AI integration and automation
The path from a model to a business process.
An integration gives a model or an agent a defined way into the systems you already run, and a defined way back out. Dynacore designs that path with the same care as the infrastructure it sits on.
Where programs stall
Many AI programs can demonstrate a model. Fewer can say which system of record it may change, under whose identity, and what the log contains the next morning. That gap is integration work. It is also the control.
We sit with the application owners, the integration team and the security function. The artefact is a design the operators can run: interfaces, data contracts, identity, failure behaviour and a rollback.
Patterns we use
Request and reply
A system calls a model or an agent for a bounded answer, with a timeout and a human path if the call fails.
Events
A business event starts a workflow. The model is one step, with a schema for what it may return.
Retrieval
Answers are grounded in sources you have approved, with a citation back to the source.
Human approval
Actions that change money, access, or a person’s record stop for a person when the impact calls for it.
Identity
The agent or the integration runs as an identity the security team can see, rotate and revoke.
Automation, with the model in a box
Some steps in a workflow should stay deterministic: calculations, entitlements, approvals that already have a rule. A model belongs where the input is messy and the cost of a checked mistake is acceptable. We write that boundary down.
Platform engineering sits beside this: infrastructure as code for the environments the integration runs in, and an operating routine for secrets, versions and promotion between environments. The AI factory practice covers the accelerated compute when the workload needs it.
Controls that travel with the design
- Least privilege for every integration identity
- An allow-list of tools, APIs and data sets
- An audit trail of calls, inputs that are safe to store, and outcomes
- Timeouts, idempotency and a rollback for actions that change state
- A privacy review where personal information crosses the boundary
Governance for agents is set out on the Agentic AI page. Workplace identity, which these integrations depend on, is covered under Cloud and workplace.
Answers
Direct answers
Short statements for the questions this page is here to settle.
What is AI integration?
AI integration is a defined way for a model or an agent to reach the identity, data and workflows an organisation already runs, and a defined way back out, with an allow-list and a record of what ran.
Which actions should stay with a person?
Actions that change money, access, or a person’s record stop for a person when the impact calls for it. The integration is designed with that approval step, a timeout, and a path if the call fails.
Brief us on the workflow
Describe the process, the systems it touches, and what a wrong action would cost.