Enterprise AI agents: choose the workflow first
An AI workflow combines judgment-like processing with actions in real systems. The design must distinguish what the model may suggest from what the workflow is authorised to execute.
FI Digital builds automation around defined business states, approved tools and recoverable actions. Deterministic rules are used where they are appropriate, with AI reserved for tasks that benefit from interpreting unstructured information.
Choose the relevant service
Finance and billing agents
Scope the financial information and approvals before an agent takes action.
Sales and outreach agents
Define qualification, permitted outreach and the handoff to sales.
Operations and dispatch agents
Connect the operational request to an accountable decision and exception route.
Legal workflow agents
Keep legal judgment and consequential decisions with authorised professionals.
AI agent development
Review the underlying engineering and evaluation approach.
LLM integration and RAG
Connect approved knowledge and supported actions with explicit access boundaries.
FI Digital UK / Workflow
Enterprise AI agents
Illustrative workflow, not a product screenshot or a claim about a client’s production architecture.
Step 5 of 5Resolve with the owner (Needs correction)
Keep scrolling to follow the flow
- Process boundaryDefine the trigger, desired outcome and owner of each decision.
- Tool permissionsLimit available actions and validate inputs before execution.
- Review and authoriseDecision point
- Operations and evaluationTest retries, duplicate events, model errors and changes. Log the business outcome as well as the technical run.
- Resolve with the ownerOutcome: Needs correction
What the engagement covers
Process boundary
Define the trigger, desired outcome and owner of each decision.
Tool permissions
Limit available actions and validate inputs before execution.
Human and exception paths
Route uncertain or high-consequence decisions to a person with enough context to act.
Operations and evaluation
Test retries, duplicate events, model errors and changes. Log the business outcome as well as the technical run.
A practical example
An illustrative workflow reads a meeting note and proposes follow-up tasks. The task owner should be able to confirm the assignment and due date before the automation creates a commitment based on an ambiguous statement.
Relevant delivery experience
Australian group delivery example: an anonymous injury-law firm used customer-service agents built around Zoho Desk, OpenAI API, Zia agents and custom scripts. The client confirmed 70% autonomous resolution across approximately 800 tickets over six months. The build took six weeks. Legal-advice requests stayed with a human agent. The result is specific to that workflow and review period.
Questions that shape the scope
Use these questions to identify the first decision and the information needed to make it.
- Where is AI actually needed?
- Which actions are allowed?
- How is a duplicate prevented?
- Who owns an unresolved run?
Make the UK operating requirements explicit
Separate the UK organisation’s requirements from any wider group template. Identify the legal entity, business owner, connected accounts and the people authorised to accept the result. If the workflow relies on financial or employment rules, your qualified advisers should confirm the applicable settings and obligations; software configuration alone does not establish compliance.
From assessment to an accepted result
Define a useful first release
Agree the user, workflow, expected outcome and acceptance owner. Identify the important unknowns early.
Design the system boundaries
Set out records, interfaces, permissions and dependencies. Make failure recovery part of the design.
Build and review working increments
Review usable work against representative scenarios. Keep decisions and scope changes visible.
Release with an operating plan
Confirm acceptance, rollback, account ownership, documentation and the support arrangement.
Scope and responsibilities
The scope, acceptance criteria, third-party dependencies and operating responsibilities are agreed for the engagement. No partner tier, certification, guaranteed saving or fixed delivery period is implied.
Frequently asked questions
001When should we use rules instead of AI?
Use deterministic rules when the inputs and decisions can be specified clearly and reliably. AI can help interpret unstructured material, but it introduces evaluation and uncertainty management. A good design combines the approaches deliberately rather than inserting a model into every step or treating flexibility as a reason to remove controls.
002Can MCP make the whole integration automatic?
MCP can provide a standard way to expose tools and context, but it does not decide permissions, business rules or safe recovery. The workflow still needs authenticated access, validated actions, testing and operational ownership. Evaluate the total system and the cost of maintaining it, not only how quickly a connector can be configured.
003Can the project start with a smaller first release?
Yes, if the first release covers a useful end-to-end workflow rather than disconnected screens. Agree the user, outcome, acceptance checks and operational owner. Keep later features visible without placing them all in the initial scope. The first release should test the important assumptions and leave a maintainable foundation for the next decision.
004How are cost and delivery time estimated?
The estimate follows the agreed workflow, interfaces, data, permissions and operating requirements. Discovery should expose uncertain dependencies before they become hidden commitments. Break the work into reviewable milestones and state assumptions, exclusions and third-party costs. A fixed number of weeks or a low starting price is not a reliable substitute for a defined delivery scope.
005Can you integrate with our existing systems?
Integration depends on the systems’ available interfaces, access rights, limits and commercial terms. Review the actual records and actions required before choosing a connector or custom interface. Define identifiers, retry behaviour, duplicate prevention and reconciliation. The proposal should distinguish supported actions from capabilities that still require discovery or permission from another system owner.
006Who owns the software and accounts?
Ownership, licensing and access should be explicit in the agreement. Distinguish custom code from existing product components, third-party services and open-source dependencies. Agree where repositories, cloud accounts, credentials and documentation will be controlled, and what is handed over. Do not assume that commissioning a project automatically transfers every underlying software right or provider account.
007How do you test before launch?
Test the business workflow, user permissions, ordinary failures and recovery, using representative scenarios agreed with the people who will operate the result. Include data quality and integration behaviour where relevant. Record acceptance and unresolved issues before release, with a rollback or fallback appropriate to the change. A demonstration of the happy path is only one part of readiness.
008Is ongoing maintenance included?
The proposal should distinguish delivery, warranty or defect correction, and ongoing operational support. Dependencies, usage and business requirements can change after launch, so assign ownership for updates and monitoring. Support hours, response targets and service levels require a specific agreement. They should not be assumed from a general description of development or platform operations services.
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