LangGraph × DAXA: Secure, Scalable, and Production-Ready AI for Banking
Where Intelligence Meets Compliance
The banking world is changing fast. AI agents are now capable of handling everything from processing loan applications to assessing risk and assisting customers. With this new frontier, one challenge persists:
How can banks deploy intelligent, automated workflows without compromising security or violating regulations?
That’s where LangGraph’s agent orchestration meets Pebblo MCP by DAXA - creating a secure, compliant foundation for real-world, production-grade AI.
The Challenge: Regulation vs. Innovation
To deliver value, AI agents need access to sensitive financial data, but that access is heavily regulated. Financial institutions must be able to answer:
- Which agent can access what company data?
- How do we ensure confidential files remain off-limits?
- Can every AI interaction be tracked and audited?
Embedding these controls into each agent is complex, unsustainable, and risky. Because agents are autonomous, they can be compromised, misconfigured, or manipulated.
Relying on agents to self-enforce compliance introduces vulnerabilities.
The solution lies in a separate, policy-driven control layer, one that governs access independently of agent logic.
The Shift: Standardized Interfaces for Agentic Access
To support this shift, the Model Context Protocol (MCP) is emerging as a key enabler. Introduced by Anthropic, MCP defines a standardized interface for AI agents to discover and access external tools and enterprise data.
This open protocol allows agents to operate without embedding access logic directly into their code. That’s the shift: agents orchestrate workflows, while external systems enable dynamic, policy-driven access.
The Integration: Agentic Power Meets Real-Time Governance
Through this integration:
- LangGraph enables powerful, multi-agent orchestration.
- Pebblo MCP acts as a secure, real-time gateway that enforces identity-based and semantic access policies.
Agents now operate in a governed, compliant, and auditable environment, without compromising their flexibility or design.
How It Works: A Streamlined, Secure Workflow
Let’s simulate a bank’s loan processing system using two LangGraph agents:
- Document Processor Agent – queries customer application data
- Credit Checker Agent – evaluates loan risk using external APIs
The Document Processor agent accesses files via Pebblo MCP, which connects securely to enterprise content (e.g., SharePoint). Data is indexed via a RAG engine for context-aware retrieval.
Example Scenarios:
- Jennifer Walsh – Application processed and approved with a 36% debt-to-income ratio
- Sarah Johnson – File marked "Confidential" and automatically blocked by Pebblo policies, triggering a manual review
Result: Automation when safe. Governance when required.
Two-Layer Security: Designed for Compliance
Pebblo MCP applies a dual-layered security model, purpose-built for regulated environments:
1. Identity-Based Access Control
- Agents inherit permissions from the human user (e.g., a loan officer)
- Pebblo dynamically validates identity and roles
- Agents operate under existing enterprise entitlements
2. Semantic Policy Enforcement
- Company-level policies control AI access to specific content
- For example, agents are blocked from accessing documents marked “Confidential” or “Secret”
- Even with misconfigured access, compliance is preserved
This defense-in-depth approach ensures agents only access what they’re allowed to, and nothing more.
Enterprise-Grade Architecture
The end-to-end stack includes:
- Workflow Trigger Layer – Initiates the request (e.g., by a loan officer)
- LangGraph Agents – Drive multi-step processing and decisions
- Pebblo MCP – Enforces real-time access policies
- RAG Engine – Powers intelligent, semantic retrieval
- SharePoint and Data Sources – Hold structured and unstructured content
Why This Works for Banking
For Developers
- Build intelligent agent workflows without hardcoding security
- Focus on business logic, not access logic
For IT & Security Teams
- Enforce compliance and security at the data layer, not the agent layer
- Monitor, control, and audit AI usage without friction
For Financial Institutions
- Confidently deploy AI for real business impact
- Stay compliant and audit-ready, while scaling intelligent automation
See It in Action
Watch the demo to see secure, compliant AI in a real-world banking environment:
- Agents autonomously process sensitive financial data
- Pebblo MCP enforces policy at every interaction
- Loan decisions are made intelligently, safely, and in seconds
Built for Banks. Ready for Production.
This is not a prototype. It’s production-grade AI, built for real use cases in real environments.
Governed by policy. Designed for safety. Powered by LangGraph orchestration and Pebblo's secure MCP server.
Let’s redefine what’s possible with AI in banking, securely and responsibly.
