AI agents can retrieve information, summarize records, and recommend next steps. Supporting operational work requires an additional capability: a reliable way to carry out an approved business action through the systems that already run the organization.
An agent may understand that a customer wants to update an account, submit a claim, change a reservation, or initiate a payment. Before it can complete that request, it needs to know which business function to call, what information the function requires, which permissions apply, whether human approval is needed, and how the system will report the outcome.
This challenge is becoming more important as organizations expand agents into multi-step workflows. Anthropic’s 2026 State of AI Agents, based on a survey of more than 500 technical leaders, found that 57% of organizations were using agents for multi-stage workflows, while 46% identified integration with existing systems as a primary obstacle.
The path from an AI request to a completed business transaction therefore depends on the integration architecture between the agent and the systems responsible for execution.
What Separates an AI Answer From an Approved Business Action?
Data retrieval returns information. Transaction execution changes something in the business.
A retrieval request may check an order status, return an account balance, display policy information, or confirm whether inventory is available. A business action may update an address, release a payment, create an account, submit a claim, change a reservation, or alter the status of an order.
Executing an action carries additional operational requirements. The system must confirm that the request is valid, the user or agent is authorized, all required information is present, and the action is allowed under current business rules. It must also return a clear response. The agent needs to know whether the transaction succeeded, failed validation, requires approval, reached only part of the workflow, or should not be attempted again.
Access to enterprise data does not automatically provide a safe path for completing these actions.
Why Does Core Business Logic Still Matter?
Many critical business rules remain inside mainframe and other core applications because those systems continue to process the transactions that run the organization.
A payment system may enforce account rules, transaction limits, and authorization requirements. A reservation platform may manage inventory and ticket status. An insurance application may apply eligibility, policy, and claims logic.
Recreating those rules inside every AI application can lead to inconsistent behavior and duplicated logic. It can also make future changes harder to control because the same business rule may need to be updated in several places.
A more reliable approach keeps the authoritative transaction and business logic in the system designed to manage it. The agent receives a defined way to invoke that function and interpret the response.
What Must an Agent-Callable Business Function Define?
Before a core function is made available to an AI agent, teams need to define the complete service contract around it.
That contract should include:
- Function: The specific action the service is authorized to perform
- Inputs: Required fields, formats, identifiers, and acceptable values
- Identity: The application, agent, or user initiating the request
- Authorization: The identities and roles permitted to invoke the function
- Approval: The conditions that require human or business review
- Outputs: The structured information returned after execution
- Errors: How validation failures, unavailable systems, and exceptions are reported
- Retry behavior: How duplicate transactions are prevented when a request is repeated
- Traceability: What is recorded for operational review, troubleshooting, and audit
A defined contract gives the agent a controlled business function to call instead of broad access to the application behind it. It also gives architecture, security, operations, and business teams a shared understanding of what the agent is allowed to do.
Why Are Direct Agent-to-Core Connections Difficult?
Core applications were designed to support controlled transactions, not open-ended requests from AI applications.
They may use specialized interfaces, transaction formats, protocols, or data structures. A single business process may also require several systems to participate in a specific sequence. An agent may understand that a customer wants to update an account, but the core environment may require an exact customer identifier, validated field values, a specific transaction path, and a defined response code.
Credentials create another challenge. Giving an agent broad system access can expose more data and functionality than the use case requires. The agent should only be able to invoke the approved service, under the approved identity, with the permissions required for that action.
Operational behavior also needs to be predictable. A downstream system may be unavailable. A request may fail halfway through a workflow. A retry may risk repeating a payment, reservation, or account update. Some actions may need to pause until a person approves them.
These conditions require a defined integration process rather than leaving the agent to interpret and navigate the core environment independently.
How Should Enterprises Manage AI Agent Permissions and Approval Boundaries?
Each agent should have a defined identity and only the permissions required for its approved use case.
A customer-service agent, for example, may be able to retrieve an order and initiate a limited servicing request without gaining access to every transaction in the underlying system. Approval requirements should also reflect the risk of the action, with higher-risk requests routed for human review when needed.
The need for these controls is already visible in enterprise adoption. A 2026 Cloud Security Alliance survey found that 53% of organizations had experienced agents exceeding their intended permissions, while 47% reported a security incident involving an AI agent.
A governed operating model should clearly define identity, authorized functions and data, transaction limits, approval requirements, and how actions are logged and reviewed.
The agent can support the workflow, but the organization remains responsible for the controls around it.
Where Does a Governed Integration Layer Fit?
A governed integration layer provides a controlled connection between the AI application and the core systems responsible for business execution.
Instead of giving an agent direct access to multiple back-end applications, approved functions can be exposed through defined services. The integration layer can authenticate the request, validate and transform inputs, invoke the appropriate transaction, coordinate additional systems when needed, and return a structured response. It can also define how errors, exceptions, and approval requirements are handled before or during execution.
This shields the AI application from back-end complexity while protecting core systems from broad or inconsistent access. The same reusable services can also support employee tools, customer applications, partner integrations, and automation without rebuilding access to the underlying business function each time.
How Does Adaptive Integration Fabric Support Agent-Ready Operations?
Adaptive Integration Fabric provides the integration foundation for turning trusted core capabilities into governed, reusable services.
Fabric can expose approved mainframe programs, transactions, and data through REST or SOAP APIs while preserving the production systems and business logic already in place. These services can support modern application, partner, workflow, and automation initiatives without recreating the underlying integration for every consumer.
For multi-step processes, Fabric can coordinate applications, data, transformations, rules, and external services through one orchestration layer. It also allows teams to manage reusable integrations through a governed platform rather than connecting each AI application directly to the core environment.
This gives organizations a consistent way to make established business functions available to AI applications without requiring the agent to understand every program, transaction, data structure, or system dependency.
The organization still defines identity, authorization, human approval, and AI governance policies. Adaptive Integration Fabric provides the controlled connection to the applications where approved business actions are executed.
Building AI That Can Act Reliably
Enterprise AI agents become more useful when they can move from understanding a request to invoking a trusted business function under defined conditions.
That requires more than access to enterprise data. Agents need callable services, validated inputs, limited permissions, structured responses, approval boundaries, and reliable handling for errors and exceptions. Adaptive Integration Fabric helps create that foundation by making established core transactions available through reusable APIs and coordinating the workflows around them.
With that integration layer in place, organizations can extend AI into operational use cases while keeping transaction execution, business logic, and control anchored in the systems designed to manage them.
Learn more about Adaptive Integration Fabric