and the data it uses

We will index the data on your own estate, show the metadata catalog that results, register the agent, and show what it receives compared with what a second agent receives for the same request.

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