Integrating AI Agents with Enterprise Systems
I'll let you in on a secret: the AI part of AI agents is often the easy part.
The hard part? Connecting that AI to your CRM, ERP, billing system, and that legacy application running on a server nobody wants to touch.
Integration is where AI agent projects succeed or fail. Here's how to approach it.
The Integration Challenge
Enterprise AI agents need to:
- Read from multiple systems (customer data, order history, product info)
- Write to multiple systems (update records, create tickets, send notifications)
- Coordinate across systems (ensure consistency, handle transactions)
- Respect existing security and access controls
All while maintaining the speed users expect from AI interactions.
A typical agent might need to:
- Look up customer in CRM
- Check order status in ERP
- Review support history in ticketing system
- Update customer record
- Create follow-up task
- Send confirmation email
That's 5+ systems, each with its own API (if you're lucky), authentication, and quirks.
Integration Architecture
The Tool Gateway Pattern
Don't let your AI agent call external systems directly. Route everything through a tool gateway:
AI Agent → Tool Gateway → External Systems
│
├── Authentication
├── Rate Limiting
├── Logging
├── Error Handling
└── Retry Logic
Benefits:
- Single point of control and monitoring
- Consistent error handling
- Easier security management
- Simpler agent development
API-First (When Available)
Modern systems usually have APIs. Use them:
REST APIs: Most common. Well understood. Good tooling.
GraphQL: Flexible queries. Good for complex data needs. Less common in enterprise.
Webhooks: For real-time updates from external systems.
When APIs exist, integration is (relatively) straightforward.
Legacy Integration Patterns
But many enterprise systems don't have good APIs:
Database-level integration: Read/write directly to the database.
- Pros: Bypasses application logic, often faster
- Cons: Fragile, can break when app changes, misses business rules
File-based integration: Import/export files (CSV, XML, etc.)
- Pros: Works with almost anything
- Cons: Slow, not real-time, error-prone
Screen scraping/RPA: Automate the UI
- Pros: No system access needed
- Cons: Extremely fragile, slow, maintenance nightmare
Custom adapters: Build middleware to expose legacy data as APIs
- Pros: Clean interface for agent, encapsulates legacy complexity
- Cons: Development investment required
For most AI agent projects, custom adapters are worth the investment. They pay off in maintainability.
iPaaS and Integration Platforms
Integration platforms can accelerate connections:
MuleSoft, Boomi, Azure Integration Services: Enterprise-grade, pre-built connectors, good governance.
Zapier, Make: Good for simpler integrations, less enterprise-ready.
Custom built: Full control, fits exactly, more development effort.
For agents touching many systems, a platform approach often makes sense.
Common Integration Scenarios
CRM Integration (Salesforce, HubSpot, Dynamics)
What agents need:
- Read: Contact info, account details, interaction history
- Write: Update records, log activities, create tasks
Typical approach:
- OAuth authentication
- REST API for CRUD operations
- Webhooks for real-time updates
Watch out for:
- API rate limits (Salesforce is notorious)
- Field-level security (agent sees what user sees)
- Complex object relationships
ERP Integration (SAP, Oracle, NetSuite)
What agents need:
- Read: Order status, inventory, pricing, customer credit
- Write: Create orders, update shipments, process returns
Typical approach:
- Often needs middleware (SAP has several options)
- May require custom development
- Consider read-replicas for performance
Watch out for:
- Complexity (ERP integrations are never simple)
- Transaction integrity
- Master data management
Ticketing/Support (Zendesk, ServiceNow, Jira)
What agents need:
- Read: Ticket history, status, SLAs
- Write: Create tickets, update status, add notes
Typical approach:
- REST APIs usually well-documented
- Webhooks for ticket updates
Watch out for:
- Workflow rules that might conflict with agent actions
- Notification cascades
Communication (Email, SMS, Chat)
What agents need:
- Send: Emails, SMS, chat messages
- Receive: Inbound messages for processing
Typical approach:
- SendGrid, Mailgun, Twilio for sending
- Webhooks or polling for receiving
Watch out for:
- Deliverability (spam filters, sender reputation)
- Compliance (opt-in/opt-out, content requirements)
- Rate limits
Security Considerations
Authentication
Never put credentials in the agent or LLM prompts.
Pattern:
Agent → Tool Gateway → Credential Store → External System
↓
(Azure Key Vault,
AWS Secrets Manager,
HashiCorp Vault)
Use managed identities where possible.
Authorization
The agent shouldn't have more access than the user it's serving.
Implement:
- Per-user or per-conversation context
- Action-level permissions
- Data-level filtering
Don't give the agent admin access to everything.
Data Handling
External system data flowing through agents needs protection:
- Encrypt in transit (TLS everywhere)
- Minimise data in prompts (only what's needed)
- Don't log sensitive data
- Clean up temporary data
Audit Trail
For compliance and debugging:
- Log all external system calls
- Include: timestamp, user context, action, parameters, result
- Retain appropriately
- Make searchable
Performance Optimization
Parallel Execution
Don't make sequential calls when parallel is possible:
# Bad: Sequential
customer = await crm.get_customer(customer_id)
orders = await erp.get_orders(customer_id)
tickets = await support.get_tickets(customer_id)
# Good: Parallel
customer, orders, tickets = await asyncio.gather(
crm.get_customer(customer_id),
erp.get_orders(customer_id),
support.get_tickets(customer_id)
)
Caching
Cache aggressively (where appropriate):
- Reference data (products, policies)
- Slowly-changing data (customer details)
- Computed values
Invalidate carefully:
- Time-based for reference data
- Event-based for transactional data
Batching
When possible, batch operations:
# Instead of 10 individual calls
for order_id in order_ids:
order = await erp.get_order(order_id)
# Batch retrieval
orders = await erp.get_orders(order_ids)
Testing Integration
Integration Testing
Test each external system connection:
- Happy path operations
- Error conditions (not found, unauthorized, timeout)
- Edge cases (special characters, large data)
Contract Testing
Ensure APIs behave as expected:
- Use tools like Pact for contract verification
- Catch breaking changes early
End-to-End Testing
Test full agent flows with real (or realistic) external systems:
- Development/sandbox environments
- Synthetic test data
- Monitoring of external system impact
Chaos Testing
What happens when systems fail?
- Simulate external system outages
- Test timeout handling
- Verify graceful degradation
When Integration Gets Hard
Some integrations are genuinely difficult:
Mainframe systems: May require terminal emulation or specialised middleware.
Acquired company systems: Different tech stack, unknown documentation.
Vendor systems with no API: Screen scraping or manual workarounds.
Options:
- Custom adapter development (invest upfront, cleaner long-term)
- RPA bridge (faster to deploy, fragile)
- Process change (maybe the agent doesn't need that data)
- Phased approach (start without, add later)
Be realistic about integration effort in project planning.
Our Integration Experience
We've integrated AI agents with CRMs, ERPs, and field service systems. We've built document processing that feeds into existing workflows.
Integration is often 50%+ of our AI agent project effort. We plan for it explicitly.
If you're building AI agents and facing integration challenges, we're happy to discuss approaches.