Scale securely agentic systems with search intelligence

Introduction

One of the Middle East’s leading banks, managing hundreds of billions in assets, has been at the forefront of enterprise AI transformation.

The bank deployed AI agents across functions including M&A, marketing, sales, and analytics. While the first generation of agents was thoughtfully designed, the lack of controlled access to external intelligence limited the quality, relevance, and freshness of their outputs. As adoption expanded, the bank needed a unified, enterprise-grade search layer capable of powering agents with real-time information while meeting extremely strict security, compliance, and governance requirements.

The challenge

As the bank expanded its agentic footprint across teams and functions, three structural challenges emerged:

• Avoiding “airplane mode” agents: Early generations of agents lacked access to external intelligence, limiting the relevance, freshness, and usefulness of their outputs.
• Meeting stringent security standards: Any web connectivity layer had to comply with strict enterprise security requirements, including private environment deployment, IP whitelisting, SSO integration, and zero data retention policies.
• Ensuring production-grade reliability: The bank required enterprise-level SLAs and operational reliability to support mission-critical agent deployments across the organization

Why they chose Linkup

The bank needed a central intelligence layer capable of powering a diverse, organization-wide family of AI agents while meeting extremely high standards for security, reliability, and scalability. Linkup differentiated from other leading search providers through

• Enterprise-grade security architecture, giving the bank’s security and infrastructure teams strict control over how the search layer was accessed and governed:
  • Private environment: User queries and data traffic were routed through a dedicated private connection rather than Linkup’s public endpoints.
  • IP whitelisting: Only requests originating from explicitly authorized bank infrastructure could access the Linkup API.
  • Custom SSO integration: Authentication and access management were integrated directly into the bank’s internal identity systems.
  • Zero data retention: No queries or customer data were stored. Requests were processed in real time, and query-related logs were deleted immediately after the response was returned. This was a mandatory requirement for deployment.
• Real-time external intelligence for agents: By connecting agents to live, auditable web intelligence, Linkup enabled the bank to move beyond “airplane mode” agents and significantly improve the freshness, relevance, and usefulness of outputs across business functions.
• Unified intelligence layer across the organization: Rather than deploying separate search solutions for each team, Linkup provided a single, consistent intelligence layer capable of serving use cases across M&A, marketing, sales, and analytics — simplifying governance and reducing operational complexity.
• Production-grade reliability and auditability: Linkup delivered enterprise-level SLAs, scalable performance, and fully cited results, enabling both agents and human reviewers to verify sources, trace reasoning, and operate with confidence in highly regulated environments.

Implementation

Linkup sits as the retrieval layer underneath the bank's entire AI agent infrastructure. Every agent, regardless of function or team, routes its web search queries through a single Linkup integration. The flow:

Step1. An AI agent receives a task requiring external information, whether a regulatory filing, a market report, or a competitor profile

Step 2. The agent sends a search query to Linkup via its private link, authenticated through SSO

Step 3. Linkup returns clean, source-cited results filtered to approved domains

Step 4. The agent uses the sourced output to ground its reasoning, passing verifiable, cited context into its response

Use cases powered by Linkup:

• M&A research: agents surface company profiles, financial filings, regulatory disclosures, and deal precedents from approved financial and legal sources, scoped tightly to avoid low-authority results.
• Marketing intelligence: agents track industry news, competitor activity, and market developments across approved media and industry domains.
• General knowledge: agents enrich prospect profiles with current company information, news, and relevant context drawn from approved business sources.
• Financial analysis: analysts use AI agents to research macro trends, sector developments, and company-specific data, with every traceable reference.

Results

With Linkup as the unified search layer, the bank's AI agent rollout shifted from fragmented and inconsistently trusted to a single, reliable infrastructure serving the entire organisation:

• Analyst adoption of agents grew by 35%: teams use their AI agents more frequently because outputs are sourced and verifiable
• Cost reduction: Centralizing search infrastructure and high accuracy reduced manual workarounds
• Improved reliability: A single, consistently performing search layer replaced the inconsistency of fragmented tooling, improving the quality and dependability of agent outputs across business-critical workflows
• New use cases unlocked due to access to the web (e.g., reputation monitoring, geopolitics impact on market forecast)

Conclusion

Deploying AI agents across a regulated financial institution requires every layer of the stack to meet enterprise security and compliance standards. By integrating Linkup as a centralized intelligence layer rather than a per-team tool, the bank eliminated the fragmentation that was limiting agent adoption and gave every team, from M&A to financial analysis, a consistent, verifiable source of web intelligence.

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